Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
1911.03078	Adversarial Attacks on GMM i-vector based Speaker Verification Systems	This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring func...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	152,545
1610.06912	KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs	Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research we f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	62,712
2302.04963	Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal	We give query complexity lower bounds for convex optimization and the related feasibility problem. We show that quadratic memory is necessary to achieve the optimal oracle complexity for first-order convex optimization. In particular, this shows that center-of-mass cutting-planes algorithms in dimension $d$ which use $...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	344,877
1008.1615	Optimal Partitioned Cyclic Difference Packings for Frequency Hopping and Code Synchronization	Optimal partitioned cyclic difference packings (PCDPs) are shown to give rise to optimal frequency-hopping sequences and optimal comma-free codes. New constructions for PCDPs, based on almost difference sets and cyclic difference matrices, are given. These produce new infinite families of optimal PCDPs (and hence optim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	7,235
1903.12178	Open-ended Evolution and a Mechanism of Novelties in Web Services	Analogous to living ecosystems in nature, web services form an artificial ecosystem consisting of many tags and their associated media, such as photographs, movies, and web pages created by human users. Concerning biological ecosystems, we regard tag as a species and human as a hidden environmental resource. We subsequ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	125,659
2301.05761	Uncertainty Quantification for Local Model Explanations Without Model Access	We present a model-agnostic algorithm for generating post-hoc explanations and uncertainty intervals for a machine learning model when only a static sample of inputs and outputs from the model is available, rather than direct access to the model itself. This situation may arise when model evaluations are expensive; whe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,435
2404.14355	Pre-Calc: Learning to Use the Calculator Improves Numeracy in Language Models	Quantitative and numerical comprehension in language is an important task in many fields like education and finance, but still remains a challenging task for language models. While tool and calculator usage has shown to be helpful to improve mathematical reasoning in large pretrained decoder-only language models, this ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	448,648
2403.14221	Improving the Robustness of Large Language Models via Consistency Alignment	Large language models (LLMs) have shown tremendous success in following user instructions and generating helpful responses. Nevertheless, their robustness is still far from optimal, as they may generate significantly inconsistent responses due to minor changes in the verbalized instructions. Recent literature has explo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	439,967
2309.13635	PanopticNDT: Efficient and Robust Panoptic Mapping	As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what their spatial extent i...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	394,295
1807.10058	Fast cosine transform for FCC lattices	Voxel representation and processing is an important issue in a broad spectrum of applications. E.g., 3D imaging in biomedical engineering applications, video game development and volumetric displays are often based on data representation by voxels. By replacing the standard sampling lattice with a face-centered lattice...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	103,864
2001.00234	Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach	We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a nov...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	159,163
1811.08181	HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings	To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed -- giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw). Giv...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	113,978
2401.04651	Learning to Prompt Segment Anything Models	Segment Anything Models (SAMs) like SEEM and SAM have demonstrated great potential in learning to segment anything. The core design of SAMs lies with Promptable Segmentation, which takes a handcrafted prompt as input and returns the expected segmentation mask. SAMs work with two types of prompts including spatial promp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,497
1601.04036	Microdatabases for the Industrial Internet	The Industrial Internet market is targeted to grow by trillions of US dollars by the year 2030, driven by adoption, deployment and integration of billions of intelligent devices and their associated data. This digital expansion faces a number of significant challenges, including reliable data management, security and p...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	50,971
2008.01342	LoCo: Local Contrastive Representation Learning	Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and is not biologically plausible. Recent advances in unsupervised contrastive representation learning point to the question of whether a learn...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	190,285
2210.16068	Using Supervised Deep-Learning to Model Edge-FBG Shape Sensors	Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving precise and reliable motion control of such snake-like manipulators necessitates an accurate navigation system that requires no line-of-sight and...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	327,210
2109.06352	Uncertainty-Aware Machine Translation Evaluation	Several neural-based metrics have been recently proposed to evaluate machine translation quality. However, all of them resort to point estimates, which provide limited information at segment level. This is made worse as they are trained on noisy, biased and scarce human judgements, often resulting in unreliable quality...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	255,116
2208.11856	Design and Implementation of a Human-Robot Joint Action Framework using Augmented Reality and Eye Gaze	When humans work together to complete a joint task, each person builds an internal model of the situation and how it will evolve. Efficient collaboration is dependent on how these individual models overlap to form a shared mental model among team members, which is important for collaborative processes in human-robot te...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	314,557
2401.05727	Zero Resource Cross-Lingual Part Of Speech Tagging	Part of speech tagging in zero-resource settings can be an effective approach for low-resource languages when no labeled training data is available. Existing systems use two main techniques for POS tagging i.e. pretrained multilingual large language models(LLM) or project the source language labels into the zero resour...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,888
1307.6459	Distortion bounds and Two-Way Protocols for One-Shot Transmission of Correlated Random Variables	This paper provides lower bounds on the reconstruction error for transmission of two continuous correlated random vectors sent over both sum and parallel channels using the help of two causal feedback links from the decoder to the encoders connected to each sensor. This construction is considered for both uniformly and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,026
1811.06825	Towards a Science of Mind	The ancient mind/body problem continues to be one of deepest mysteries of science and of the human spirit. Despite major advances in many fields, there is still no plausible link between subjective experience (qualia) and its realization in the body. This paper outlines some of the elements of a rigorous science of min...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	113,604
2103.10292	How I failed machine learning in medical imaging -- shortcomings and recommendations	Medical imaging is an important research field with many opportunities for improving patients' health. However, there are a number of challenges that are slowing down the progress of the field as a whole, such optimizing for publication. In this paper we reviewed several problems related to choosing datasets, methods, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	225,413
1202.4534	Bifurcation Boundary Conditions for Switching DC-DC Converters Under Constant On-Time Control	Sampled-data analysis and harmonic balance analysis are applied to analyze switching DC-DC converters under constant on-time control. Design-oriented boundary conditions for the period-doubling bifurcation and the saddle-node bifurcation are derived. The required ramp slope to avoid the bifurcations and the assigned po...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	14,506
1807.01957	Low Overhead Weighted-Graph-Coloring-Based Two-Layer Precoding for FDD Massive MIMO Systems	A massive multiple-input multiple-output (MIMO) system, operating in Frequency Division Duplexing (FDD) mode of operation, suffers from prohibitively high overhead associated with downlink channel state information (CSI) acquisition and downlink precoding, due to the lack of uplink/downlink channel reciprocity. In this...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	102,166
2109.04352	PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery	Correctly capturing intraoperative brain shift in image-guided neurosurgical procedures is a critical task for aligning preoperative data with intraoperative geometry for ensuring accurate surgical navigation. While the finite element method (FEM) is a proven technique to effectively approximate soft tissue deformation...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	254,371
1401.4335	On the Controllability and Observability of Networked Dynamic Systems	Some necessary and sufficient conditions are obtained for the controllability and observability of a networked system with linear time invariant (LTI) dynamics. The topology of this system is fixed but arbitrary, and every subsystem is permitted to have different dynamic input-output relations. These conditions essenti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,063
2401.01836	Neural Control: Concurrent System Identification and Control Learning with Neural ODE	Controlling continuous-time dynamical systems is generally a two step process: first, identify or model the system dynamics with differential equations, then, minimize the control objectives to achieve optimal control function and optimal state trajectories. However, any inaccuracy in dynamics modeling will lead to sub...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	419,515
2003.05597	On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited	Arbitrary-oriented object detection has been a building block for rotation sensitive tasks. We first show that the boundary problem suffered in existing dominant regression-based rotation detectors, is caused by angular periodicity or corner ordering, according to the parameterization protocol. We also show that the ro...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	167,896
2502.10973	Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues	In this paper, we introduce the Akan Conversation Emotion (ACE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. ACE, developed for the Akan language, contains 385 emotion-labeled dial...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,125
2404.01819	Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection	In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries. In DETR-based SSOD, the one-to-one assignment strategy provides inaccurate pseudo-labels, while the one-to-many assignments strat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,599
2205.09327	Let's Talk! Striking Up Conversations via Conversational Visual Question Generation	An engaging and provocative question can open up a great conversation. In this work, we explore a novel scenario: a conversation agent views a set of the user's photos (for example, from social media platforms) and asks an engaging question to initiate a conversation with the user. The existing vision-to-question model...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	297,225
1802.10591	Stereoscopic Neural Style Transfer	This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR/VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,563
2201.08810	GAP-Gen: Guided Automatic Python Code Generation	Automatic code generation from natural language descriptions can be highly beneficial during the process of software development. In this work, we propose GAP-Gen, a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. We first introduce Python syntactic constra...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	276,452
1506.06681	Adaptive Digital Scan Variable Pixels	The square and rectangular shape of the pixels in the digital images for sensing and display purposes introduces several inaccuracies in the representation of digital images. The major disadvantage of square pixel shapes is the inability to accurately capture and display the details in the objects having variable orien...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,439
2209.00552	Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System	This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a specific use case of an NNCS performing autonomous formation flight while an RTA ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,617
2412.12119	Mastering Board Games by External and Internal Planning with Language Models	While large language models perform well on a range of complex tasks (e.g., text generation, question answering, summarization), robust multi-step planning and reasoning remains a considerable challenge for them. In this paper we show that search-based planning can significantly improve LLMs' playing strength across se...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	517,733
2406.02539	Parrot: Multilingual Visual Instruction Tuning	The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V has marked a significant step towards artificial general intelligence. Existing methods mainly focus on aligning vision encoders with LLMs through supervised fine-tuning (SFT) to endow LLMs with multimodal abilities, making MLLMs' inherent ab...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	460,814
2111.14482	High Quality Segmentation for Ultra High-resolution Images	To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among obje...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,614
2407.17863	factgenie: A Framework for Span-based Evaluation of Generated Texts	We present factgenie: a framework for annotating and visualizing word spans in textual model outputs. Annotations can capture various span-based phenomena such as semantic inaccuracies or irrelevant text. With factgenie, the annotations can be collected both from human crowdworkers and large language models. Our framew...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	476,152
1612.04451	Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation	Reconstructing brain activity through electroencephalography requires a boundary value problem (BVP) solver to take a proposed distribution of current dipoles within the brain and compute the resulting electrostatic potential on the scalp. This article proposes the use of sequential kriging optimization to identify dif...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	65,524
2209.10229	Intelligent wayfinding vehicle design based on visual recognition	Intelligent drug delivery trolley is an advanced intelligent drug delivery equipment. Compared with traditional manual drug delivery, it has higher drug delivery efficiency and lower error rate. In this project, an intelligent drug delivery car is designed and manufactured, which can recognize the road route and the ro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,796
2010.13321	View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose	Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant appearance variations across viewpoints that make the recognition tasks challenging. To address this, we explor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,085
1603.02752	Best-of-K Bandits	This paper studies the Best-of-K Bandit game: At each time the player chooses a subset S among all N-choose-K possible options and observes reward max(X(i) : i in S) where X is a random vector drawn from a joint distribution. The objective is to identify the subset that achieves the highest expected reward with high pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	53,051
2404.07686	Depth Estimation using Weighted-loss and Transfer Learning	Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance m...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	445,935
2402.13851	VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models	Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning capabilities in a multimodal context. Recently, multimodal instruction tuning has been proposed to further enhance instruction-following abilities. However, we uncover the potential threat posed by backdoor attacks on autoregressive VLMs...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	431,436
2204.08134	A Practical Cross-Device Federated Learning Framework over 5G Networks	The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising the privacy. However, limited by the capacity of wireless data transmission, the emp...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	291,975
2407.16696	PartGLEE: A Foundation Model for Recognizing and Parsing Any Objects	We present PartGLEE, a part-level foundation model for locating and identifying both objects and parts in images. Through a unified framework, PartGLEE accomplishes detection, segmentation, and grounding of instances at any granularity in the open world scenario. Specifically, we propose a Q-Former to construct the hie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,692
1904.06517	Improving detection of protein-ligand binding sites with 3D segmentation	In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks lik...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,572
1505.00162	A Modification of the Halpern-Pearl Definition of Causality	The original Halpern-Pearl definition of causality [Halpern and Pearl, 2001] was updated in the journal version of the paper [Halpern and Pearl, 2005] to deal with some problems pointed out by Hopkins and Pearl [2003]. Here the definition is modified yet again, in a way that (a) leads to a simpler definition, (b) handl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	42,674
2303.04418	FUSQA: Fetal Ultrasound Segmentation Quality Assessment	Deep learning models have been effective for various fetal ultrasound segmentation tasks. However, generalization to new unseen data has raised questions about their effectiveness for clinical adoption. Normally, a transition to new unseen data requires time-consuming and costly quality assurance processes to validate ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	350,083
2205.14025	Inference and Sampling for Archimax Copulas	Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas are a family of distributions endowed with a precise represent...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,182
2112.04894	Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer	Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has shown encouraging results in fully supervised medical image segmentation. However, it is still challenging for them to achieve good performance with limited annotations for training. In this work, we present a very simple yet efficie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,675
2206.14389	Data Redaction from Pre-trained GANs	Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train them differently from scratch using different data or different regularization -- which uses a lot of computational resources and does not alwa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,273
1910.14033	Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control	Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficientl...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	151,542
2408.10284	AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference	Mixture-of-Experts (MoE) models are designed to enhance the efficiency of large language models (LLMs) without proportionally increasing the computational demands. However, their deployment on edge devices still faces significant challenges due to high on-demand loading overheads from managing sparsely activated expert...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,799
1711.07721	The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack	Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an ac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,058
1805.06539	Beyond Structural Causal Models: Causal Constraints Models	Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), an...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	97,628
2205.11775	Constrained Monotonic Neural Networks	Wider adoption of neural networks in many critical domains such as finance and healthcare is being hindered by the need to explain their predictions and to impose additional constraints on them. Monotonicity constraint is one of the most requested properties in real-world scenarios and is the focus of this paper. One o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	298,277
2307.07686	Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++	In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset is created from a range of representative open-source OpenMP benchmarks. It is also refined using a meticulous code similarity test. The ef...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	379,502
2310.06837	Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency	Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses. Moreover, many tests require multiple distinct sets of questions administered throughout the school year to closely monitor students' progress, know...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	398,731
cmp-lg/9405025	An Optimal Tabular Parsing Algorithm	In this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algorithms, and a parallel algorithm. We show that these algorithms are based on the same underlying ideas. By relating existing ideas, we hope ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,069
2406.16052	Pivotal Auto-Encoder via Self-Normalizing ReLU	Sparse auto-encoders are useful for extracting low-dimensional representations from high-dimensional data. However, their performance degrades sharply when the input noise at test time differs from the noise employed during training. This limitation hinders the applicability of auto-encoders in real-world scenarios whe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,978
1802.04085	Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case	In this paper, we study the Empirical Risk Minimization problem in the non-interactive local model of differential privacy. In the case of constant or low dimensionality ($p\ll n$), we first show that if the ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity, to achieve e...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	90,140
2405.12203	Accelerating Relative Entropy Coding with Space Partitioning	Relative entropy coding (REC) algorithms encode a random sample following a target distribution $Q$, using a coding distribution $P$ shared between the sender and receiver. Sadly, general REC algorithms suffer from prohibitive encoding times, at least on the order of $2^{D_{\text{KL}}[Q\|\|P]}$, and faster algorithms are...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	455,433
1912.07219	Prospect Theory for Human-Centric Communications	Entering the 5G/6G era, the core concept of human-centric communications has intensified the search effort into analytical frameworks for integrating technological and non-technological domains. Among non-technological domains, human behavioral, psychological, and socio-economic contexts are widely considered as indisp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	157,550
2305.17420	CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization	To efficiently extract textual information from color degraded document images is a significant research area. The prolonged imperfect preservation of ancient documents has led to various types of degradation, such as page staining, paper yellowing, and ink bleeding. These types of degradation badly impact the image pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,582
1901.04028	Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology	Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,542
1903.03587	An innovative method to determine optimum insulation thickness based on non-uniform adaptive moving grid	It is well known that thermal insulation is a leading strategy for reducing energy consumption associated to heating or cooling processes in buildings. Nevertheless, building insulation can generate high expenditures so that the selection of an optimum insulation thickness requires a detailed energy simulation as well ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	123,769
1707.07342	An Online Learning Approach to Buying and Selling Demand Response	We adopt the perspective of an aggregator, which seeks to coordinate its purchase of demand reductions from a fixed group of residential electricity customers, with its sale of the aggregate demand reduction in a two-settlement wholesale energy market. The aggregator procures reductions in demand by offering its custom...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	77,606
1904.01664	Mirroring to Build Trust in Digital Assistants	We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user st...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	126,202
2301.01997	Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games	In this paper, we formulate inverse reinforcement learning (IRL) as an expert-learner interaction whereby the optimal performance intent of an expert or target agent is unknown to a learner agent. The learner observes the states and controls of the expert and hence seeks to reconstruct the expert's cost function intent...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	339,386
1801.00588	Improving Stock Market Prediction via Heterogeneous Information Fusion	Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	87,593
2408.00636	Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images	Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,910
1704.05948	Semi-supervised classification for dynamic Android malware detection	A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	72,095
1904.03111	PoMo: Generating Entity-Specific Post-Modifiers in Context	We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, "Barack Obama, _______, suppo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	126,613
1807.04912	Perceptrons from Memristors	Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for a network in which both the synapses and the neurons are implemented u...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	102,828
1809.04014	Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection	A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	107,449
2209.13048	Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments	Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy. The meta-policy, when adapted over only a small (or just a single) number of steps, is able to perform near-optimally on a new, related task. However, a major challenge to ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	319,752
2201.03481	Learning Population-level Shape Statistics and Anatomy Segmentation From Images: A Joint Deep Learning Model	Statistical shape modeling is an essential tool for the quantitative analysis of anatomical populations. Point distribution models (PDMs) represent the anatomical surface via a dense set of correspondences, an intuitive and easy-to-use shape representation for subsequent applications. These correspondences are exhibite...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,864
cs/0202027	BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)	We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execu...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	537,509
2305.18111	The Minimax Risk in Testing Uniformity of Poisson Data under Missing Ball Alternatives within a Hypercube	We study the problem of testing the goodness of fit of occurrences of items from many categories to an identical Poisson distribution over the categories. As a class of alternative hypotheses, we consider the removal of an $\ell_p$ ball, $p \leq 2$, of radius $\epsilon$ from a hypercube around the sequence of uniform P...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	368,863
2406.19317	Jump Starting Bandits with LLM-Generated Prior Knowledge	We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been widely used in recommendation systems to generate personalized suggestions based on user-specific contexts. We show that LLMs, pre-trained o...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	468,379
2211.15997	MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations	Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	333,478
2401.08121	CycLight: learning traffic signal cooperation with a cycle-level strategy	This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditional RL-based traffic controllers that focus on step-by-step decision making, CycLight adopts a cycle-level strategy, optimizing cycle lengt...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	421,773
2303.04337	Strategic Planning for Flexible Agent Availability in Large Taxi Fleets	In large-scale multi-agent systems like taxi fleets, individual agents (taxi drivers) are self-interested (maximizing their own profits) and this can introduce inefficiencies in the system. One such inefficiency is with regard to the "required" availability of taxis at different time periods during the day. Since a tax...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	350,046
2305.13071	Machine-Created Universal Language for Cross-lingual Transfer	There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and translate-test, which explicitly translates different languages into an intermediate language, such as English. Translate-test offers better inte...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,330
2212.10438	Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks	Semantic communications seeks to transfer information from a source while conveying a desired meaning to its destination. We model the transmitter-receiver functionalities as an autoencoder followed by a task classifier that evaluates the meaning of the information conveyed to the receiver. The autoencoder consists of ...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	337,462
2209.07921	ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery	The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets often exhibit highly imbalanced distribution, which is overlooked by the current literature but may severely compromise the fairness and ge...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	317,942
2103.01476	Time-Optimal Navigation in Uncertain Environments with High-Level Specifications	Mixed observable Markov decision processes (MOMDPs) are a modeling framework for autonomous systems described by both fully and partially observable states. In this work, we study the problem of synthesizing a control policy for MOMDPs that minimizes the expected time to complete the control task while satisfying synta...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	222,633
2407.20879	A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning	The integration of knowledge graphs and graph machine learning (GML) in genomic data analysis offers several opportunities for understanding complex genetic relationships, especially at the RNA level. We present a comprehensive approach for leveraging these technologies to analyze genomic variants, specifically in the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	477,317
2210.08065	Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion	Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive, requiring large training datasets and replay buffers to achieve performant results. This poses a challenge for the next generation of field ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	323,966
1211.1752	3D Scene Grammar for Parsing RGB-D Pointclouds	We pose 3D scene-understanding as a problem of parsing in a grammar. A grammar helps us capture the compositional structure of real-word objects, e.g., a chair is composed of a seat, a back-rest and some legs. Having multiple rules for an object helps us capture structural variations in objects, e.g., a chair can optio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,627
2210.09228	A Model-Consistent Data-Driven Computational Strategy for PDE Joint Inversion Problems	The task of simultaneously reconstructing multiple physical coefficients in partial differential equations (PDEs) from observed data is ubiquitous in applications. In this work, we propose an integrated data-driven and model-based iterative reconstruction framework for such joint inversion problems where additional dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	324,464
2204.02549	C3KG: A Chinese Commonsense Conversation Knowledge Graph	Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps. To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	289,987
2407.07434	Aging-Resistant Wideband Precoding in 5G and Beyond Using 3D Convolutional Neural Networks	To meet the ever-increasing demand for higher data rates, 5G and 6G technologies are shifting transceivers to higher carrier frequencies, to support wider bandwidths and more antenna elements. Nevertheless, this solution poses several key challenges: i) increasing the carrier frequency and bandwidth leads to greater ch...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	471,761
2407.14262	Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning	This paper focuses on hyperparameter optimization for autonomous driving strategies based on Reinforcement Learning. We provide a detailed description of training the RL agent in a simulation environment. Subsequently, we employ Efficient Global Optimization algorithm that uses Gaussian Process fitting for hyperparamet...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	474,718
1805.08716	Reducing Disparate Exposure in Ranking: A Learning To Rank Approach	Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational placement, and even social success of those being ranked. Researche...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	98,234
1802.09405	Improving Graph Convolutional Networks with Non-Parametric Activation Functions	Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs have been proposed, they only consider simple nonlinear activation functions in th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	91,321
1807.09968	Face De-Spoofing: Anti-Spoofing via Noise Modeling	Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an indivisible unit, and process it holistically, without explicit modeling of the spoofing process. In this work, motivated by the noise mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,844
2403.18756	Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray	Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation coh...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	442,065

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