The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of BLE (Bluetooth Low Energy) and similar technologies enabling context-awar
From the relative scarcity of training data to the lack of standardized benchmarks, the development of foundation models for polymers face significant and multi-faceted challenges. At the core, many of these issues are tied directly to the structural
A novel approach to evaluation of hardware and software testability, represented in the form of register transfer graph, is proposed. Instances of making of software graph models for their subsequent testing and diagnosis are shown.
Multimodal LLMs often produce fluent yet unreliable reasoning, exhibiting weak step-to-step coherence and insufficient visual grounding, largely because existing alignment approaches supervise only the final answer while ignoring the reliability of t
We investigate the stochastic homogenization of a class of turbulent diffusions generated by non-local symmetric Lévy operators with divergence-free drift fields in ergodic random environments, where neither the drift fields nor their associated stre
The Lorenz equations [1] are a severe Galerkin-truncation of the Oberbeck-Boussinesq (OB) equations describing Rayleigh-Bénard convection (RBC). Here we examine the mathematical connections between the chaotic lobe-switching behavior of a stochastic
LLM-based automatic survey systems are transforming how users acquire information from the web by integrating retrieval, organization, and content synthesis into end-to-end generation pipelines. While recent works focus on developing new generation p
We introduce collaborative tagging and faceted search on structured P2P systems. Since a trivial and brute force mapping of an entire folksonomy over a DHT-based system may reduce scalability, we propose an approximated graph maintenance approach. Ev
In the context of security protocol parallel composition, where messages belonging to different protocols can intersect each other, we introduce a new paradigm: term-based composition (i.e. the composition of message components also known as terms).
The Pi GHz Sky Survey (PiGSS) is a key project of the Allen Telescope Array. PiGSS is a 3.1 GHz survey of radio continuum emission in the extragalactic sky with an emphasis on synoptic observations that measure the static and time-variable properties
The Late Heavy Bombardment (LHB) period is the narrow time interval between 3.8 and 3.9 Gyr ago, where the bulk of the craters we see on the Moon formed. Even more craters formed on the Earth. During a field expedition to the 3.8 Gyr old Isua green
In wireless ad-hoc broadcast networks the pairing problem consists of establishing a (long-term) connection between two specific physical nodes in the network that do not yet know each other. We focus on the ephemeral version of this problem. Ephemer
The supersymmetric SO(10) GUT based on the {bf{210oplus 10oplus 120oplus 126oplus {bar {126}}}} Higgs system provides a minimal framework for the emergence of the R-parity exact MSSM at low energies and a viable supersymmetric seesaw explanation fo
(abridged) We study the validity of the approximation of a Gaussian cosmic shear likelihood. We estimate the true likelihood for a fiducial cosmological model from a large set of ray-tracing simulations and investigate the impact of non-Gaussianity o
We propose that unconstrained artificial intelligence obeys a Second Law analogous to thermodynamics, where ethical entropy, defined as a measure of divergence from intended goals, increases spontaneously without continuous alignment work. For gradie
Diffusion models have established the state-of-the-art in text-to-image generation, but their performance often relies on a diffusion prior network to translate text embeddings into the visual manifold for easier decoding. These priors are computatio
We present the results of a series of radiation-MHD simulations of a local patch of an accretion disk, with fixed vertical gravity profile but with different surface mass densities and a broad range of radiation to gas pressure ratios. Each simulatio
As the focus of applied research in topological insulators (TI) evolves, the need to synthesize large-area TI films for practical device applications takes center stage. However, constructing scalable and adaptable processes for high-quality TI compo
Although scaling laws and many empirical results suggest that increasing the size of Vision Transformers often improves performance, model accuracy and training behavior are not always monotonically increasing with scale. Focusing on ViT-B/16 trained
Theoretical and experimental developments in the 1920s that accompanied the birth of coincidence methods, as well as later crucial applications during the 1930s and 1940s are presented. In 1924 Walther Bothe and Hans Geiger applied a coincidence meth
Code & Model: https://github.com/fudan-generative-vision/WAM-Flow Figure 1 . WAM-Flow enables flexible slow-fast and coarse-to-fine trajectory prediction. For straightforward driving scenarios, 1-step denoising achieves competitive performance (89.1
In this paper, we characterize the wave front sets of solutions to fractional Schrödinger equations (ipartial_{t}u =(-Δ)^{θ/2}u + V(x)u) with 0<θ<2 via the wave packet transform (short-time Fourier transform). We clarify the relationship between
Large multi-modal models (LMMs) show increasing performance in realistic visual tasks for images and, more recently, for videos. For example, given a video sequence, such models are able to describe in detail objects, the surroundings and dynamic act
We introduce and analyze a nonlocal generalization of Whittle--Matérn Gaussian fields in which the smoothness parameter varies in space through the fractional order, s=s(x)in[underline{s},,bar{s}]subset(0,1) . The model is defined via an integral-fo
Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, cor
We report an observation of X-ray emission from the exciting region of Cepheus A with the Chandra/ACIS instrument. What had been an unresolved X-ray source comprising the putative power sources is now resolved into at least 3 point-like sources, each
This work proposes a unified control architecture that couples a Reinforcement Learning (RL)-driven controller with a disturbance-rejection Extended State Observer (ESO), complemented by an Event-Triggered Mechanism (ETM) to limit unnecessary computa
In recent years, Visual Anomaly Detection (VAD) has gained significant attention due to its ability to identify anomalous images using only normal images during training. Many VAD models work without supervision but are still able to provide visual e
Validation gating is a fundamental component of classical Kalman-based tracking systems. Only measurements whose normalized innovation squared (NIS) falls below a prescribed threshold are considered for state update. While this procedure is statistic
A persistent paradox in continual learning (CL) is that neural networks often retain linearly separable representations of past tasks even when their output predictions fail. We formalize this distinction as the gap between deep (feature-space) and s
Large Language Models (LLMs) are increasingly deployed as automated tutors to address educator shortages; however, they often fail at pedagogical reasoning, frequently validating incorrect student solutions (sycophancy) or providing overly direct ans
Diabetic retinopathy (DR) grading plays a critical role in early clinical intervention and vision preservation. Recent explorations predominantly focus on visual lesion feature extraction through data processing and domain decoupling strategies. Howe
Clustered Federated Learning (CFL) has emerged as a powerful approach for addressing data heterogeneity and ensuring privacy in large distributed IoT environments. By clustering clients and training cluster-specific models, CFL enables personalized m
Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even after sophisticated data curation. A natural approach to better leverage high-quality data is curriculum-ba
This paper presents a novel post-processing methodology for extracting high-quality geometries from density-based topology optimization results. Current post-processing approaches often struggle to simultaneously achieve smooth boundaries, preserve v
Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing signals such as images, audio, and 3D scenes. However, existing INR frameworks-including MLPs with Fourier features, SIREN, and multiresolution hash grids-imp
Figure 1: SPACECONTROL enables spatially controlled 3D asset generation from simple geometric primitives such as superquadrics (light blue) and other geometry types such as polygon meshes. Top: rapid asset generation. From quick 3D sketches and brief
Imitation learning with diffusion models has advanced robotic control by capturing the multimodal action distributions. However, existing methods typically treat observations only as highlevel conditions to the denoising network, rather than integrat
Figure 1 : Comparison of 3DGS, 2DGS, and our EGGS. While 3DGS achieves high-fidelity appearance, it often produces inaccurate geometry, with imprecise surfaces and blurred edges. 2DGS improves geometric consistency across views but suffers from redu
Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural language descriptions that are often ambiguous, redundant, and unstructured
Sphere packing, Hilbert's eighteenth problem, asks for the densest arrangement of congruent spheres in n-dimensional Euclidean space. Although relevant to areas such as cryptography, crystallography, and medical imaging, the problem remains unresolve
Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have recen
In light of recent hardware advances, it is striking that real arithmetic in balanced ternary logic has received almost no attention in the literature. This is particularly surprising given ternary logic's promising properties, which could open new a
Schema matching is a critical task in data integration, particularly in the medical domain where disparate Electronic Health Record (EHR) systems must be aligned to standard models like OMOP CDM. While Large Language Models (LLMs) have shown promise
Controller synthesis is a formal method approach for automatically generating Labeled Transition System (LTS) controllers that satisfy specified properties. The efficiency of the synthesis process, however, is critically dependent on exploration poli
We investigate machine learning approaches to approximating the emph{domination number} of graphs, the minimum size of a dominating set. Exact computation of this parameter is NP-hard, restricting classical methods to small instances. We compare two
In this work, we extend and generalize our solving strategy, first introduced in [1], based on a greedy optimization algorithm and the alternating direction method (ADM) for nonlinear systems computed with multiple load steps. In particular, we combi
Climate-driven reductions in Arctic sea-ice extent have renewed interest in trans-Arctic shipping, yet adoption remains limited by questions of route feasibility, safety, and excess distance. Existing work often compares idealised great-circle shortc
Large Language Models (LLMs) are increasingly deployed in high-stakes financial domains, yet they suffer from specific, reproducible hallucinations when performing arithmetic operations. Current mitigation strategies often treat the model as a black
Real-world enterprise data intelligence workflows encompass data engineering that turns raw sources into analytical-ready tables and data analysis that convert those tables into decisionoriented insights. We introduce DAComp, a benchmark of 210 tasks
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