On the Impact of Information Acquisition and Aftermarkets on Auction Efficiency

A common assumption in auction theory is that the information available to agents is given exogenously and that the auctioneer has full control over the market . We show that if an auction is smooth (e.g., first-price auction, all-pay auction), then the corresponding price of anarchy bound due to smoothness continues to hold in any environment with (a) information acquisition onopponents’ valuations, and/or (b) an aftermarket satisfying two mild conditions(voluntary participation and weak budget balance) We also consider the specialcase with two ex ante symmetric bidders, where the first- price auction is knownto be efficient in isolation .…

The Element Extraction Problem and the Cost of Determinism and Limited Adaptivity in Linear Queries

This work studies the fundamental search problem of\textsc{element-extraction} in a query model that combines both: linearmeasurements with bounded adaptivity . In the problem, one is given a nonzero vector$\mathbf{z} = (z_1,\ldots,z_n) and must report an index $i$where $z_i = 1$. This problem admits an efficient nonadaptiverandomized solution (through the well known technique of $ell_0$-sampling) and an efficient fully adaptive deterministic solution .…

Everybody Is Unique Towards Unbiased Human Mesh Recovery

Recent progress in mesh recovery has been restricted to images of non-obese people . We show our method acts as adrop-in to improve the performance of contemporary meshestimation methods . A key innovation of this technique is that it does not rely onsupervision from expensive-to-create mesh parameters .…

Domain Irrelevant Representation Learning for Unsupervised Domain Generalization

Domain generalization aims to help models trained on a set of sourcedomains generalize better on unseen target domains . We propose a Domain-Irrelevant UnsupervisedLearning (DIUL) method to cope with the significant and misleadingheterogeneity within unlabeled data . DIUL can not onlycounterbalance the scarcity of labeled data but also strengthen thegeneralization ability of models when the labeled data are sufficient .…

3D Parametric Wireframe Extraction Based on Distance Fields

We present a pipeline for parametric wireframe extraction from denselysampled point clouds . Our approach processes a scalar distance field that represents proximity to the nearest sharp feature curve . In intermediatestages, it detects corners, constructs curve segmentation, and builds atopological graph fitted to wireframe .…

Contextual Games Multi Agent Learning with Side Information

We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round . By means of kernel-based regularity assumptions, we model the correlation between different contexts and outcomes . We propose a novel online algorithm that exploits suchcorrelations to minimize the contextual regret of individual players .…

Coloring graphs with forbidden bipartite subgraphs

A conjecture of Alon, Krivelevich, and Sudakov states that, for any graph$F$ there is a constant $c_F 0$ such that if $G$ is an $F$-free graph of maximum degree, $C_F$ is a $F-free $K-free . We improve thisbound to $(1+o(1) \Delta/\log \Delta$ making the constant factor independent of $t .…

ProGS Property Graph Shapes Language Extended Version

Property graphs constitute data models for representing knowledge graphs . They allow for the convenient representation of facts, including facts about facts, represented by triples in subject or object position of other triples . In RDF representations, this error can be addressed by shapelanguages such as SHACL or ShEx .…

Contrastive Learning for Cold Start Recommendation

Recommending cold-start items is a long-standing and fundamental challenge inrecommender systems . CFscheme fails to use collaborative signals to infer user preference on these items . CLCRec aims to maximize themutual dependencies between item content and collaborative signals . It allows us to preserve collaborative signals in the content representations in thecontent representations for both warm and cold start items .…

Separating LREC from LFP

LREC= is an extension of first-order logic with a logarithmic recursion operator . It was introduced by Grohe et al. and shown to capture complexityclass L over trees and interval graphs . It does not capture L in general as it is contained in FPC – fixed-point logic with counting .…

Giotto ph A Python Library for High Performance Computation of Persistent Homology of Vietoris Rips Filtrations

giotto-ph is based on Morozov andNigmetov’s lockfree (multicore) implementation of Ulrich Bauer’s Ripser package . It also contains a re-implementation of Boissonnat and Pritam’s “EdgeCollapser”, implemented so far only in the GUDHI library . The final implementation of our persistent homology backend establishes a new state ofthe art, surpassing even GPU-accelerated implementations such as Ripsing when using as few as 5–10 CPU cores .…

EduCOR An Educational and Career Oriented Recommendation Ontology

EduCOR is an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems . The EduCOR ontology is designed to enable learningmaterial repositories to offer learning path recommendations . It is designed for the user’s learning goals, academic and psychological parameters, and the labour-market skills .…

Incremental Vulnerability Detection via Back Propagating Symbolic Execution of Insecurity Separation Logic

We present the first compositional, incremental static analysis for detectingmemory-safety and information leakage vulnerabilities in C-like programs . We develop the first under-approximate relational program logics, including InsecSL . We show how InSecSL can be automated viaback-propagating symbolic execution (BPSE) to build a bottom-up,inter-procedural and incremental analysis .…

PonderNet Learning to Ponder

In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt . PonderNet learns end-to-end the number of computational steps to achieve aneffective compromise between training prediction accuracy, computational cost and generalization .…

Tortured phrases A dubious writing style emerging in science Evidence of critical issues affecting established journals

More complex AI-powered generation techniques produce texts indistinguishable from that of humans . Some websites offer to rewrite texts for free, generating gobbledegook full of tortured phrases . We believe some authors used rewrittentexts to pad their manuscripts . Deception with synthetic texts threatens the integrity of the scientific literature.…

Inapproximability of counting hypergraph colourings

Recent developments in approximate counting have made startling progress indeveloping fast algorithmic methods for approximating the number of solutionsto constraint satisfaction problems (CSPs) with large arities . However, the boundaries of these methods for CSPs with non-Boolean domain are not well-understood .…

Let s Play for Action Recognizing Activities of Daily Living by Learning from Life Simulation Video Games

Recognizing Activities of Daily Living (ADL) is a vital process forintelligent assistive robots . But collecting large annotated datasets require temporal labeling and raises privacy concerns . We build Sims4Action by specifically executingactions-of-interest in a “top-down” manner . We integrate two modern algorithms for video-based activityrecognition in our framework .…

Inapproximability of counting hypergraph colourings

Recent developments in approximate counting have made startling progress indeveloping fast algorithmic methods for approximating the number of solutionsto constraint satisfaction problems (CSPs) with large arities . However, the boundaries of these methods for CSPs with non-Boolean domain are not well-understood .…

On the Computational Complexity of the Chain Rule of Differential Calculus

Many modern numerical methods in computational science and engineering rely on derivatives of mathematical models for the phenomena under investigation . The computation of these derivatives often represents the bottleneck in termsof overall runtime performance . The chain rule of differentiation is the fundamental prerequisite for computing accurate derivatives of composite functions which perform apotentially very large number of elemental function evaluations .…

MidiBERT Piano Large scale Pre training for Symbolic Music Understanding

This paper presents an attempt to employ the mask language modeling approach of BERT to pre-train a 12-layer Transformer model over 4,166 pieces of polyphonic piano MIDI files for tackling a number of symbolic-domaindiscriminative music understanding tasks . We find that, given a pre-trained Transformer, our models outperform recurrent neural network based baselines with less than 10epochs of fine-tuning .…

Peer Selection with Noisy Assessments

In this paper we extend PeerNomination, the most accurate peer reviewing algorithm to date, into WeightedPeerNomination . We show analytically that a weighting scheme can improve the overall accuracy of the selection significantly . We explicitly formulate assessors’ reliability weights in a way that doesn’t violate strategyproofness, and use this information to reweight their scores .…

ProGS Property Graph Shapes Language Extended Version

Property graphs constitute data models for representing knowledge graphs . They allow for the convenient representation of facts, including facts about facts, represented by triples in subject or object position of other triples . In RDF representations, this error can be addressed by shapelanguages such as SHACL or ShEx .…

A New Approach for Active Automata Learning Based on Apartness

We present a new and simple approach to active automata learning . Instead of focusing on equivalence of observations, $L^{\#}$ takes a different perspective . It triesto establish apartness, a constructive form of inequality . Experiments with a prototype implementation, written in Rust, suggest that $L${\#$ outperforms existing algorithms .…

Worst Case Welfare of Item Pricing in the Tollbooth Problem

We study the worst-case welfare of item pricing in the tollbooth problem . The problem is a special case of thecombinatorial auction in which (i) each of the $m$ items in the auction is anedge of some underlying graph . We show that the gap between the two settings is atleast a constant even when the underlying graph is a single path .…

Coherent differentiation

The categorical models of the differential lambda-calculus are additivecategories because of the Leibniz rule which requires the summation of twoexpressions . This means that, as far as the differential Lambdacalculus and linear logic are concerned, these models feature finitenondeterminism . We introduce a categorical framework for differentiation which does not requireadditivity and is compatible with deterministic models such as coherence spaces .…

Algorithmic Causal Effect Identification with causaleffect

The report is to review and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data . The main identification algorithm can be seen as a repeated application of the rules of $do$-calculus . We introduce our newly developed Python library and givesome usage examples .…

Open Loop Equilibrium Strategies for Dynamic Influence Maximization Game Over Social Networks

We consider the problem of budget allocation for competitive influencemaximization over social networks . In this problem, multiple competing parties(players) want to distribute their limited advertising resources over a set of social individuals to maximize their long-run cumulative payoffs . We show that the optimal investment strategy for the case of a single-playercan be found in polynomial time by solving a concave program, and theopen-loop equilibrium strategies for the multiplayer dynamic game can becomputed efficiently by following natural regret minimization dynamics .…

Giotto ph A Python Library for High Performance Computation of Persistent Homology of Vietoris Rips Filtrations

giotto-ph is based on Morozov andNigmetov’s lockfree (multicore) implementation of Ulrich Bauer’s Ripser package . It also contains a re-implementation of Boissonnat and Pritam’s “EdgeCollapser”, implemented so far only in the GUDHI library . The final implementation of our persistent homology backend establishes a new state ofthe art, surpassing even GPU-accelerated implementations such as Ripsing when using as few as 5–10 CPU cores .…

A New Approach for Active Automata Learning Based on Apartness

We present a new and simple approach to active automata learning . Instead of focusing on equivalence of observations, $L^{\#}$ takes a different perspective . It triesto establish apartness, a constructive form of inequality . Experiments with a prototype implementation, written in Rust, suggest that $L${\#$ outperforms existing algorithms .…

Parallel Element based Algebraic Multigrid for H curl and H div Problems Using the ParELAG Library

This paper presents the use of element-based algebraic multigrid (AMGe)hierarchies, implemented in the ParELAG (Parallel Element AgglomerationAlgebraic Multigrid Upscaling and Solvers) library . It produces multilevelvelpreconditioners and solvers for H(curl) and H(div) formulations . This paper demonstrates some of the capabilities of the library and outlines some of its components and procedures .…

A Splitting Scheme for Flip Free Distortion Energies

We introduce a robust optimization method for flip-free distortion energies . We exploit the special structure of distortion energies to employ an operatorsplitting technique . The scheme results in an efficient method where theglobal step involves a single matrix multiplication and the local steps are closed-form per-triangle/per-tetrahedron expressions that are highlyparallelizable .…

How to Approximate Ontology Mediated Queries

We introduce and study several notions of approximation for ontology-mediatedqueries based on description logics ALC and ALCI . Our approximations are oftwo kinds: we may (1) replace the ontology with one formulated in a tractableontology language such as ELI or certain TGDs .…

Teaching Design by Contract using Snap

Snap! is a visual programming language aimed at high school students . We provide support both for static and dynamic verification of Snap! programs . Special attention is given to the error messaging, to make this as intuitive as possible .…

ROBIN A Robust Optical Binary Neural Network Accelerator

Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs . However, mapping sophisticated neural network models on theseaccelerators still entails significant energy and memory consumption, alongwith high inference time overhead .…

Quantum Radon Transform and Its Application

A new kind ofperiodic discrete Radon transform (PDRT) called quantum Radon transforms (QRT) is proposed . The QRT has a quantum implementation that is exponentially faster than the classical Radon Transform . The simulation results show that QRTpreserves the good denoising capability as in the classical PDRT .…

Separating LREC from LFP

LREC= is an extension of first-order logic with a logarithmic recursion operator . It was introduced by Grohe et al. and shown to capture complexityclass L over trees and interval graphs . It does not capture L in general as it is contained in FPC – fixed-point logic with counting .…

Strong recovery of geometric planted matchings

We study the problem of efficiently recovering the matching between anunlabelled collection of $n$ points in $\mathbb{R}^d$ and a small randomperturbation of those points . In this setting, the maximum likelihood estimator can be found in polynomial time as the solution of a linear assignmentproblem .…