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We propose to measure the beam normal single spin asymmetry in elastic scattering of transversely polarized electron from target nuclei with 12 $\leq Z \leq$ 90 at Q$^2$ = 0.0092 GeV$^2$ to study its nuclear dependence. While the theoretical calculations based on two-photon exchange suggest no nuclear dependence at this kinematics, the results of 208Pb from Jefferson Lab show a striking disagreement from both theoretical predictions and light nuclei measurements. The proposed measurements will provide new data for intermediate to heavy nuclei where no data exists for $Z \geq$ 20 in the kinematics of previous high-energy experiments. It will allow one to investigate the missing contributions that are not accounted in the current theoretical models., Comment: Submitted to Jefferson Lab PAC52

As quantum computing continues to advance, its ability to compromise widely used cryptographic systems projects a significant challenge to modern cybersecurity. This paper outlines a strategic roadmap for industries to anticipate and mitigate the risks posed by quantum attacks. Our study explores the development of a quantum-resistant cryptographic solutioning framework for the industry, offering a practical and strategic approach to mitigating quantum attacks. We, here, propose a novel strategic framework, coined name STL-QCRYPTO, outlines tailored, industry-specific methodologies to implement quantum-safe security systems, ensuring long-term protection against the disruptive potential of quantum computing. The following fourteen high-risk sectors: Financial Services, Banking, Healthcare, Critical Infrastructure, Government & Defence, E-commerce, Energy & Utilities, Automotive & Transportation, Cloud Computing & Data Storage, Insurance, Internet & Telecommunications, Blockchain Applications, Metaverse Applications, and Multiagent AI Systems - are critically assessed for their vulnerability to quantum threats. The evaluation emphasizes practical approaches for the deployment of quantum-safe security systems to safeguard these industries against emerging quantum-enabled cyber risks. Additionally, the paper addresses the technical, operational, and regulatory hurdles associated with adopting quantum-resistant technologies. By presenting a structured timeline and actionable recommendations, this roadmap with proposed framework prepares industries with the essential strategy to safeguard their potential security threats in the quantum computing era.

Each year, the production of synthetic dye wastewater reaches a trillion tons, posing a significant challenge to addressing water scarcity on a global level. Hence, the treatment of wastewater to prevent water scarcity is of prime importance, and failing to do so will increase ecotoxicological risks and human health. Textile wastewater contains harmful dye. Photocatalytic degradation of such dye-contaminated wastewater is crucial to purifying the dye-contaminated water. However, this process takes time, uses high-power lamps, and is expensive. Here, we report the effect of the concentration of precursor on the size and surface morphology of TiO2 nanostructures prepared by facile hydrothermal synthesis and its ability to perform as a photocatalyst to degrade the most common industrial textile dye, methylene blue (MB), under natural sunlight. The impact of particle size on the photocatalytic activity and photocarrier migration rate was thoroughly examined. Also, the effect of pH on adsorption and photocatalytic degradation has been evaluated in detail. With several optimized conditions, almost complete dye degradation was achieved within 40 minutes under the direct illumination of natural sunlight. The enhanced photocatalytic performance can be correlated to the synergetic effect of a higher charge transfer mechanism, good catalytic active surface area availability (386 m2/g), and several optimized parameters that affect the reaction efficacy. Additionally, repeated use of NPs without sacrificing performance five times confirmed its stability and Sustainability as a promising candidate for large-scale industrial textile wastewater remedies.

AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, and cosmology. Trained on the complete collection of astronomy-related arXiv papers from 2007-2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates remarkable proficiency on a wide range of questions. AstroSage-Llama-3.1-8B scores 80.9% on the AstroMLab-1 benchmark, greatly outperforming all models -- proprietary and open-weight -- in the 8-billion parameter class, and performing on par with GPT-4o. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. AstroSage-Llama-3.1-8B is freely available, enabling widespread access to advanced AI capabilities for astronomical education and research.

Large language models (LLMs) are known to exhibit demographic biases, yet few studies systematically evaluate these biases across multiple datasets or account for confounding factors. In this work, we examine LLM alignment with human annotations in five offensive language datasets, comprising approximately 220K annotations. Our findings reveal that while demographic traits, particularly race, influence alignment, these effects are inconsistent across datasets and often entangled with other factors. Confounders -- such as document difficulty, annotator sensitivity, and within-group agreement -- account for more variation in alignment patterns than demographic traits alone. Specifically, alignment increases with higher annotator sensitivity and group agreement, while greater document difficulty corresponds to reduced alignment. Our results underscore the importance of multi-dataset analyses and confounder-aware methodologies in developing robust measures of demographic bias in LLMs., Comment: 18 pages, 8 figures, ACL'25

In this paper, we consider the interior structure of a multi-band holographic superconductor model. We focus on the holographic superconductor system with two scalar fields which correspond to two s-wave order parameters in the dual condensed matter system. With two s-wave order parameters, the boundary system has more interesting behaviors which can also be reflected in black hole interior structure. We find the Einstein-Rosen bridge collapse and Josephson oscillations of two scalar fields inside the horizon. For the region near the singularity, we find that the metric still presents Kasner form and there is also Kasner transition behavior. However, when two scalar fields coexist, the Kasner exponents and Kasner transition formula will be different from the single scalar field case. The different interior structures between multi-band holographic superconductor and single-band holographic superconductor we find in this work further confirms that the black hole interior is important to reflect the properties of dual condensed matter systems., Comment: v1:20 pages, 12 figures, 1 table. Comments are welcome. v2: typos corrected, reference added, Fig.11 updated

We compute the supersymmetric index of half BPS black holes in N=2 supergravity with higher curvature corrections and show that the result agrees with the degeneracy of supersymmetric extremal black holes carrying the same charges. Both sides of the computation are done gravitationally., Comment: 27 pages

The image of a Kerr-Newman (KN) black hole (BH) surrounded by a thin accretion disk is derived. By employing elliptic integrals and ray-tracing methods, we analyze photon trajectories around the KN BH. At low observation inclination angles, the secondary image of particles is embedded within the primary image. However, as the inclination increases, the primary and secondary images separate, forming a hat-like structure. The spin and charge of the BH, along with the observer's inclination angle, affect the image's asymmetry and the distortion of the inner shadow. To investigate the redshift distribution on the accretion disk, we extended the inner boundary of the accretion disk to the event horizon. The results show that the redshift distribution is significantly influenced by the observation inclination angle. Furthermore, we conducted a detailed analysis of the KN BH image using fisheye camera ray-tracing techniques and found that the optical appearance and intensity distribution of the BH vary at different observation frequencies (specifically at 230GHz and 86GHz). We also examined differences in intensity distribution for prograde and retrograde accretion disk scenarios. Comparing observational at the two frequencies, we found that both the total intensity and peak intensity at 86GHz are higher than those at 230GHz., Comment: 26 pages, 21 figures

Significant advances have been made in natural language processing in recent years. However, our current deep learning approach to language modeling requires substantial resources in terms of data and computation. One of the side effects of this data-hungry paradigm is the current schism between languages, separating those considered high-resource, where most of the development happens and resources are available, and the low-resource ones, which struggle to attain the same level of performance and autonomy. This study aims to introduce a new set of resources to stimulate the future development of neural text generation in Portuguese. In this work, we document the development of GigaVerbo, a concatenation of deduplicated Portuguese text corpora amounting to 200 billion tokens. Via this corpus, we trained a series of decoder-transformers named Tucano. Our models perform equal or superior to other Portuguese and multilingual language models of similar size in several Portuguese benchmarks. The evaluation of our models also reveals that model performance on many currently available benchmarks used by the Portuguese NLP community has little to no correlation with the scaling of token ingestion during training, highlighting the limitations of such evaluations when it comes to the assessment of Portuguese generative language models. All derivatives of our study are openly released on GitHub and Hugging Face. See https://nkluge-correa.github.io/Tucano/

Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers and text aligned to timestamps, and includes data from two different domains: climate science and healthcare. Our data is a significant contribution to the rare selection of available multimodal datasets. We also propose the Hybrid Multi-Modal Forecaster (Hybrid-MMF), a multimodal LLM that jointly forecasts both text and time series data using shared embeddings. However, contrary to our expectations, our Hybrid-MMF model does not outperform existing baselines in our experiments. This negative result highlights the challenges inherent in multimodal forecasting. Our code and data are available at https://github.com/Rose-STL-Lab/Multimodal_ Forecasting., Comment: 21 pages, 4 tables, 2 figures

As Large Language Models (LLMs) continue to grow, reducing costs and alleviating GPU demands has become increasingly critical. However, existing schedulers primarily target either GPU compute or Key-Value Cache (KVC) utilization, failing to fully optimize both GPU compute and KVC usage during each iteration or guarantee timely KVC allocations when needed. To address these challenges, we conducted a trace-based experimental analysis and made insightful observations, leading to the design of a system called EcoServe. EcoServe maximizes multi-resource utilization while ensuring service-level objective (SLO) guarantees in LLM serving. To enable adding prompts to a batch to maximize GPU utilization in each iteration, EcoServe maintains separate waiting queues for prompt processing tasks (PTs) and generation tasks (GTs). It batches GTs with the same predicted response lengths (RL) to save scheduling time and allocates KVC space for the predicted RL to avoid KVC allocation failures. It further has a novel KVC pipelining method, allowing sharing allocated but unused KVC space to enhance KVC utilization. In addition, it prioritizes queued requests that occupy more KVC to release KVC earlier and satisfy request service-level-objective (SLO). Experimental results demonstrate that EcoServe increases throughput by up to 4$\times$ with the same level of latency, generates up to 91\% lower job completion time and up to 91\% higher SLO satisfaction ratio compared to vLLM. It also reduces the number of GPUs used in DistServe by up to 78\% while maintaining the same level of goodput., Comment: 14 pages

Electroencephalogram (EEG)-based emotion decoding can objectively quantify people's emotional state and has broad application prospects in human-computer interaction and early detection of emotional disorders. Recently emerging deep learning architectures have significantly improved the performance of EEG emotion decoding. However, existing methods still fall short of fully capturing the complex spatiotemporal dynamics of neural signals, which are crucial for representing emotion processing. This study proposes a Dynamic-Attention-based EEG State Transition (DAEST) modeling method to characterize EEG spatiotemporal dynamics. The model extracts spatiotemporal components of EEG that represent multiple parallel neural processes and estimates dynamic attention weights on these components to capture transitions in brain states. The model is optimized within a contrastive learning framework for cross-subject emotion recognition. The proposed method achieved state-of-the-art performance on three publicly available datasets: FACED, SEED, and SEED-V. It achieved 75.4% accuracy in the binary classification of positive and negative emotions and 59.3% in nine-class discrete emotion classification on the FACED dataset, 88.1% in the three-class classification of positive, negative, and neutral emotions on the SEED dataset, and 73.6% in five-class discrete emotion classification on the SEED-V dataset. The learned EEG spatiotemporal patterns and dynamic transition properties offer valuable insights into neural dynamics underlying emotion processing., Comment: 14 pages, 6 figures

Quantum materials governed by emergent topological fermions have become a cornerstone of physics. Dirac fermions in graphene form the basis for moir\'e quantum matter, and Dirac fermions in magnetic topological insulators enabled the discovery of the quantum anomalous Hall effect. In contrast, there are few materials whose electromagnetic response is dominated by emergent Weyl fermions. Nearly all known Weyl materials are overwhelmingly metallic, and are largely governed by irrelevant, conventional electrons. Here we theoretically predict and experimentally observe a semimetallic Weyl ferromagnet in van der Waals (Cr,Bi)$_2$Te$_3$. In transport, we find a record bulk anomalous Hall angle $> 0.5$ along with non-metallic conductivity, a regime sharply distinct from conventional ferromagnets. Together with symmetry analysis, our data suggest a semimetallic Fermi surface composed of two Weyl points, with a giant separation $> 75\%$ of the linear dimension of the bulk Brillouin zone, and no other electronic states. Using state-of-the-art crystal synthesis techniques, we widely tune the electronic structure, allowing us to annihilate the Weyl state and visualize a unique topological phase diagram exhibiting broad Chern insulating, Weyl semimetallic and magnetic semiconducting regions. Our observation of a semimetallic Weyl ferromagnet offers an avenue toward novel correlated states and non-linear phenomena, as well as zero-magnetic-field Weyl spintronic and optical devices., Comment: Nature, in press

In this work, we study the evaporation behaviors of asymptotically flat charged black holes in the Einstein-Horndeski gravity theory. Based on the thermodynamics of the Horndeski black hole, we present a physical understanding of the scalar charge of the Horndeski black hole and also clarify its connection to the Einstein vector theory. As the presence of non-minimal coupling, the evaporating behaviors of the Horndeski black hole are vastly different from the Reissner-Nordstrom (RN) black hole case. Due to the different spacetime and electric field structures, the evaporation rate of the Horndeski black hole will slow down at the late stage of evaporation and thus gain a lifetime much longer than the RN black hole. These results illuminate the effect of non-minimally coupled matters on the black hole evaporation and provide clues to search for these matter fields in future observations., Comment: 8 pages 3 figures

In \href{https://link.aps.org/doi/10.1103/PhysRevD.92.103004}{Phys. Rev. D 92 (2015) 103004}, simple analytical solutions of the Friedman equations were obtained for a universe having stiff matter component in the early universe together with a dark matter, and a dark energy component. In this analysis, the universe is considered to be made of a dark fluid which behaves as a stiff matter in the early phase of the universe (when the internal energy dominates). It is also more logical to consider quantum gravitational effects in the early phase of the cosmological evolution. In this analysis, following \href{https://link.aps.org/doi/10.1103/PhysRevD.65.043508}{Phys. Rev. D 65 (2002) 043508}, we consider renormalization group improved modified Friedmann equations where the Newton's gravitational constant ($G$) and the cosmological constant ($\Lambda$) flows with the momentum scale $k$ of the universe. It is observed that for a universe undergoing a stiff matter era, radiation era, and matter era, inflation is absent in the early time regime of the universe when the flow of the Newton's gravitational constant and cosmological constant is under consideration. Using the identification of the momentum scale with the scale factor of the universe, we then explore the era $t>t_{\text{Pl}}$ which indicates a primarily matter dominated era with accelerated expansion due to the presence of dark energy. Finally, considering the total equation of state as a combination of linear equation of state along with a polytropic equation of state, we observe that after the Planck-time the universe can undergo an inflationary phase and we find out that the inflation is enhanced by quantum gravitational effects arising due to the consideration of renormalization group approach to quantum gravity., Comment: 17 pages LATEX, comments are welcome. OTM

The present study numerically investigates two-dimensional mixed convective flow of air past a square cylinder placed at an angle of incidence of $\alpha = 45^{\circ}$ to the free-stream. We perform direct numerical simulations (DNS) for a Reynolds number (Re) of 100 and a range of Richardson numbers (Ri) between 0.0 and 1.0 and a Prandtl number (Pr) of 0.7. The critical Richardson number at which the near-field becomes a steady flow from an unsteady one, using Stuart-Landau analysis, is found to be Ri $=0.68$, and simultaneously, the far-field unsteadiness emerges. There is no range of Ri for which the entire flow field is seen to be steady. At a relatively moderate Ri, the flow field reveals the presence of vorticity inversion through the momentum deficit/addition in the downstream region. We discuss the dual wake-plume nature of the flow beyond the cylinder. The wake exhibits characteristics similar to those of a buoyant jet in the far-field at increased buoyancy. We explore the cause of the far-field unsteadiness, and discuss the mechanism of the observed flow physics using instantaneous and time-averaged flow fields. The important flow quantities, such as force coefficients, vortex shedding frequency, and Nusselt number, are discussed at various Richardson numbers.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic segments, known as Couinaud segmentation, is challenging due to indistinct sub-region boundaries and the need for extensive annotated datasets. This study introduces LiverFormer, a novel Couinaud segmentation model that effectively integrates global context with low-level local features based on a 3D hybrid CNN-Transformer architecture. Additionally, a registration-based data augmentation strategy is equipped to enhance the segmentation performance with limited labeled data. Evaluated on CT images from 123 patients, LiverFormer demonstrated high accuracy and strong concordance with expert annotations across various metrics, allowing for enhanced treatment planning for surgery and radiation therapy. It has great potential to reduces complications and minimizes potential damages to surrounding tissue, leading to improved outcomes for patients undergoing complex liver cancer treatments.

Implementing and tuning chirality is fundamental in physics, chemistry, and material science. Chiral charge density waves (CDWs), where chirality arises from correlated charge orders, are attracting intense interest due to their exotic transport and optical properties. However, a general framework for predicting chiral CDW materials is lacking, primarily because the underlying mechanisms remain elusive. Here, we address this challenge by developing the first comprehensive predictive framework, systematically identifying chiral CDW materials via first-principles calculations. The key lies in the previously overlooked phase difference of the CDW Q-vectors between layers, which is linked to opposite collective atomic displacements across different layers. This phase difference induces a spiral arrangement of the Q-vectors, ultimately giving rise to a chiral structure in real space. We validate our framework by applying it to the kagome lattice AV$_{3}$Sb$_{5}$ (A = K, Rb, Cs), successfully predicting emergent structural chirality. To demonstrate the generality of our approach, we extend it to predict chiral CDWs in the triangular-lattice NbSe$_{2}$. Beyond material predictions, our theory uncovers a universal and unprecedented Hall effect in chiral CDW materials, occurring without external magnetic fields or intrinsic magnetization. Our experiments on CsV$_{3}$Sb$_{5}$ confirm this prediction, observing a unique signature where the Hall conductivity's sign reverses when the input current is reversed, a phenomenon distinct from known Hall effects. Our findings elucidate the mechanisms behind chiral CDWs and open new avenues for discovering materials with unconventional quantum properties, with potential applications in next-generation electronic and spintronic devices.

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. To address this gap, we propose a method which advances the literature of continuous semantic mapping from voxels to ellipsoids, combining the precise structure of 3D-GS with the ability to quantify uncertainty of probabilistic robotic maps. Given a set of images, our algorithm performs a probabilistic semantic update directly on the 3D ellipsoids to obtain an expectation and variance through the use of conjugate priors. We also propose a probabilistic rasterization which returns per-pixel segmentation predictions with quantifiable uncertainty. We compare our method with similar probabilistic voxel-based methods to verify our extension to 3D ellipsoids, and perform ablation studies on uncertainty quantification and temporal smoothing.

Evaluating Large Language Models (LLMs) in low-resource and linguistically diverse languages remains a significant challenge in NLP, particularly for languages using non-Latin scripts like those spoken in India. Existing benchmarks predominantly focus on English, leaving substantial gaps in assessing LLM capabilities in these languages. We introduce MILU, a Multi task Indic Language Understanding Benchmark, a comprehensive evaluation benchmark designed to address this gap. MILU spans 8 domains and 42 subjects across 11 Indic languages, reflecting both general and culturally specific knowledge. With an India-centric design, incorporates material from regional and state-level examinations, covering topics such as local history, arts, festivals, and laws, alongside standard subjects like science and mathematics. We evaluate over 45 LLMs, and find that current LLMs struggle with MILU, with GPT-4o achieving the highest average accuracy at 72 percent. Open multilingual models outperform language-specific fine-tuned models, which perform only slightly better than random baselines. Models also perform better in high resource languages as compared to low resource ones. Domain-wise analysis indicates that models perform poorly in culturally relevant areas like Arts and Humanities, Law and Governance compared to general fields like STEM. To the best of our knowledge, MILU is the first of its kind benchmark focused on Indic languages, serving as a crucial step towards comprehensive cultural evaluation. All code, benchmarks, and artifacts are publicly available to foster open research.

We demonstrate that the kinematically-constrained model of Rydberg atoms on a two-leg ladder with staggered detuning, $\Delta \in [0,1]$, has quantum many-body scars (QMBS) in its spectrum and represents a non-perturbative generalization of the paradigmatic PXP model defined on a chain. We show that these QMBS result in coherent many-body revivals and site-dependent magnetization dynamics for both N\'eel and Rydberg vacuum initial states around $\Delta=1$. The latter feature leads to eigenstate thermalization hypothesis (ETH)-violating finite imbalance at long times in a disorder free system. This is further demonstrated by constructing appropriate local imbalance operators that display finite long-time averages for N\'eel and vacuum initial states. We also study the fidelity and Shannon entropy for such dynamics which, along with the presence of long-time finite imbalance, brings out the qualitatively different nature of QMBS in PXP ladders with $\Delta \sim 1$ from those in the PXP chain., Comment: v1; 4+$\epsilon$ pages; 3 figs; Supplementary Materials attached

Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite making significant headway in this context -- have only heightened such challenges as they rely on larger models and heavier attention mechanisms, resulting in slower inference speeds. In this paper, we introduce a training-free method to accelerate video DiTs, termed Adaptive Caching (AdaCache), which is motivated by the fact that "not all videos are created equal": meaning, some videos require fewer denoising steps to attain a reasonable quality than others. Building on this, we not only cache computations through the diffusion process, but also devise a caching schedule tailored to each video generation, maximizing the quality-latency trade-off. We further introduce a Motion Regularization (MoReg) scheme to utilize video information within AdaCache, essentially controlling the compute allocation based on motion content. Altogether, our plug-and-play contributions grant significant inference speedups (e.g. up to 4.7x on Open-Sora 720p - 2s video generation) without sacrificing the generation quality, across multiple video DiT baselines., Comment: Project-page is available at https://adacache-dit.github.io

The strongly polarizing political discourse in the U. S. implies that a small minority of the population, determining the outcome of the presidential elections in a few so called battleground-states, also determines the outcome of the overall election. Given the almost equal distributions of the electoral college members in the so-called blue and red states, the members elected from these battleground states would determine the election results. We build a kinetic exchange opinion model that takes into account the dynamical nature of the opinions of the individuals in the battleground states and the already determined core voters of the non-battleground states. In a fully connected graph, we consider the interaction among the population in the battleground states while the agents in the non-battleground states are assumed to have fixed opinions. We provide the analytical results and numerical simulations using realistic parameters from the opinion poll of the previous election's data. Counter-intuitively, a more noisy environment predicts a higher chance of the Democrats' win., Comment: 9 pages, 4 figures

Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. Researchers and developers collaboratively explore how to leverage the powerful problem-solving ability of LLMs for specific domain tasks. Due to the wide usage of LLM-based applications, e.g., ChatGPT, multiple works have been proposed to ensure the security of LLM systems. However, a comprehensive understanding of the entire processes of LLM system construction (the LLM supply chain) is crucial but relevant works are limited. More importantly, the security issues hidden in the LLM SC which could highly impact the reliable usage of LLMs are lack of exploration. Existing works mainly focus on assuring the quality of LLM from the model level, security assurance for the entire LLM SC is ignored. In this work, we take the first step to discuss the potential security risks in each component as well as the integration between components of LLM SC. We summarize 12 security-related risks and provide promising guidance to help build safer LLM systems. We hope our work can facilitate the evolution of artificial general intelligence with secure LLM ecosystems.

In this paper we present a conditional KRnet (cKRnet) based domain decomposed uncertainty quantification (CKR-DDUQ) approach to propagate uncertainties across different physical domains in models governed by partial differential equations (PDEs) with random inputs. This approach is based on the domain decomposed uncertainty quantification (DDUQ) method presented in [Q. Liao and K. Willcox, SIAM J. Sci. Comput., 37 (2015), pp. A103--A133], which suffers a bottleneck of density estimation for local joint input distributions in practice. In this work, we reformulate the required joint distributions as conditional distributions, and propose a new conditional normalizing flow model, called cKRnet, to efficiently estimate the conditional probability density functions. We present the general framework of CKR-DDUQ, conduct its convergence analysis, validate its accuracy and demonstrate its efficiency with numerical experiments.

The structural and magnetic effects of non-magnetic vanadium (V) doping in helimagnetic SrFeO$_{3-\delta}$ (SFO) are investigated, focusing on up to 3% substitution at the Fe site. Structural analysis from X-ray diffraction (XRD) and Raman spectroscopy, supported by phonon mode calculations, reveals that pure SFO exists as a mixed tetragonal-orthorhombic phase, while V-doped samples exhibit an emerging cubic phase alongside tetragonal symmetry. Magnetic hysteresis (M-H) loops show notable ferromagnetic behavior within the antiferromagnetic matrix, persisting even at room temperature. Temperature-dependent magnetization measurements indicate a Neel temperature (TN ) shift from 70K to 55K, along with increased magnetization differences in field-cooled (FC) and zero field-cooled (ZFC) data, reflecting heightened magnetic frustration due to competing FM/AFM exchange interactions. X-ray photoelectron spectroscopy (XPS) and X-ray absorption near-edge structure (XANES) analyses reveal a rise in Fe$^{3+}$ and V$^{5+}$ states, affecting oxygen vacancy distributions and corresponding structural shifts seen in XRD and Raman results. The multivalent Fe$^{3+}$/Fe$^{4+}$ and V$^{4+}$/V$^{5+}$ states enhance double-exchange (DE) and super-exchange (SE) interactions (Fe$^{3+}$-O-Fe$^{4+}$ and Fe$^{3+}$-O-V$^{5+}$), promoting ferromagnetism. Frequency-dependent magnetization studies display a subtle susceptibility peak shift, indicating spin-glass-like behavior in V-doped samples.

We introduce a teleoperation system that integrates a 5 DOF actuated neck, designed to replicate natural human head movements and perception. By enabling behaviors like peeking or tilting, the system provides operators with a more intuitive and comprehensive view of the environment, improving task performance, reducing cognitive load, and facilitating complex whole-body manipulation. We demonstrate the benefits of natural perception across seven challenging teleoperation tasks, showing how the actuated neck enhances the scope and efficiency of remote operation. Furthermore, we investigate its role in training autonomous policies through imitation learning. In three distinct tasks, the actuated neck supports better spatial awareness, reduces distribution shift, and enables adaptive task-specific adjustments compared to a static wide-angle camera.

In this paper we propose a formal, model-agnostic meta-learning framework for safe reinforcement learning. Our framework is inspired by how parents safeguard their children across a progression of increasingly riskier tasks, imparting a sense of safety that is carried over from task to task. We model this as a meta-learning process where each task is synchronized with a safeguard that monitors safety and provides a reward signal to the agent. The safeguard is implemented as a finite-state machine based on a safety specification; the reward signal is formally shaped around this specification. The safety specification and its corresponding safeguard can be arbitrarily complex and non-Markovian, which adds flexibility to the training process and explainability to the learned policy. The design of the safeguard is manual but it is high-level and model-agnostic, which gives rise to an end-to-end safe learning approach with wide applicability, from pixel-level game control to language model fine-tuning. Starting from a given set of safety specifications (tasks), we train a model such that it can adapt to new specifications using only a small number of training samples. This is made possible by our method for efficiently transferring safety bias between tasks, which effectively minimizes the number of safety violations. We evaluate our framework in a Minecraft-inspired Gridworld, a VizDoom game environment, and an LLM fine-tuning application. Agents trained with our approach achieve near-minimal safety violations, while baselines are shown to underperform.

Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.

Scaling laws for critical phenomena take pivotal status in almost all branches of physics. However, as scaling laws are commonly guaranteed by the renormalization group theory, systems that violate them have rarely been found. In this letter, we demonstrate that gravitational system can break scaling laws. We derive this result through investigating phase transition and critical phenomenon in a gravitational system with quantum anomaly. For the first time, we outline the key conditions to violate the scaling laws in generic gravitational system viewed from the equation of state $P=P(T,V)$. Our results indicate that quantum effects can magnify the distinctiveness of gravity, which may be significant to understand the microscopic structure of spacetime., Comment: 7 pages, 3 figures

Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid mistakenly colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited input, such as a single RGB-D image. We describe SceneComplete, a system for constructing a complete, segmented, 3D model of a scene from a single view. It provides a novel pipeline for composing general-purpose pretrained perception modules (vision-language, segmentation, image-inpainting, image-to-3D, and pose-estimation) to obtain high-accuracy results. We demonstrate its accuracy and effectiveness with respect to ground-truth models in a large benchmark dataset and show that its accurate whole-object reconstruction enables robust grasp proposal generation, including for a dexterous hand. Project website - https://scenecomplete.github.io/

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions., Comment: 255 pages

In Adil et al. 2023, we reported an increasing trend in $S_8$ with effective redshift $z_{\textrm{eff}}$ based on $f \sigma_8(z)$ constraints over the redshift range $0 \lesssim z \lesssim 2$, and predicted that this trend would be observable in independent datasets. Recently, the studies by Artis et al. and the ACT+DESI collaboration appeared, presenting data that aligns with the expected trends. In this letter, we quantify the statistical significance of the increasing $S_8$ trends in recent studies by fitting a linear model to estimate the slope $\Delta\,S_8/\Delta\, z_{\textrm{eff}}$, and comparing the results to mock simulations. We find probabilities of $p = 0.0163$ and $p = 0.01893$, corresponding to approximately $2.1\sigma$ for each dataset. Using Fisher's method to combine the independent probabilities, we obtained $p=0.0027$ ($2.8 \sigma$). When we incorporate our earlier findings, the combined statistical significance reaches between $3\sigma$ and $3.7\sigma$. This letter continues a series of studies initiated in 2020 that explore redshift-dependent $\Lambda$CDM parameters as an indication of a breakdown in the standard cosmological model., Comment: A short comment on trends in recent papers and how they align with a previous claim. Comments are welcome!

Recent observations by JWST yield a large abundance of luminous galaxies at $z\gtrsim 10$ compared to that expected in the CDM scenario based on extrapolations of the star formation efficiency measured at lower redshifts. While several astrophysical processes can be responsible for such observations, here we explore to what extent such an effect can be rooted in the assumed Dark Energy (DE) sector of the current cosmological model. This is motivated by recent results from different cosmological probes combined with the last data release of the Dark Energy Spectroscopic Instrument (DESI), which indicate a tension in the DE sector of the concordance ${\Lambda}$ CDM model. We have considered the effect of assuming a DE characterized by a negative {\Lambda} as the ground state of a quintessence field on the galaxy luminosity function (LF) at high redshifts. We find that such models naturally affect the galaxy UV luminosities in the redshift range $10 \lesssim z\lesssim 15$ needed to match the JWST observations, and with the value of ${\Omega}_{\Lambda}$=[-0.6,-0.3] remarkably consistent with that required by independent cosmological probes. A sharp prediction of such models is the steep decline of the abundance of bright galaxies in the redshift range $15 \lesssim z\lesssim 16$., Comment: p pages, accepted for publication in The Astrophysical Journal

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity $\mathcal{O}(1/(1-\gamma)^2)$. We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity $\mathcal{O}(1/(1-\gamma))$. Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization., Comment: Accepted by NeurIPS 2024

Fluoroscopy is critical for real-time X-ray visualization in medical imaging. However, low-dose images are compromised by noise, potentially affecting diagnostic accuracy. Noise reduction is crucial for maintaining image quality, especially given such challenges as motion artifacts and the limited availability of clean data in medical imaging. To address these issues, we propose an unsupervised training framework for dynamic context-aware denoising of fluoroscopy image sequences. First, we train the multi-scale recurrent attention U-Net (MSR2AU-Net) without requiring clean data to address the initial noise. Second, we incorporate a knowledge distillation-based uncorrelated noise suppression module and a recursive filtering-based correlated noise suppression module enhanced with motion compensation to further improve motion compensation and achieve superior denoising performance. Finally, we introduce a novel approach by combining these modules with a pixel-wise dynamic object motion cross-fusion matrix, designed to adapt to motion, and an edge-preserving loss for precise detail retention. To validate the proposed method, we conducted extensive numerical experiments on medical image datasets, including 3500 fluoroscopy images from dynamic phantoms (2,400 images for training, 1,100 for testing) and 350 clinical images from a spinal surgery patient. Moreover, we demonstrated the robustness of our approach across different imaging modalities by testing it on the publicly available 2016 Low Dose CT Grand Challenge dataset, using 4,800 images for training and 1,136 for testing. The results demonstrate that the proposed approach outperforms state-of-the-art unsupervised algorithms in both visual quality and quantitative evaluation while achieving comparable performance to well-established supervised learning methods across low-dose fluoroscopy and CT imaging., Comment: 15 pages, 10 figures

In practical application, the widespread deployment of diffusion models often necessitates substantial investment in training. As diffusion models find increasingly diverse applications, concerns about potential misuse highlight the imperative for robust intellectual property protection. Current protection strategies either employ backdoor-based methods, integrating a watermark task as a simpler training objective with the main model task, or embedding watermarks directly into the final output samples. However, the former approach is fragile compared to existing backdoor defense techniques, while the latter fundamentally alters the expected output. In this work, we introduce a novel watermarking framework by embedding the watermark into the whole diffusion process, and theoretically ensure that our final output samples contain no additional information. Furthermore, we utilize statistical algorithms to verify the watermark from internally generated model samples without necessitating triggers as conditions. Detailed theoretical analysis and experimental validation demonstrate the effectiveness of our proposed method.

Materials with magnetoelectric coupling (MEC) between ferroic orders at room temperature are extremely important in modern technology and physics. BaTiO$_3$ is a robust ferroelectric in which several doping have led to MEC. However, often MEC depends on the amount of substitution. In a series of (1-x)BaTiO$_{3.}$(x)CaMnO$_3$ (x=0,0.03, 0.06, 0.09), MEC was only observed in (0.97)BaTiO$_{3.}$(0.03)CaMnO$_3$. The structural modifications with changing substitution reveal a reduced Ti-O-Ti angle for this sample which is the most ferromagnetic in nature. A mixed phase of tetragonal P4mm and hexagonal P6$_3$/mmc space groups of BaTiO$_3$ is observed in the substituted samples, with nominal contribution of the hexagonal phase for x=0.03. A valence state study using XPS and XANES reveals the presence of enhanced proportion of Mn3+ ions in the sample which support a Jahn-Teller distortion, thereby supporting the ferroelectricity for x=0.03. Indirect and direct evidences of MEC was obtained from magnetodielectric and magnetoelectric measurements. A magnetocapacitance of 0.4% was observed with a dc magnetic field of 4kOe at 10kHz, while magnetoelectric coupling coefficient, $\alpha_{\rm ME} \sim 88$ mVcm$^{-1}$Oe$^{-1}$ was obtained for dc magnetic field of 600 Oe and a 10Hz ac field of 40 Oe. Such MEC was not observed for higher substitution which emphasizes the sensitivity of the structural properties on substitution.

We investigate the behaviour of the prominent non-radial fundamental $f$-mode oscillations of dark matter (DM) admixed strange quark stars (DMSQSs), by adopting an equation of state (EoS) developed in Ref.~\cite{Sen:2022pfr}, which considers the possible presence of feebly interacting DM in strange quark stars (SQSs) for the first time. Within the model, feeble interaction between fermionic DM $\chi$ and strange quark matter (SQM) is invoked via a vector new physics mediator $\xi$ with coupling strength $y_{\xi}$. The pure SQM is described by the vector MIT Bag model. By varying different EoS parameters, the structural properties viz. the mass, radius and tidal deformability ($\Lambda$) of the DMSQSs are studied with respect to various astrophysical constraints. We study in detail the $f$-mode spectra within the Cowling approximation by obtaining the frequencies as a function of mass, compactness and $\Lambda$ of the star. To the best of our knowledge, this study represents the first analysis of non-radial $f$-mode oscillations of DMSQSs. Our investigation indicates that the presence of DM and its interaction with SQM has great impact on the $f$-modes. We show that the $f$-mode frequencies are larger for DMSQSs, which are largely populated with massive DM fermions, compared to the SQSs. Further, we obtain a linear empirical relation between the $f$-modes and the average density of the star. We also find that the mass-scaled angular frequency varies universally with compactness for DMSQSs., Comment: 14 pages, 9 figures

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. {Firstly, the absence of a unified evaluation platform for defining and describing tools' supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity.} Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose a unified platform named VulsTotal, supporting various vulnerability types, enabling comprehensive and versatile analysis across diverse SAST tools. Specifically, we begin by meticulously selecting 11 free and open-sourced SAST tools from a pool of 97 existing options, adhering to clearly defined criteria. After that, we invest significant efforts in comprehending the detection rules of each tool, subsequently unifying 67 general/common vulnerability types for {Android} SAST tools. We also redefine and implement a standardized reporting format, ensuring uniformity in presenting results across all tools. Additionally, to mitigate the problem of benchmarks, we conducted a manual analysis of huge amounts of CVEs to construct a new CVE-based benchmark based on our comprehension of Android app vulnerabilities. Leveraging the evaluation platform, which integrates both existing synthetic benchmarks and newly constructed CVE-based benchmarks from this study, we conducted a comprehensive analysis to evaluate and compare these selected tools from various perspectives, such as general vulnerability type coverage, type consistency, tool effectiveness, and time performance., Comment: Accepted by TSE

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. The current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. In this paper, we present ScamSweeper, a novel framework to identify web3 scams on Ethereum. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts. Our experiments indicate that ScamSweeper exceeds the state-of-the-art in detecting web3 scams., Comment: This work is accepted by ASE'24

This paper addresses the medical imaging problem of joint detection in the upper limbs, viz. elbow, shoulder, wrist and finger joints. Localization of joints from X-Ray and Computerized Tomography (CT) scans is an essential step for the assessment of various bone-related medical conditions like Osteoarthritis, Rheumatoid Arthritis, and can even be used for automated bone fracture detection. Automated joint localization also detects the corresponding bones and can serve as input to deep learning-based models used for the computerized diagnosis of the aforementioned medical disorders. This in-creases the accuracy of prediction and aids the radiologists with analyzing the scans, which is quite a complex and exhausting task. This paper provides a detailed comparative study between diverse Deep Learning (DL) models - YOLOv3, YOLOv7, EfficientDet and CenterNet in multiple bone joint detections in the upper limbs of the human body. The research analyses the performance of different DL models, mathematically, graphically and visually. These models are trained and tested on a portion of the openly available MURA (musculoskeletal radiographs) dataset. The study found that the best Mean Average Precision (mAP at 0.5:0.95) values of YOLOv3, YOLOv7, EfficientDet and CenterNet are 35.3, 48.3, 46.5 and 45.9 respectively. Besides, it has been found YOLOv7 performed the best for accurately predicting the bounding boxes while YOLOv3 performed the worst in the Visual Analysis test. Code available at https://github.com/Sohambasu07/BoneJointsLocalization