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2510.23578
Abstract and deep analysis are available in the full post.
2512.11083
As climate change continues to reshape marginal ice zones in the Arctic, accurate and reliable sea ice data are critical for ensuring maritime safety. This study compares regional ice charts from the Alaska Sea Ice Program (ASIP) with satellite-derived Copernicus sea ice concentration data to evalua
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2512.12804
I develop a novel semantics for probabilities of counterfactuals that generalizes the standard Pearlian semantics: it applies to probabilistic causal models that cannot be extended into realistic structural causal models and are therefore beyond the scope of Pearl's semantics. This generalization is
2512.12997
CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Previous work of adversarial fine-tuning largely focuses on matching the predicted logits between clean and adversarial examples, which overlooks uncertainty calibration and may degrade the zero-shot
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2512.13985
This work presents an extended formulation of the Least Squares with Virtual Displacements (LSVD) method for estimating shear strength parameters from multiple soil samples under varying resistance conditions including cohesionless, frictional, and mixed types. LSVD is designed to identify a common
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2512.14031
This study evaluates two leading approaches for teaching construction robots new skills to understand their applicability for construction automation: a Vision-Language-Action (VLA) model and Reinforcement Learning (RL) methods. The goal is to understand both task performance and the practical effor
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FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
As the large language models (LLMs) grow in size each day, efficient training and fine-tuning has never been as important as nowadays. This resulted in the great interest in parameter efficient fine-tuning (PEFT), and effective methods including low-rank adapters (LoRA) has emerged. Although the var
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Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
The simultaneous application of multiple treatments is increasingly common in many fields, such as healthcare and marketing. In such scenarios, it is important to estimate the single treatment effects and the interaction treatment effects that arise from treatment combinations. Previous studies have
Learning
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A Bayesian Approach to Learning Causal Networks
Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods oft
Learning
Artificial Intelligence
Computer Science
Network
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A Case Study of the Arbitrariness of the h-Index and the Highly-Cited-Publications Indicator
The arbitrariness of the h-index becomes evident, when one requires q*h instead of h citations as the threshold for the definition of the index, thus changing the size of the core of the most influential publications of a dataset. I analyze the citation records of 26 physicists in order to determine
Digital Libraries
Computer Science
Physics
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A Decision-Based View of Causality
Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able to predict the effects of actions. In this paper, we attempt
Artificial Intelligence
Computer Science
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A high-field 3He Metastability Exchange Optical Pumping polarizer operating in a 1.5 T medical scanner for lung MRI
After being hyperpolarized using the technique of Metastability Exchange Optical Pumping (MEOP), 3He can be used as a contrast agent for lung magnetic resonance imaging (MRI). MEOP is usually performed at low magnetic field (~ 1 mT) and low pressure (~ 1 mbar), which results in a low magnetization p
Physics
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A New Look at Causal Independence
Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effe
Artificial Intelligence
Computer Science
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A new scheme of signature extraction for iris authentication
Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, thus is the most promising for high security environment. Iris recognition is proposed in this report. We describe some methods, the first one is based on grey level histogram
Computer Vision
Computer Science
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An Efficient Detection Mechanism for Distributed Denial of Service (DDoS) Attack
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing collection particular overhaul disruptions, often for total periods of instance. The relative ease and low costs of initiation such attacks, supplemented by the present insufficient sa
Cryptography and Security
Computer Science
Detection
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Approximation Algorithms for the Loop Cutset Problem
We show how to find a small loop curser in a Bayesian network. Finding such a loop cutset is the first step in the method of conditioning for inference. Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the numbe
Artificial Intelligence
Computer Science
Data Structures
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Assessing Semantic Quality of Web Directory Structure
The administration of a Web directory content and associated structure is a labor intensive task performed by human domain experts. Because of that there always exists a realistic risk of the structure becoming unbalanced, uneven and difficult to use to all except for a few users proficient in a par
Information Retrieval
Digital Libraries
Computer Science
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Basic Classes of Grammars with Prohibition
A practical tool for natural language modeling and development of human-machine interaction is developed in the context of formal grammars and languages. A new type of formal grammars, called grammars with prohibition, is introduced. Grammars with prohibition provide more powerful tools for natural
NLP
Formal Languages
Computer Science
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Computational Complexity Reduction for BN2O Networks Using Similarity of States
Although probabilistic inference in a general Bayesian belief network is an NP-hard problem, computation time for inference can be reduced in most practical cases by exploiting domain knowledge and by making approximations in the knowledge representation. In this paper we introduce the property of s
Artificial Intelligence
Computer Science
Network
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Constructing Belief Networks to Evaluate Plans
This paper examines the problem of constructing belief networks to evaluate plans produced by an knowledge-based planner. Techniques are presented for handling various types of complicating plan features. These include plans with context-dependent consequences, indirect consequences, actions with pr
Artificial Intelligence
Computer Science
Network
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Crowdsourcing for Bioinformatics
Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base construction and protein structure determination all benefit from human input. In some cases people are needed in vast quantities while in other
Quantitative Biology
Physics
Computer Science
Computers and Society
Social Networks
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Development of Yes/No Arabic Question Answering System
Developing Question Answering systems has been one of the important research issues because it requires insights from a variety of disciplines,including,Artificial Intelligence,Information Retrieval, Information Extraction,Natural Language Processing, and Psychology.In this paper we realize a formal
Information Retrieval
System
NLP
Computer Science
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Effective System for Pregnant Women using Mobile GIS
World Health Organization showed at one year about 287 000 women died most of them during and following pregnancy and childbirth in Africa and south Asia. This paper suggests mHealth system for serving pregnant women, that proposed system is first an effective mHealth system works base on mobile GIS
System
Computer Science
Computers and Society
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Enhanced secure key exchange systems based on the Johnson-noise scheme
We introduce seven new versions of the Kirchhoff-Law-Johnson-(like)-Noise (KLJN) classical physical secure key exchange scheme and a new transient protocol for practically-perfect security. While these practical improvements offer progressively enhanced security and/or speed for the non-ideal condit
System
Cryptography and Security
Computer Science
Emerging Technologies
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Epsilon-Safe Planning
We introduce an approach to high-level conditional planning we call epsilon-safe planning. This probabilistic approach commits us to planning to meet some specified goal with a probability of success of at least 1-epsilon for some user-supplied epsilon. We describe several algorithms for epsilon-saf
Artificial Intelligence
Computer Science
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Exceptional Subclasses in Qualitative Probability
System Z+ [Goldszmidt and Pearl, 1991, Goldszmidt, 1992] is a formalism for reasoning with normality defaults of the form 'typically if phi then + (with strength cf)' where 6 is a positive integer. The system has a critical shortcoming in that it does not sanction inheritance across exceptional subc
Artificial Intelligence
Computer Science
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Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
A model to represent spatial information is presented in this paper. It is based on fuzzy constraints represented as fuzzy geometric relations that can be hierarchically structured. The concept of spatial template is introduced to capture the idea of interrelated objects in two-dimensional space. Th
Artificial Intelligence
Computer Science
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GEMINI: A Generic Multi-Modal Natural Interface Framework for Videogames
In recent years videogame companies have recognized the role of player engagement as a major factor in user experience and enjoyment. This encouraged a greater investment in new types of game controllers such as the WiiMote, Rock Band instruments and the Kinect. However, the native software of these
Framework
Computer Science
HCI
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General Belief Measures
Probability measures by themselves, are known to be inappropriate for modeling the dynamics of plain belief and their excessively strong measurability constraints make them unsuitable for some representational tasks, e.g. in the context of firstorder knowledge. In this paper, we are therefore going
Artificial Intelligence
Computer Science
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Generating New Beliefs From Old
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective (first-order, statistical, and default) information. But allowing
Artificial Intelligence
Computer Science
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How to derive an advantage from the arbitrariness of the g-index
The definition of the g-index is as arbitrary as that of the h-index, because the threshold number g^2 of citations to the g most cited papers can be modified by a prefactor at one's discretion, thus taking into account more or less of the highly cited publications within a dataset. In a case study
Digital Libraries
Computer Science
Physics
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Inhomogeneous Deformation of Brain Tissue During Tension Tests
Mechanical characterization of brain tissue has been investigated extensively by various research groups over the past fifty years. These properties are particularly important for modelling Traumatic Brain Injury (TBI). In this research, we present the design and calibration of a High Rate Tension D
Physics
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Inter-causal Independence and Heterogeneous Factorization
It is well known that conditional independence can be used to factorize a joint probability into a multiplication of conditional probabilities. This paper proposes a constructive definition of inter-causal independence, which can be used to further factorize a conditional probability. An inference a
Artificial Intelligence
Computer Science
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Model-Based Diagnosis with Qualitative Temporal Uncertainty
In this paper we describe a framework for model-based diagnosis of dynamic systems, which extends previous work in this field by using and expressing temporal uncertainty in the form of qualitative interval relations a la Allen. Based on a logical framework extended by qualitative and quantitative t
Artificial Intelligence
Computer Science
Model
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Models of Consensus for Multiple Agent Systems
Models of consensus are used to manage multiple agent systems in order to choose between different recommendations provided by the system. It is assumed that there is a central agent that solicits recommendations or plans from other agents. That agent the n determines the consensus of the other agen
Multiagent Systems
Model
Computer Science
Systems and Control
System
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New Dimension Value Introduction for In-Memory What-If Analysis
OLAP systems operate on historical data and provide answers to analysts queries. Recent in-memory implementations provide significant performance improvement for real time ad-hoc analysis. Philosophy and techniques of what-if analysis on data warehouse and in-memory data store based OLAP systems hav
Databases
Computer Science
Analysis
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On Axiomatization of Probabilistic Conditional Independencies
This paper studies the connection between probabilistic conditional independence in uncertain reasoning and data dependency in relational databases. As a demonstration of the usefulness of this preliminary investigation, an alternate proof is presented for refuting the conjecture suggested by Pearl
Artificial Intelligence
Computer Science
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On Testing Whether an Embedded Bayesian Network Represents a Probability Model
Testing the validity of probabilistic models containing unmeasured (hidden) variables is shown to be a hard task. We show that the task of testing whether models are structurally incompatible with the data at hand, requires an exponential number of independence evaluations, each of the form: 'X is c
Artificial Intelligence
Computer Science
Model
Network
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Optimal Factory Scheduling using Stochastic Dominance A*
We examine a standard factory scheduling problem with stochastic processing and setup times, minimizing the expectation of the weighted number of tardy jobs. Because the costs of operators in the schedule are stochastic and sequence dependent, standard dynamic programming algorithms such as A* may f
Artificial Intelligence
Computer Science
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Parametrized KAM Theorem for Differentiable Hamiltonian Vector Fields without Action-Angle Variables
We proved a parametrized KAM theorem in Hamiltonian system which has differentiable Hamiltonian without action-angle coordinates. It is a generalization of the result of [Llave et al. 2005] that deals with real analytic Hamiltonians.
Mathematics
Nonlinear Sciences
MATH-PH
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Path Planning under Time-Dependent Uncertainty
Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these depend
Artificial Intelligence
Computer Science
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Pawel Max Maksym (1983-2013) Polish Astronomer and Film-maker
The sudden and untimely death of Pawel Max Maksym will not diminish his contributions to the field of occultation astronomy, and to Polish society in general. Founder of the Pope Silvester II Observatory in Bukowiec, he was also writing a book to introduce children to astronomy. Graduating in Geogra
Astrophysics
Physics
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Perangkat lunak bantu mengenal huruf arab melayu ke bentuk huruf latin bahasa Indonesia
The development of computer science has contributed greatly for increasing of efficiency and effectively. Many areas are covered by computer science, included education. The purpose of this research is to introduce jawi a type of Indonesian letters. Jawis letter is one of the most popular letter in
Other CS
Computer Science
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Plausibility Measures: A Users Guide
We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probab
Artificial Intelligence
Computer Science
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Probability Update: Conditioning vs. Cross-Entropy
Conditioning is the generally agreed-upon method for updating probability distributions when one learns that an event is certainly true. But it has been argued that we need other rules, in particular the rule of cross-entropy minimization, to handle updates that involve uncertain information. In thi
Artificial Intelligence
Computer Science
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Quasi-Bayesian Strategies for Efficient Plan Generation: Application to the Planning to Observe Problem
Quasi-Bayesian theory uses convex sets of probability distributions and expected loss to represent preferences about plans. The theory focuses on decision robustness, i.e., the extent to which plans are affected by deviations in subjective assessments of probability. The present work presents soluti
Artificial Intelligence
Computer Science
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Role of genetic polymorphisms in transgenerational inheritance in budding yeast
Transgenerational inheritance of a trait is presumably affected by both genetic and environmental factors but remains poorly understood. We studied the effect of genetic polymorphisms on transgenerational inheritance of yeast segregants that were derived from a cross between a laboratory strain and
Quantitative Biology
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Simulations and experiments of short intense envelope solitons of surface water waves
The problem of existence of stable nonlinear groups of gravity waves in deep water is revised by means of laboratory and numerical simulations with the focus on intense waves. Wave groups with steepness up to $A_{cr} omega_m^2 /g approx 0.30$ are reproduced in laboratory experiments ($A_{cr}$ is the
Nonlinear Sciences
Physics
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Some Experiments with Real-Time Decision Algorithms
Real-time Decision algorithms are a class of incremental resource-bounded [Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence diagrams. We present a test domain for real-time decision algorithms, and the results of experiments with several Real-time Decision Algorithms in this do
Artificial Intelligence
Computer Science
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Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), whi
Artificial Intelligence
Computer Science
Network
< Category Statistics (Total: 5013) >
Astrophysics
41
Computer Science
1829
Condensed Matter
51
Economics
3
Electrical Engineering and Systems Science
1
General Relativity
4
General Research
37
HEP-EX
4
HEP-PH
2
HEP-TH
4
MATH-PH
12
Mathematics
88
NUCL-EX
7
NUCL-TH
1
Nonlinear Sciences
25
Physics
385
Quantitative Biology
157
Quantitative Finance
72
Quantum Physics
11
Research
2662
Statistics
45