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• Jan 2022 Journal Article ACM Transactions on Information Systems

Topic Difficulty: Collection and Query Formulation Effects

J Shane Culpepper, Guglielmo Faggioli, Nicola Ferro, Oren Kurland
Abstract

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has

• 23 Dec 2021 Journal Article The New England Journal of Medicine

Protection against Covid-19 by BNT162b2 booster across age groups

Yinon M Bar-On, Yair Goldberg, Micha Mandel, Omri Bodenheimer, Laurence S Freedman, Sharon Alroy-Preis, Nachman Ash, Amit Huppert, Ron Milo
Abstract

BACKGROUND After promising initial results from the administration of a third (booster) dose of the BNT162b2 messenger RNA vaccine (Pfizer–BioNTech) to persons 60 years of age or older, the booster campaign in Israel was gradually expanded to persons in younger age groups who had received a second dose at least 5 months earlier. METHODS We extracted data for the period

• 17 Dec 2021 Conference Paper WINE 2021 - The 17th Conference on Web and Internet Economics

Optimal DSIC Auctions for Correlated Private Values: Ex-Post vs. Ex-Interim IR

Ido Feldman, Ron Lavi
Abstract

We study Dominant-Strategy Incentive-Compatible (DSIC) revenue-maximizing auctions ("optimal" auctions) for a single-item and correlated private values. We give tight bounds on the ratio of the revenue of the optimal Ex-Post Individually Rational (EPIR) auction and the revenue of the optimal Ex-Interim Individually Rational (EIIR) auction. This bound is expressed as a

• 15 Dec 2021 Preprint bioRxiv

Simulated visual hallucinations in virtual reality enhance cognitive flexibility

Clara Rastelli, Antonino Greco, Yoed N Kenett, Chiara Finocchiaro, Nicola De Pisapia
Abstract

Historically, psychedelic drugs are known to modulate cognitive flexibility, a central aspect of cognition permitting adaptation to changing environmental demands. Despite proof suggesting phenomenological similarities between artificially-induced and actual psychedelic altered perception, experimental evidence is still lacking about whether the former is also able to

• 14 Dec 2021 Journal Article Operations Research

Naive Learning Through Probability Overmatching

Itai Arieli, Yakov Babichenko, Manuel Mueller-Frank
Abstract

We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions, random decision rules are sufficient to achieve agreement in finite time in

• 9 Dec 2021 Journal Article The New England Journal of Medicine

Waning Immunity after the BNT162b2 Vaccine in Israel

Yair Goldberg, Micha Mandel, Yinon M Bar-On, Omri Bodenheimer, Laurence S Freedman, Eric J Haas, Ron Milo, Sharon Alroy-Preis, Nachman Ash, Amit Huppert
Abstract

Background In December 2020, Israel began a mass vaccination campaign against coronavirus disease 2019 (Covid-19) by administering the BNT162b2 vaccine, which led to a sharp curtailing of ...

• 9 Dec 2021 Preprint arXiv

Latent Space Explanation by Intervention

Abstract

The success of deep neural nets heavily relies on their ability to encode complex relations between their input and their output. While this property serves to fit the training data well, it also obscures the mechanism that drives prediction. This study aims to reveal hidden concepts by employing an intervention mechanism that shifts the predicted class based on discrete

• 9 Dec 2021 Journal Article Npj Science of Learning

Education shapes the structure of semantic memory and impacts creative thinking

Solange Denervaud, Alexander P Christensen, Yoed N Kenett, Roger E Beaty
Abstract

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory—a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational

• 6 Dec 2021 Conference Paper 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Learning Generalized Gumbel-max Causal Mechanisms

Guy Lorberbom, Daniel Johnson, Chris J Maddison, Daniel Tarlow, Tamir Hazan
Abstract

To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into noise sources and deterministic functions mapping realizations of noise to samples. Unfortunately, the causal mechanism is not uniquely identified by data that can be gathered by observing and

• 6 Dec 2021 Conference Paper 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems

Abstract

Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in developing efficient methods for\textit {smooth} low-rank optimization problems that avoid maintaining high-rank matrices and computing expensive high-rank SVDs, advances for nonsmooth problems

• 6 Dec 2021 Preprint PsyArXiv

Dynamic changes in semantic memory structure support successful problem-solving

Theophile Bieth, Yoed N Kenett, Marcela Ovando Tellez, Alizée Lopez-Persem, Célia Lacaux, Delphine Oudiette, Emmanuelle Volle
Abstract

While problem-solving is central in our daily life, its underlying mechanisms remain largely unknown. Restructuration (i.e., reinterpretation and reorganization of problem-related representations) is theoretically considered as one such mechanism, yet empirical evidence supporting it is scarce. We investigated restructuration as a mechanism underlying problem-solving

• 5 Dec 2021 Preprint MedRxiv

Protection and waning of natural and hybrid COVID-19 immunity

Yair Goldberg, Micha Mandel, Yinon M Bar-On, Omri Bodenheimer, Laurence S Freedman, Nachman Ash, Sharon Alroy-Preis, Amit Huppert, Ron Milo
Abstract

BACKGROUND Infection with SARS-CoV-2 provides substantial natural immunity against reinfection. Recent studies have shown strong waning of the immunity provided by the BNT162b2 vaccine. The time course of natural and hybrid immunity is unknown. METHODS Data on confirmed SARS-CoV-2 infections were extracted from the Israeli Ministry of Health database for the period

• 1 Dec 2021 Journal Article Journal of Statistical Planning and Inference

Optimal rerandomization designs via a criterion that provides insurance against failed experiments

Adam Kapelner, Abba M Krieger, Michael Sklar, David Azriel
Abstract

We present an optimized rerandomization design procedure for a non-sequential treatment-control experiment. Randomized experiments are the gold standard for finding causal effects in nature. But sometimes random assignments result in unequal partitions of the treatment and control group visibly seen as imbalance in observed covariates. There can additionally be imbalance

• 1 Dec 2021 Journal Article Algorithmica

Approximation Schemes for the Generalized Extensible Bin Packing Problem

Abstract

We present a new generalization of the extensible bin packing with unequal bin sizes problem. In our generalization the cost of exceeding the bin size (also known as the bin capacity) depends on the index of the bin and not only on the amount in which the size of the bin is exceeded. This generalization does not satisfy the assumptions on the cost function that were

• 1 Dec 2021 Journal Article Artificial Intelligence

Local and global explanations of agent behavior: Integrating strategy summaries with saliency maps

Tobias Huber, Katharina Weitz, Elisabeth André, Ofra Amir
Abstract

With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as the environments in which they act have large state spaces, and their decision-making can be affected by delayed rewards, making it difficult to analyze their

• 30 Nov 2021 Journal Article Journal of Business Ethics

Who Do I (Dis)Trust and Monitor for Ethical Misconduct? Status, Power, and the Structural Paradox

Kelly Raz, Alison R Fragale, Liat Levontin
Abstract

A wealth of research documents the critical role of trust for social exchange and cooperative behavior. The ability to inspire trust in others can often be elusive, and distrust can have adverse interpersonal and ethical consequences. Drawing from the literature on social hierarchy and interpersonal judgments, the current research explores the predictive role of a

• 22 Nov 2021 Journal Article Advances in Mathematics

Fisher-KPP equation with small data and the extremal process of branching Brownian motion

Leonid Mytnik, Jean-Michel Roquejoffre, Lenya Ryzhik
Abstract

We consider the limiting extremal process $X$ of the particles of the binary branching Brownian motion. We show that after a shift by the logarithm of the derivative martingale Z, the rescaled “density” of particles, which are at distance $n+x$ from a position close to the tip of $X$, converges

• 18 Nov 2021 Preprint arXiv

Multi-Channel Bayesian Persuasion

Abstract

The celebrated Bayesian persuasion model considers strategic communication between an informed agent (the sender) and uniformed decision makers (the receivers). The current rapidly-growing literature assumes a dichotomy: either the sender is powerful enough to communicate separately with each receiver (a.k.a. private persuasion), or she cannot communicate separately at

• 18 Nov 2021 Preprint arXiv

A limit law for the most favorite point of simple random walk on a regular tree

Marek Biskup, Oren Louidor
Abstract

We consider a continuous-time random walk on a regular tree of finite depth and study its favorite points among the leaf vertices. For the walk started from a leaf vertex and stopped upon hitting the root we prove that, in the limit as as the depth of the tree tends to infinity, the suitably scaled and centered maximal time spent at any leaf converges to a randomly-shifted

• 15 Nov 2021 Journal Article Discrete Applied Mathematics

Efficiently enumerating minimal triangulations

Abstract

We present an algorithm that enumerates all the minimal triangulations of a graph in incremental polynomial time. Consequently, we get an algorithm for enumerating all the proper tree decompositions, in incremental polynomial time, where “proper” means that the tree decomposition cannot be improved by removing or splitting a bag. The algorithm can incorporate any method

• 15 Nov 2021 Preprint arXiv

Enumerating Minimal Separators in Ranked Order

Abstract

Let $G$ be an $n$-vertex graph, and $s,t$ vertices of $G$. We present an efficient algorithm which enumerates the set of minimal $st$-separators of $G$ in ascending order of cardinality, with a delay of $O(n^{3.5})$ per separator. In particular, we present an algorithm that lists, in ascending order of cardinality, all minimal separators with at most $k$ vertices. In

• 12 Nov 2021 Preprint arXiv

EPTAS for parallel identical machine scheduling with time restrictions

Abstract

We consider the non-preemptive scheduling problem on identical machines where there is a parameter B and each machine in every unit length time interval can process up to B different jobs. The goal function we consider is the makespan minimization and we develop an EPTAS for this problem. Prior to our work a PTAS was known only for the case of one machine and constant

• 12 Nov 2021 Journal Article Frontiers in Psychology

On the Effect of Practice on Exploration and Exploitation of Options and Strategies

Doron Cohen, Kinneret Teodorescu
Abstract

Insufficient exploration of one’s surroundings is at the root of many real-life problems, as demonstrated by many famous biases (e.g., the status quo bias, learned helplessness). The current work focuses on the emergence of this phenomenon at the strategy level: the tendency to under-explore the set of available choice strategies. We demonstrate that insufficient

• 11 Nov 2021 Preprint arXiv

Open surgery tool classification and hand utilization using a multi-camera system

Abstract

Purpose: The goal of this work is to use multi-camera video to classify open surgery tools as well as identify which tool is held in each hand. Multi-camera systems help prevent occlusions in open surgery video data. Furthermore, combining multiple views such as a Top-view camera covering the full operative field and a Close-up camera focusing on hand motion and anatomy

• 4 Nov 2021 Journal Article Journal of Scheduling

More on ordered open end bin packing

Janos Balogh, Leah Epstein, Asaf Levin
Abstract

We consider the Ordered Open End Bin Packing problem. Items of sizes in (0, 1] are presented one by one, to be assigned to bins in this order. An item can be assigned to any bin for which the current total size is strictly below 1. This means also that the bin can be overloaded by its last packed item. We improve lower and upper bounds on the asymptotic competitive

• 4 Nov 2021 Journal Article Theoretical Computer Science

Golden games

Abstract

We consider extensive form 2-player win-lose games, with alternating moves, of perfect and complete information. The games are played over a complete binary-tree of depth n, where 0/1 payoffs in the leaves are drawn according to an i.i.d. Bernoulli distribution with probability p. Whenever p differs from the golden ratio, asymptotically as $n\to \infty$

• 3 Nov 2021 Conference Paper 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Abstract

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. However, when measuring individual outcomes is costly, as is the case of a tumor biopsy

• 2 Nov 2021 Journal Article Discrete Applied Mathematics

Starting time minimization for the maximum job variant

Leah Epstein, Asaf Levin
Abstract

We consider a scheduling problem on identical machines, where the cost for each machine is the total size of jobs assigned to it, excluding its largest job. The objective is to minimize the cost of the schedule, which is the maximum cost over all machines. We study online algorithms with and without migration. We design an algorithm with competitive ratio 3 for the

• 2 Nov 2021 Journal Article Psychopharmacology

The effect of methylphenidate and mixed amphetamine salts on cognitive reflection: a field study

Abstract

Rationale

Methylphenidate (MPH) and mixed D,L-amphetamine salts (MASs; Adderall) were previously found to have unreliable effects on judgment and decision processes.

Objective

We predicted that MPH and MASs have a specific effect of reducing heuristic responses, which should lead to increased performance on the cognitive reflection test (CRT). The CRT is considered

• 1 Nov 2021 Journal Article Operations Research Letters

Parameterized complexity of configuration integer programs

Dušan Knop, Martin Koutecký, Asaf Levin, Matthias Mnich, Shmuel Onn
Abstract

Configuration integer programs (IP) have been key in the design of algorithms for NP-hard high-multiplicity problems. First, we develop fast exact (exponential-time) algorithms for Configuration IP and matching hardness results. Second, we showcase the implications of these results to bin-packing and facility-location-like problems.

• 1 Nov 2021 Journal Article Journal of Organizational Behavior

When do service employees smile? Response-dependent emotion regulation in emotional labor

Abstract

We advance the theoretical and practical understanding of affect in service interactions by conceptualizing employees and customers as concurrent participants in the same interaction. We analyzed employees' emotional labor requirements, which comprise both the well-recognized requirement to display positive affect (i.e., acting is response independent) and the

• Nov 2021 Journal Article Brazilian Journal of Probability and Statistics

Absolute continuity of the super-Brownian motion with infinite mean

Rustam Mamin, Leonid Mytnik
Abstract

In this work, we prove that for any dimension $d\ge 1$ and any $\gamma \in (0,1)$ super-Brownian motion corresponding to the log-Laplace equation

• Nov 2021 Conference Paper Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

Location Classification Based on Tweets

Elad Kravi, Yaron Kanza, Benny Kimelfeld, Roi Reichart
Abstract

Location classification is used for associating type to locations, to enrich maps and support a plethora of geospatial applications that rely on location types. Classification can be performed by humans, but using machine learning is more efficient and faster to react to changes than human-based classification. Machine learning can be used in lieu of human classification

• Nov 2021 Journal Article Context Sensitive Health Informatics: The Role of Informatics in Global Pandemics

Information Chaos: An Adapted Framework Describing Citizens’ Experiences with Information During COVID-19

Helen Monkman, Andre W Kushniruk, Avi Parush, Blake J Lesselroth
Abstract

With the onset of the coronavirus pandemic, clinicians, public officials, and citizens alike struggled to stay abreast of the constant and evolving stream of information about the clinical manifestations of illness, epidemiology of the disease, and the public health response. In this paper, we adapted (ie, added and modified elements) Beasley and colleagues’ information

• 29 Oct 2021 Journal Article Sustainability

Workspace Integration and Sustainability: Linking the Symbolic and Social Affordances of the Workspace to Employee Wellbeing

Iris Vilnai-Yavetz, Anat Rafaeli
Abstract

Our goal in this paper is to connect workspace design to employee wellbeing and social sustainability. Toward this connection, we introduce and empirically test a new concept of “workspace integration”. This concept refers to the continuum of integration of an employee’s workspace with the organizational, physical space. We further define three workspace affordances

• 28 Oct 2021 Journal Article Proceedings of the National Academy of Sciences of the United States of America

Frequency of enforcement is more important than the severity of punishment in reducing violation behaviors

Kinneret Teodorescu, Ori Plonsky, Shahar Ayal, Rachel Barkan
Abstract

External enforcement policies aimed to reduce violations differ on two key components: the probability of inspection and the severity of the punishment. Different lines of research offer different insights regarding the relative importance of each component. In four studies, students and Prolific crowdsourcing participants (Ntotal = 816) repeatedly faced temptations to

• 26 Oct 2021 Conference Paper CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Driving the Herd: Search Engines as Content Influencers

Gregory Goren, Oren Kurland, Moshe Tennenholtz, Fiana Raiber
Abstract

In competitive search settings such as the Web, many documents' authors (publishers) opt to have their documents highly ranked for some queries. To this end, they modify the documents --- specifically, their content --- in response to induced rankings. Thus, the search engine affects the content in the corpus via its ranking decisions. We present a first study of the

• 26 Oct 2021 Preprint arXiv

Video-based fully automatic assessment of open surgery suturing skills

Adam Goldbraikh, Anne-Lise D D'Angelo, Carla M Pugh, Shlomi Laufer
Abstract

The goal of this study was to develop new reliable open surgery suturing simulation system for training medical students in situation where resources are limited or in the domestic setup. Namely, we developed an algorithm for tools and hands localization as well as identifying the interactions between them based on simple webcam video data, calculating motion metrics

• 26 Oct 2021 Conference Paper CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry

Abstract

Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that data is readily available (the big data phenomenon), is oftentimes challenged by the need to effectively acquire quality data under severe constraints. In this paper we propose a design methodology

• 21 Oct 2021 Preprint arXiv

Video and Text Matching with Conditioned Embeddings

Ameen Ali, Idan Schwartz, Tamir Hazan, Lior Wolf
Abstract

We present a method for matching a text sentence from a given corpus to a given video clip and vice versa. Traditionally video and text matching is done by learning a shared embedding space and the encoding of one modality is independent of the other. In this work, we encode the dataset data in a way that takes into account the query's relevant information. The power

• 19 Oct 2021 Journal Article Psychological Medicine

Attempted suicide rates before and during the COVID-19 pandemic: interrupted time series analysis of a nationally representative sample

Yael Travis-Lumer, Arad Kodesh, Yair Goldberg, Sophia Frangou, Stephen Z Levine
Abstract
Background To characterize the association between the protracted biopsychosocial coronavirus disease 2019 (COVID-19) pandemic exposures and incident suicide attempt rates. Methods Data were from a nationally representative cohort based on electronic health records from January 2013 to February 2021 (N = 852 233), with an interrupted time series study design. For the… show more
• 13 Oct 2021 Preprint arXiv

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit
Abstract

We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expert data in a contextual MDP setup. We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms

• 7 Oct 2021 Preprint medRxiv

Protection Across Age Groups of BNT162b2 Vaccine Booster against Covid-19

Yinon M Bar-On, Yair Goldberg, Micha Mandel, Omri Bodenheimer, Laurence S Freedman, Sharon Alroy-Preis, Nachman Ash, Amit Huppert, Ron Milo
Abstract
BACKGROUND Following administration to persons 60+ years of age, the booster vaccination campaign in Israel was gradually expanded to younger age groups who received a second dose >5 months earlier. We study the booster effect on COVID-19 outcomes. METHODS We extracted data for the period July 30, 2021 to October 6, 2021 from the Israeli Ministry of Health database… show more
• 7 Oct 2021 Journal Article Decision

On the effect of perceived patterns in decisions from sampling

Doron Cohen, Kinneret Teodorescu
Abstract

Many real-life choices are based on previous experiences. Research devoted to these decisions from experience has typically employed static settings, where the probability of a given outcome is constant across trials. However, recent studies of repeated choice suggest that people tend to follow perceived patterns of outcomes even when true patterns do not exist (i.e.

• 7 Oct 2021 Journal Article The New England Journal of Medicine

Protection of BNT162b2 Vaccine Booster against Covid-19 in Israel

Yinon M Bar-On, Yair Goldberg, Micha Mandel, Omri Bodenheimer, Laurence S Freedman, Nir Kalkstein, Barak Mizrahi, Sharon Alroy-Preis, Nachman Ash, Ron Milo, Amit Huppert
Abstract
BACKGROUND On July 30, 2021, the administration of a third (booster) dose of the BNT162b2 messenger RNA vaccine (Pfizer–BioNTech) was approved in Israel for persons who were 60 years of age or older and who had received a second dose of vaccine at least 5 months earlier. Data are needed regarding the effect of the booster dose on the rate of confirmed coronavirus 2019… show more
• 5 Oct 2021 Journal Article Information Processing Letters

Robust algorithms for preemptive scheduling on uniform machines of non-increasing job sizes

Abstract

Preemptive scheduling problems of minimizing the makespan on parallel machines are basic problems. Motivated by sensitivity analysis and online algorithms, we investigate the problem of designing robust algorithms that are faced with the input one job at a time, but unlike online algorithms they are allowed to modify a small portion of the solution whenever a new job

• 2 Oct 2021 Journal Article Human Factors

Object-Process Methodology as an Alternative to Human Factors Task Analysis

Dov Dori, Ahmad Jbara, Yongkai E Yang, Andrew Liu, Charles M Oman
Abstract

Objective

We define and demonstrate the use of OPM-TA—a model-based task analysis (TA) framework that uses object-process methodology (OPM) ISO 19450 as a viable alternative to traditional TA techniques.

Background

A variety of different TA methods exist in human factors engineering, and several of them are often applied successively for a broad task representation

• 1 Oct 2021 Journal Article Clinical Microbiology and Infection

Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study

Gil Caspi, Avshalom Dayan, Yael Eshal, Sigal Liverant-Taub, Gilad Twig, Uri Shalit, Yair Lewis, Avi Shina, Oren Caspi
Abstract

Objective To analyze the correlation between COVID-19 vaccination percentage and socioeconomic status (SES). Methods A nationwide ecologic study based on open-sourced, anonymized, aggregated data provided by the Israel Ministry of Health. The correlations between municipal SES, vaccination percentage, and active COVID-19 cases during the vaccination campaign were analyzed

• 28 Sep 2021 Preprint arXiv

Unified Fair Allocation of Goods and Chores via Copies

Abstract

We consider fair allocation of indivisible items in a model with goods, chores, and copies, as a unified framework for studying: (1)~the existence of EFX and other solution concepts for goods with copies; (2)~the existence of EFX and other solution concepts for chores. We establish a tight relation between these issues via two conceptual contributions: First, a refinement

• 24 Sep 2021 Preprint arXiv

Continuity and uniqueness of percolation critical parameters in Finitary Random Interlacements

Zhenhao Cai, Eviatar B Procaccia, Yuan Zhang
Abstract

We prove that the critical percolation parameter for Finitary Random Interlacements (FRI) is continuous with respect to the path length parameter T. The proof uses a result which is interesting on its own right; equality of natural critical parameters for FRI percolation phase transition.

• 24 Sep 2021 Preprint arXiv

Discrete $\ell^{1}$ Double Bubble solution is at most ceiling +2 of the continuous solution"

Parker Duncan, Rory O'Dwyer, Eviatar B Procaccia
Abstract

In this paper we show that the solution of the discrete Double Bubble problem over $\mathbb{Z}^2$ is at most the ceiling function plus two of the continuous solution to the Double Bubble problem, with respect to the $\ell^1$ norm, found in [11] and [6].

• 23 Sep 2021 Journal Article Journal of Artificial Intelligence Research

Worst-case Bounds on Power vs. Proportion in Weighted Voting Games with an Application to False-name Manipulation

Abstract

Weighted voting games apply to a wide variety of multi-agent settings. They enable the formalization of power indices which quantify the coalitional power of players. We take a novel approach to the study of the power of big vs. small players in these games. We model small (big) players as having single (multiple) votes. The aggregate relative power of big players is

• 21 Sep 2021 Journal Article Safety Science

Architectural features and indoor evacuation wayfinding: The starting point matters

Abstract

Effective indoor wayfinding in the event of an emergency is key to guaranteeing safe and timely evacuation. However, despite the increasing number of evacuation studies, only a limited number focus on the influence of architectural elements. Through a virtual reality experiment, we create a link between human factors in indoor emergency wayfinding and architectural

• 17 Sep 2021 Journal Article Formal Methods in System Design

Vacuity in synthesis

Roderick Bloem, Hana Chockler, Masoud Ebrahimi, Ofer Strichman
Abstract

In reactive synthesis, one begins with a temporal specification $$\varphi$$, and automatically synthesizes a system $$M$$ such that $$M\models \varphi$$. As many systems can satisfy a given specification, it is natural to seek ways to force the synthesis tool to synthesize systems that are of a higher quality, in some well-defined sense. In this article we focus on

• 16 Sep 2021 Journal Article Topics in Cognitive Science

Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science

Thomas T Hills, Yoed N Kenett
Abstract

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating

• 15 Sep 2021 IIR 2021: The 11th Italian Information Retrieval Workshop

Do Hard Topics Exist? A Statistical Analysis

J Shane Culpepper, Guglielmo Faggioli, Nicola Ferro, Oren Kurland
Abstract

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. Recently there has been

• 15 Sep 2021 Preprint arXiv

PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching

Abstract

Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web and data mining for many years, the main challenge remains the ability to generate quality matches among data concepts (e.g., database attributes). In this work, we examine a novel angle on the behavior of humans as matchers, studying match

• 14 Sep 2021 Journal Article Vietnam Journal of Mathematics

The Complexity of Vector Partition

Abstract

We consider the vector partition problem, where n agents, each with a d-dimensional attribute vector, are to be partitioned into p parts so as to minimize cost which is a given function on the sums of attribute vectors in each part. The problem has applications in a variety of areas including clustering, logistics and health care. We consider the complexity and

• 13 Sep 2021 Journal Article Journal of Optimization Theory and Applications

A Dynamic Alternating Direction of Multipliers for Nonconvex Minimization with Nonlinear Functional Equality Constraints

Eyal Cohen, Nadav Hallak, Marc Teboulle
Abstract

This paper studies the minimization of a broad class of nonsmooth nonconvex objective functions subject to nonlinear functional equality constraints, where the gradients of the differentiable parts in the objective and the constraints are only locally Lipschitz continuous. We propose a specific proximal linearized alternating direction method of multipliers in which

• 12 Sep 2021 Preprint arXiv

Mixing between the Cross Entropy and the Expectation Loss Terms

Barak Battash, Lior Wolf, Tamir Hazan
Abstract

The cross entropy loss is widely used due to its effectiveness and solid theoretical grounding. However, as training progresses, the loss tends to focus on hard to classify samples, which may prevent the network from obtaining gains in performance. While most work in the field suggest ways to classify hard negatives, we suggest to strategically leave hard negatives

• 7 Sep 2021 Preprint SSRN

Herd Design

Itai Arieli, Ronen Gradwohl, Rann Smorodinsky
Abstract

The classical herding model examines the asymptotic behavior of agents who observe their predecessors' actions as well as a private signal from an exogenous information structure. In this paper, we introduce a self-interested sender into the model and study her problem of designing this information structure. If agents cannot observe each other the model reduces to the

• 2 Sep 2021 Preprint arXiv

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, Brandon M Stewart, Victor Veitch, Diyi Yang
Abstract

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary

• 1 Sep 2021 Journal Article Production and Operations Management

Two-Phase Newsvendor with Optimally Timed Additional Replenishment: Model, Algorithm, Case Study

Dina Smirnov, Yale T Herer, Assaf Avrahami
Abstract

Recent advancements in Information Technology have provided an opportunity to significantly improve the effectiveness of inventory systems. The use of in-cycle demand information enables faster reaction to demand fluctuations. In particular, for the newsvendor (NV) system, we exploit the newly available data to perform an additional review (AR) of inventory at an

• 1 Sep 2021 Journal Article Operations Research Letters

Robust learning in social networks via matrix scaling

Abstract

The influence vanishing property in social networks states that the influence of the most influential agent vanishes as society grows. Removing this assumption causes a failure of learning of boundedly rational dynamics. We suggest a boundedly rational methodology that leads to learning in almost all networks. The methodology adjusts the agent's weights based on the

• 1 Sep 2021 Journal Article Acta Psychologica

The effect of aging on facial attractiveness: An empirical and computational investigation

Dexian He, Clifford I Workman, Yoed N Kenett, Xianyou He, Anjan Chatterjee
Abstract

How does aging affect facial attractiveness? We tested the hypothesis that people find older faces less attractive than younger faces, and furthermore, that these aging effects are modulated by the age and sex of the perceiver and by the specific kind of attractiveness judgment being made. Using empirical and computational network science methods, we confirmed that with

• 1 Sep 2021 Preprint medRxiv

Viral loads of Delta-variant SARS-CoV2 breakthrough infections following vaccination and booster with the BNT162b2 vaccine

Matan Levine-Tiefenbrun, Idan Yelin, Hillel Alapi, Rachel Katz, Esma Herzel, Jacob Kuint, Gabriel Chodick, Sivan Gazit, Tal Patalon, Roy Kishony
Abstract

The BNT162b2 vaccine showed high real-life effectiveness both at preventing disease and in reducing viral loads of breakthrough infections, but coincidental with the rise of the Delta-variant SARS-CoV2, these protective effects have been decreasing, prompting a third, booster, vaccine inoculation. Here, analyzing viral loads of over 11,000 infections during the current

• 1 Sep 2021 Preprint arXiv

DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction

Entony Lekhtman, Yftah Ziser, Roi Reichart
Abstract

The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Particularly, fine-tuning a pre-trained language model on a source domain and then applying it to a different target domain

• Sep 2021 Journal Article Manufacturing & Service Operations Management

An Invitation Control Policy for Proactive Service Systems: Balancing Efficiency, Value, and Service Level

Galit B Yom-Tov, Liron Yedidsion, Yueming Xie
Abstract
Problem definition: We study the problem of designing a dynamic invitation policy for proactive service systems with finite customer patience under scarce capacity. In such systems, prior knowledge regarding customer value or importance is used to decide whether the company should offer service or not. Academic/practical relevance: Proactive service systems are becoming… show more
• Sep 2021 Journal Article Journal of Political Economy

Feasible Joint Posterior Beliefs

Abstract

We study the set of possible joint posterior belief distributions of a group of agents who share a common prior regarding a binary state and who observe some information structure. For two agents, we introduce a quantitative version of Aumann’s agreement theorem and show that it is equivalent to a characterization of feasible distributions from a 1995 work by Dawid and

• 28 Aug 2021 Journal Article Annals of Mathematics and Artificial Intelligence

Real-time solving of computationally hard problems using optimal algorithm portfolios

Abstract

Various hard real-time systems have a desired requirement which is impossible to fulfill: to solve a computationally hard optimization problem within a short and fixed amount of time T, e.g., T = 0.5 seconds. For such a task, the exact, exponential algorithms, as well as various Polynomial-Time Approximation Schemes, are irrelevant because they can exceed T. What is

• 26 Aug 2021 Journal Article Topics in Cognitive Science

Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?

Thomas T Hills, Yoed N Kenett
Abstract

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating

• 23 Aug 2021 Journal Article Journal of Combinatorial Optimization

Lower bounds for batched bin packing

Janos Balogh, József Békési, György Dósa, Leah Epstein, Asaf Levin
Abstract

We consider batched bin packing. Items are presented in a constant number of batches, and each batch should be packed before the next batch is presented. The cases of two, three, and four batches are studied. We prove improved lower bounds for the standard and parametric variants in some of the cases, and shorten the proofs for all other cases. To achieve this, we apply

• 23 Aug 2021 Journal Article Advances in Mathematics

A scaling limit for the cover time of the binary tree

Aser Cortines, Oren Louidor, Santiago Saglietti
Abstract

We consider a continuous time random walk on the rooted binary tree of depth n with all transition rates equal to one and study its cover time, namely the time until all vertices of the tree have been visited. We prove that, normalized by ${2}^{n+1}n$

• 20 Aug 2021 Conference Paper Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

Worst-case Bounds on Power vs. Proportion in Weighted Voting Games with Application to False-name Manipulation

Abstract

Weighted voting games apply to a wide variety of multi-agent settings. They enable the formalization of power indices which quantify the coalitional power of players. We take a novel approach to the study of the power of big vs.~small players in these games. We model small (big) players as having single (multiple) votes. The aggregate relative power of big players is

• 14 Aug 2021 Journal Article Vaccines

“Vaccine Passports” May Backfire: Findings from a Cross-Sectional Study in the UK and Israel on Willingness to Get Vaccinated against COVID-19

Talya Porat, Ryan Burnell, Rafael A Calvo, Elizabeth Ford, Priya Paudyal, Weston L Baxter, Avi Parush
Abstract

Domestic “vaccine passports” are being implemented across the world as a way of increasing vaccinated people’s freedom of movement and to encourage vaccination. However, these vaccine passports may affect people’s vaccination decisions in unintended and undesirable ways. This cross-sectional study investigated whether people’s willingness and motivation to get vaccinated

• 9 Aug 2021 Preprint arXiv

EPTAS for load balancing problem on parallel machines with a non-renewable resource

Abstract

The problem considered is the non-preemptive scheduling of independent jobs that consume a resource (which is non-renewable and replenished regularly) on parallel uniformly related machines. The input defines the speed of machines, size of jobs, the quantity of resource required by the jobs, the replenished quantities, and replenishment dates of the resource. Every job

• 6 Aug 2021 Journal Article Journal of Economic Theory

Abstract

We introduce the class of virtually additive non-Bayesian learning heuristics to aggregating beliefs in social networks. A virtually additive heuristic is characterized by a single function that maps a belief to a real number that represents the virtual belief. To aggregate beliefs, an agent simply sums up all the virtual beliefs of his neighbors to obtain his new

• 6 Aug 2021 Preprint arXiv

Towards Zero-shot Language Modeling

Edoardo Maria Ponti, Ivan Vulić, Ryan Cotterell, Roi Reichart, Anna Korhonen
Abstract

Can we construct a neural model that is inductively biased towards learning human languages? Motivated by this question, we aim at constructing an informative prior over neural weights, in order to adapt quickly to held-out languages in the task of character-level language modeling. We infer this distribution from a sample of typologically diverse training languages

• 5 Aug 2021 Journal Article Frontiers in Psychology

Beauty and Wellness in the Semantic Memory of the Beholder

Yoed N Kenett, L Ungar, Anjan Chatterjee
Abstract

Beauty and wellness are terms used often in common parlance, however their meaning and relation to each other is unclear. To probe their meaning, we applied network science methods to estimate and compare the semantic networks associated with beauty and wellness in different age generation cohorts (Generation Z, Millennials, Generation X, and Baby Boomers) and in women

• 1 Aug 2021 Journal Article Psychology of Aesthetics, Creativity, and the Arts

A novel coding scheme for assessing responses in divergent thinking: An embodied approach

Heath E Matheson, Yoed N Kenett
Abstract

In the present study we devised a novel coding scheme for responses generated in a divergent thinking task. Based on considerations from behavioural and neurocognitive research from an embodied perspective, our scheme aims to capture dimensions of simulations of action or the body. In an exploratory investigation, we applied our novel coding scheme to analyze responses

• Aug 2021 Conference Paper The 34th Annual Conference on Learning Theory (COLT 2021)

Frank-Wolfe with Nearest Extreme Point Oracle

Dan Garber, Noam Wolf
Abstract

We consider variants of the classical Frank-Wolfe algorithm for constrained smooth convex minimization, that instead of access to the standard oracle for minimizing a linear function over the feasible set, have access to an oracle that can find an extreme point of the feasible set that is closest in Euclidean distance to a given vector. We first show that for many

• Aug 2021 Conference Paper Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing

A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters

Mengjie Zhao, Yi Zhu, Ehsan Shareghi, Ivan Vuli, Roi Reichart, Anna Korhonen, Hinrich Schütze
Abstract

Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model

• Aug 2021 Conference Paper Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing

Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions

Daniel Rosenberg, Itai Gat, Amir Feder, Roi Reichart
Abstract

Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations

• Aug 2021 Journal Article IEEE Transactions on Knowledge and Data Engineering

Learning to Rerank Schema Matches

Avigdor Gal, Haggai Roitman, Roee Shraga
Abstract

Schema matching is at the heart of integrating structured and semi-structured data with applications in data warehousing, data analysis recommendations, Web table matching, etc. Schema matching is known as an uncertain process and a common method to overcome this uncertainty introduces a human expert with a ranked list of possible schema matches to choose from, known

• 29 Jul 2021 Conference Paper Proceedings of the Annual Meeting of the Cognitive Science Society

Creative Foraging: Examining Relations Between Foraging Styles, Semantic Memory Structure, and Creative Thinking

Yoed N Kenett, Brendan S Baker, Thomas T Hills, Yuval Hart, Roger E Beaty
Abstract

Creativity has been separately related to differences in foraging search styles and semantic memory structure. Here, we converge computational methods to examine the relation of creative foraging styles, semantic memory structure, and creative thinking. A large sample of participants was divided into groups based on their exploration and exploitation strategies in a

• 29 Jul 2021 Preprint bioRxiv

Brain connectivity-based prediction of real-life creativity is mediated by semantic memory structure

Marcela Ovando Tellez, Yoed N Kenett, Mathias Benedek, Matthieu Bernard, Joan Belo, Benoit Beranger, Theophile Bieth, Emmanuelle Volle
Abstract

Creative cognition relies on the ability to form remote associations between concepts, which allows to generate novel ideas or solve new problems. Such an ability is related to the organization of semantic memory; yet whether real-life creative behavior relies on semantic memory organization and its neural substrates remains unclear. Therefore, this study explored

• 28 Jul 2021 Preprint arXiv

From Monopoly to Competition: Optimal Contests Prevail

Xiaotie Deng, Yotam Gafni, Ron Lavi, Tao Lin, Hongyi Ling
Abstract

We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximize the contestants' sum of efforts. Our main result shows that optimal contests in the monopolistic setting (i.e., those that maximize the sum of efforts in a model with a single contest) form

• 27 Jul 2021 Journal Article Trends in Cognitive Sciences

To predict human choice, consider the context

Abstract

Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive

• 27 Jul 2021 Conference Paper The 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021)

Bandits with Partially Observable Confounded Data

Abstract

We study linear contextual bandits with access to a large, confounded, offline dataset that was sampled from some fixed policy. We show that this problem is closely related to a variant of the bandit problem with side information. We construct a linear bandit algorithm that takes advantage of the projected information, and prove regret bounds. Our results demonstrate

• 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Abstract

We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance -- a level of unidentifiability -- about an individual's response to treatment by inducing bias in CATE estimates. We present a new parametric interval estimator suited for

• 21 Jul 2021 Conference Paper arXiv

Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression

Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet
Abstract

We propose to analyse the conditional distributional treatment effect (CoDiTE), which, in contrast to the more common conditional average treatment effect (CATE), is designed to encode a treatment's distributional aspects beyond the mean. We first introduce a formal definition of the CoDiTE associated with a distance function between probability measures. Then we discuss

• 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning, ICML

Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization

Abstract

Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative models, by introducing a randomized predictor that

• 21 Jul 2021 Conference Paper Proceedings of the 38th International Conference on Machine Learning, ICML

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach

Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Abstract

This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and there are no gradient-based algorithmic frameworks capable of providing a solution. On that account, we propose a

• 20 Jul 2021 Preprint arXiv

Effect of small noise on the speed of reaction-diffusion equations with non-Lipschitz drift

Abstract

We consider the $[0,1]$-valued solution $(u_{t,x}:t\geq 0, x\in \mathbb R)$ to the one dimensional stochastic reaction diffusion equation with Wright-Fisher noise $\partial_t u = \partial_x^2 u + f(u) + \epsilon \sqrt{u(1-u)} \dot W.$ Here, $W$ is a space-time white noise, $\epsilon > 0$ is the noise strength, and $f$ is a continuous function on $[0,1]$ satisfying