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Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augment Generation (RAG) is popular to address this issue, few studies implemented and evaluated RAG in downstream domain-specific applications. We developed a RAG pipeline with 70,000 ophthalmology-specific documents that retrieve relevant documents to augment LLMs during inference time. In a case study on long-form consumer health questions, we systematically evaluated the responses including over 500 references of LLMs with and without RAG on 100 questions with 10 healthcare professionals. The evaluation focuses on factuality of evidence, selection and ranking of evidence, attribution of evidence, and answer accuracy and completeness. LLMs without RAG provided 252 references in total. Of which, 45.3% hallucinated, 34.1% consisted of minor errors, and 20.6% were correct. In contrast, LLMs with RAG significantly improved accuracy (54.5% being correct) and reduced error rates (18.8% with minor hallucinations and 26.7% with errors). 62.5% of the top 10 documents retrieved by RAG were selected as the top references in the LLM response, with an average ranking of 4.9. The use of RAG also improved evidence attribution (increasing from 1.85 to 2.49 on a 5-point scale, P<0.001), albeit with slight decreases in accuracy (from 3.52 to 3.23, P=0.03) and completeness (from 3.47 to 3.27, P=0.17). The results demonstrate that LLMs frequently exhibited hallucinated and erroneous evidence in the responses, raising concerns for downstream applications in the medical domain. RAG substantially reduced the proportion of such evidence but encountered challenges.

We identify delta Scuti pulsators amongst members of the recently-discovered Cep--Her Complex using light curves from the Transiting Exoplanet Survey Satellite (TESS). We use Gaia colours and magnitudes to isolate a subsample of provisional Cep--Her members that are located in a narrow band on the colour--magnitude diagram compatible with the zero-age main sequence. The $\delta$ Sct pulsator fraction amongst these stars peaks at 100% and we describe a trend of higher pulsator fractions for younger stellar associations. We use four methods to measure the frequency of maximum amplitude or power, $\nu_{\rm max}$, to minimise methodological bias and we demonstrate their sound performance. The $\nu_{\rm max}$ measurements display a correlation with effective temperature, but with scatter that is too large for the relation to be useful. We find two ridges in the $\nu_{\rm max}$--$T_{\rm eff}$ diagram, one of which appears to be the result of rapid rotation causing stars to pulsate in low-order modes. We measure the $\nu_{\rm max}$ values of $\delta$ Sct stars in four other clusters or associations of similar age (Trumpler 10, the Pleiades, NGC 2516, and Praesepe) and find similar behaviour with $T_{\rm eff}$. Using \'echelle diagrams we measure the asteroseismic large spacing, $\Delta\nu$, for 70 stars, and find a correlation between $\Delta\nu$, rotation, and luminosity that allows rapid rotators seen at low inclinations to be distinguished from slow rotators. We find that rapid rotators are more likely than slow rotators to pulsate, but they do so with less regular pulsation patterns. We also investigate the reliability of Gaia's vbroad measurement for A-type stars, finding that it is mostly accurate but underestimates $v\sin i$ for slow rotators ($v\sin i < 50$ km.s$^{-1}$) by 10--15%., Comment: 19 pages, or 12 without end-matter. Accepted in MNRAS. Supplementary online material will be made available via the MNRAS webpage and CDS. Version 2 has an updated reference list

The magnetic field strength and its topology play an important role in understanding the formation, evolution, and dynamics of the solar corona. Also, it plays a significant role in addressing long-standing mysteries such as coronal heating problem, origin and propagation of coronal mass ejections, drivers of space weather, origin and acceleration of solar wind, and so on. Despite having photospheric magnetograms for decades, we do not have reliable observations of coronal magnetic field strengths today. To measure the coronal magnetic field precisely, the spectropolarimetry channel of the Visible Emission Line Coronagraph (VELC) on board the Aditya-L1 mission is designed. Using the observations of coronal emission line Fe XIII [10747{\AA~}], it is possible to generate full Stokes maps (I, Q, U, and V) that help in estimating the Line-of-Sight (LOS) magnetic field strength and to derive the magnetic field topology maps of solar corona in the Field of View (FOV) (1.05 -- 1.5~R$_{\odot}$). In this article, we summarize the instrumental details of the spectropolarimetry channel and detailed calibration procedures adopted to derive the modulation and demodulation matrices. Furthermore, we have applied the derived demodulation matrices to the observed data in the laboratory and studied their performance., Comment: 12 pages, 5 Figures, Published in Journal of Experimental Astronomy

Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples and voting among generated samples. While these improve performance, they often reach a saturation point. Refinement offers an alternative by using LLM-generated feedback to improve solution quality. However, refinement introduces 3 key challenges: (1) Excessive refinement: Uniformly refining all instances can over-correct and reduce the overall performance. (2) Inability to localize and address errors: LLMs have a limited ability to self-correct and struggle to identify and correct their own mistakes. (3) Insufficient refinement: Deciding how many iterations of refinement are needed is non-trivial, and stopping too soon could leave errors unaddressed. To tackle these issues, we propose MAgICoRe, which avoids excessive refinement by categorizing problem difficulty as easy or hard, solving easy problems with coarse-grained aggregation and hard ones with fine-grained and iterative multi-agent refinement. To improve error localization, we incorporate external step-wise reward model (RM) scores. Moreover, to ensure effective refinement, we employ a multi-agent loop with three agents: Solver, Reviewer (which generates targeted feedback based on step-wise RM scores), and the Refiner (which incorporates feedback). To ensure sufficient refinement, we re-evaluate updated solutions, iteratively initiating further rounds of refinement. We evaluate MAgICoRe on Llama-3-8B and GPT-3.5 and show its effectiveness across 5 math datasets. Even one iteration of MAgICoRe beats Self-Consistency by 3.4%, Best-of-k by 3.2%, and Self-Refine by 4.0% while using less than half the samples. Unlike iterative refinement with baselines, MAgICoRe continues to improve with more iterations. Finally, our ablations highlight the importance of MAgICoRe's RMs and multi-agent communication., Comment: 22 pages, code: https://github.com/dinobby/MAgICoRe

We investigate the potential of epitaxial (001)p-NiO/(0001)n-ZnO heterostructures grown on (0001)sapphire substrates by pulsed laser deposition technique for ultraviolet photodetector application. Our study reveals that in the self-powered mode, these devices can serve as effective photodetectors for the UV-A band (320-400 nm) with response time as short as 400 microseconds. Peak responsivity as high as 5mA/W at zero bias condition have been achieved. These devices also show a very high level of stability under repeated on/off illumination cycles over a long period of time. Furthermore, we find that the response time of these detectors can be controlled from several microseconds to thousands of seconds by applying bias both in the forward and the reverse directions. This persistent photoconductivity effect has been explained in terms of the field induced change in the capture barrier height associated with certain traps located at the junction., Comment: 10 Pages

Current chess rating systems update ratings incrementally and may not always accurately reflect a player's true strength at all times, especially for rapidly improving players or very rusty players. To overcome this, we explore a method to estimate player ratings directly from game moves and clock times. We compiled a benchmark dataset from Lichess with over one million games, encompassing various time controls and including move sequences and clock times. Our model architecture comprises a CNN to learn positional features, which are then integrated with clock-time data into a Bidirectional LSTM, predicting player ratings after each move. The model achieved an MAE of 182 rating points on the test data. Additionally, we applied our model to the 2024 IEEE Big Data Cup Chess Puzzle Difficulty Competition dataset, predicted puzzle ratings and achieved competitive results. This model is the first to use no hand-crafted features to estimate chess ratings and also the first to output a rating prediction after each move. Our method highlights the potential of using move-based rating estimation for enhancing rating systems and potentially other applications such as cheating detection., Comment: Accepted CG 2024 (11 pages, 2 figures)

The multi-wavelength data from the Solar Dynamics Observatory (SDO) is extensively used in studying the physics of the Sun and its atmosphere. In this study, we estimate the formation heights of low-corona and chromospheric channels of the Atmospheric Imaging Assembly (AIA) over the atmospheres of sunspot umbrae during the quiet condition period within 20 different active regions. The upward propagating slow magnetoacoustic waves (slow MAWs) of 3-min period, which are perpetually present in sunspots, are utilized for this purpose. Employing a cross-correlation technique, the most frequent time lag between different channel pairs is measured. By combining this information with the local sound speed obtained from the characteristic formation temperatures of individual channels, we estimate the respective formation heights. The median values of formation heights obtained across all active regions in our sample are 356, 368, 858, 1180, and 1470 km, respectively, for the AIA 1600 {\AA}, 1700 {\AA}, 304 {\AA}, 131 {\AA}, and 171 {\AA} channels. The corresponding ranges in the formation heights are 247 $\--$ 453, 260 $\--$ 468, 575 $\--$ 1155, 709 $\--$ 1937, and 909 $\--$ 2585 km, respectively. These values are measured with respect to the HMI continuum. We find the formation height of UV channels is quite stable (between 250 $\--$ 500 km) and displays only a marginal difference between the AIA 1600 {\AA} and 1700 {\AA} during quiet conditions. On the other hand, the formation height of coronal channels is quite variable., Comment: accepted for publication in ApJ

Handling haphazard streaming data, such as data from edge devices, presents a challenging problem. Over time, the incoming data becomes inconsistent, with missing, faulty, or new inputs reappearing. Therefore, it requires models that are reliable. Recent methods to solve this problem depend on a hedging-based solution and require specialized elements like auxiliary dropouts, forked architectures, and intricate network design. We observed that hedging can be reduced to a special case of weighted residual connection; this motivated us to approximate it with plain self-attention. In this work, we propose HapNet, a simple baseline that is scalable, does not require online backpropagation, and is adaptable to varying input types. All present methods are restricted to scaling with a fixed window; however, we introduce a more complex problem of scaling with a variable window where the data becomes positionally uncorrelated, and cannot be addressed by present methods. We demonstrate that a variant of the proposed approach can work even for this complex scenario. We extensively evaluated the proposed approach on five benchmarks and found competitive performance.

Nowadays, billboard advertising has emerged as an effective advertising technique due to higher returns on investment. Given a set of selected slots and tags, how to effectively assign the tags to the slots remains an important question. In this paper, we study the problem of assigning tags to the slots such that the number of tags for which influence demand of each zone is satisfied gets maximized. Formally, we call this problem the Multi-Slot Tag Assignment Problem. The input to the problem is a geographical region partitioned into several zones, a set of selected tags and slots, a trajectory, a billboard database, and the influence demand for every tag for each zone. The task here is to find out the assignment of tags to the slots, such the number of tags for which the zonal influence demand is satisfied is maximized. We show that the problem is NP-hard, and we propose an efficient approximation algorithm to solve this problem. A time and space complexity analysis of the proposed methodology has been done. The proposed methodology has been implemented with real-life datasets, and a number of experiments have been carried out to show the effectiveness and efficiency of the proposed approach. The obtained results have been compared with the baseline methods, and we observe that the proposed approach leads to a number of tags whose zonal influence demand is satisfied., Comment: 13 Page

In the past three years, there has been significant interest in hyperspectral imagery (HSI) classification using vision Transformers for analysis of remotely sensed data. Previous research predominantly focused on the empirical integration of convolutional neural networks (CNNs) to augment the network's capability to extract local feature information. Yet, the theoretical justification for vision Transformers out-performing CNN architectures in HSI classification remains a question. To address this issue, a unified hierarchical spectral vision Transformer architecture, specifically tailored for HSI classification, is investigated. In this streamlined yet effective vision Transformer architecture, multiple mixer modules are strategically integrated separately. These include the CNN-mixer, which executes convolution operations; the spatial self-attention (SSA)-mixer and channel self-attention (CSA)-mixer, both of which are adaptations of classical self-attention blocks; and hybrid models such as the SSA+CNN-mixer and CSA+CNN-mixer, which merge convolution with self-attention operations. This integration facilitates the development of a broad spectrum of vision Transformer-based models tailored for HSI classification. In terms of the training process, a comprehensive analysis is performed, contrasting classical CNN models and vision Transformer-based counterparts, with particular attention to disturbance robustness and the distribution of the largest eigenvalue of the Hessian. From the evaluations conducted on various mixer models rooted in the unified architecture, it is concluded that the unique strength of vision Transformers can be attributed to their overarching architecture, rather than being exclusively reliant on individual multi-head self-attention (MSA) components., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Snow is a crucial element of the sea ice system, affecting sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have an idealized representation of snow, often including only single-layer thermodynamics and omitting several processes that shape its properties. Although advanced snow process models like SnowModel exist, they are often excluded from climate modeling due to their high computational costs. SnowModel simulates snow depth, density, blowing-snow redistribution, sublimation, grain size, and thermal conductivity in a multi-layer snowpack. It operates with high spatial (1 meter) and temporal (1 hour) resolution. However, for large regions like the Arctic Ocean, these high-resolution simulations face challenges such as slow processing and large resource requirements. Data-driven emulators are used to address these issues, but they often lack generalizability and consistency with physical laws. In our study, we address these challenges by developing a physics-guided emulator that incorporates physical laws governing changes in snow density due to compaction. We evaluated three machine learning models: Long Short-Term Memory (LSTM), Physics-Guided LSTM, and Random Forest across five Arctic regions. All models achieved high accuracy, with the Physics-Guided LSTM showing the best performance in accuracy and generalizability. Our approach offers a faster way to emulate SnowModel with a speedup of over 9000 times, maintaining high fidelity., Comment: Accepted for publication in Cambridge Environmental Data Science

The eBPF framework enables execution of user-provided code in the Linux kernel. In the last few years, a large ecosystem of cloud services has leveraged eBPF to enhance container security, system observability, and network management. Meanwhile, incessant discoveries of memory safety vulnerabilities have left the systems community with no choice but to disallow unprivileged eBPF programs, which unfortunately limits eBPF use to only privileged users. To improve run-time safety of the framework, we introduce SafeBPF, a general design that isolates eBPF programs from the rest of the kernel to prevent memory safety vulnerabilities from being exploited. We present a pure software implementation using a Software-based Fault Isolation (SFI) approach and a hardware-assisted implementation that leverages ARM's Memory Tagging Extension (MTE). We show that SafeBPF incurs up to 4% overhead on macrobenchmarks while achieving desired security properties., Comment: 15 pages, 9 figures

Knowledge conflict arises from discrepancies between information in the context of a large language model (LLM) and the knowledge stored in its parameters. This can hurt performance when using standard decoding techniques, which tend to ignore the context. Existing test-time contrastive methods seek to address this by comparing the LLM's output distribution with and without the context and adjust the model according to the contrast between them. However, we find that these methods frequently misjudge the degree of conflict and struggle to handle instances that vary in their amount of conflict, with static methods over-adjusting when conflict is absent. We propose a fine-grained, instance-level approach called AdaCAD, which dynamically infers the weight of adjustment based on the degree of conflict, as measured by the Jensen-Shannon divergence between distributions representing contextual and parametric knowledge. Our experiments across four models on six diverse question-answering (QA) datasets and three summarization tasks demonstrate that our training-free adaptive method consistently outperforms other decoding methods on QA, with average accuracy gains of 14.21% (absolute) over a static contrastive baseline, and improves the factuality of summaries by 5.59 (AlignScore). Furthermore, our analysis shows that while decoding with contrastive baselines hurts performance when conflict is absent, AdaCAD mitigates these losses, making it more applicable to real-world datasets in which some examples have conflict and others do not., Comment: 16 pages, Code: https://github.com/HanNight/AdaCAD

The $CP$-even fractions ($F_{+}$) of the decays $D^0\to\pi^{+}\pi^{-}\pi^{0}$ and $D^0\to K^{+}K^{-}\pi^{0}$ are measured with a quantum-correlated $\psi(3770)\to D\bar{D}$ data sample collected by the BESIII experiment corresponding to an integrated luminosity of 7.93 $\mathrm{fb}^{-1}$. The results are $F_{+}^{\pi^{+}\pi^{-}\pi^{0}}=0.9406\pm0.0036\pm0.0021$ and $F_{+}^{K^{+}K^{-}\pi^{0}}=0.631\pm0.014\pm0.011$, where the first uncertainties are statistical and the second systematic. These measurements are consistent with the previous determinations, and the uncertainties for $F_{+}^{\pi^{+}\pi^{-}\pi^{0}}$ and $F_{+}^{K^{+}K^{-}\pi^{0}}$ are reduced by factors of 3.9 and 2.6, respectively. The reported results provide important inputs for the precise measurement of the angle $\gamma$ of the Cabibbo-Kobayashi-Maskawa matrix and indirect $CP$ violation in charm mixing., Comment: 19 pages, 8 figures

In relativistic heavy-ion collisions, the formation of a deconfined and thermalized state of partons, known as quark-gluon plasma, leads to enhanced production of strange hadrons in contrast to proton-proton (pp) collisions, which are taken as baseline. This observation is known as strangeness enhancement in heavy-ion collisions and is considered one of the important signatures that can signify the formation of QGP. However, in addition to strangeness enhancement, recent measurements hint at observing several heavy-ion-like features in high-multiplicity pp collisions at the LHC energies. Alternatively, event shape observables, such as charged particle multiplicity, transverse spherocity, transverse sphericity, charged particle flattenicity, and relative transverse activity classifiers, can fundamentally separate hard interaction-dominated jetty events from soft isotropic events. These features of event shape observables can probe the observed heavy-ion-like features in pp collisions with significantly reduced selection bias and can bring all collision systems on equal footing. In this article, we present an extensive summary of the strange particle ratios to pions as a function of different event classifiers using the PYTHIA~8 model with color reconnection and rope hadronization mechanisms to understand the microscopic origin of strangeness enhancement in pp collisions and also prescribe the applicability of these event classifiers in the context of strangeness enhancement. Charged-particle flattenicity is found to be most suited for the study of strangeness enhancement, and it shows a similar quantitative enhancement as seen for the analysis based on the number of multi-parton interactions., Comment: 14 pages and 12 captioned figures. Submitted for publication

NiO/ZnO heterostructures are grown on c-sapphire substrates using pulsed laser deposition (PLD) technique. X-ray diffraction study shows that the ZnO layer epitaxially grows along [0001]-direction on (0001)sapphire surface as expected. While, the epitaxial NiO film is found to be deposited along [001]-direction on the (0001)ZnO surface. Moreover, the presence of three (001)NiO domains laterally rotated by 30{\deg} with respect to each other, has also been observed in our NiO films. The study reveals the continuous nature of the NiO film, which also possesses a very smooth surface morphology. In a sharp contrast, ZnO films are found to grow along [0001]-direction when deposited on (111)NiO layers. These films also show columnar morphology. (001)NiO/(0001)ZnO layers exhibit the rectifying current-voltage characteristics that suggests the existence of p-n junction in these devices. However, the behavior could not be observed in (0001)ZnO/(111)NiO heterojunctions. The reason could be the columnar morphology of the ZnO layer. Such a morphology can facilitate the propagation of the metal ions from the contact pads to the underlying NiO layer and suppress the p-n junction effect., Comment: 6 pages and 6 figures (main manuscript), 6 pages and 6 figures (supplemental material)

Craters form as the lander's exhaust interacts with the planetary surfaces. Understanding this phenomenon is imperative to ensure safe landings. We investigate crater morphology, where a turbulent air jet impinges on the granular surfaces. To reveal the fundamental aspect of this phenomenon, systematic experiments are performed with various air jet velocities, nozzle positions, and grain properties. The resultant crater morphology is characterized by an aspect ratio. We find a universal scaling law in which the aspect ratio is scaled by the dimensionless variable consisting of air velocity at the nozzle, speed of sound in air, nozzle diameter, nozzle tip distance from the surface, grain diameter, the density of grains, and density of air. The obtained scaling reveals the crossover of the length scales governing crater aspect ratio, providing a useful guideline for ensuring safe landings. Moreover, we report a novel drop shaped subsurface cratering phenomenon., Comment: 18 pages, 12 figures

Two-dimensional (2D) semiconductors have emerged as leading candidates for the development of low-power and multifunctional computing applications, thanks to their qualities such as layer-dependent band gap tunability, high carrier mobility, and excellent electrostatic control. Here, we explore a pair of 2D semiconductors with broken-gap (Type III) band alignment and demonstrate a highly gate-tunable p-MoTe$_{2}$/n-SnS$_{2}$ heterojunction tunnel field-effect transistor with multifunctional behavior. Employing a dual-gated asymmetric device geometry, we unveil its functionality as both a forward and backward rectifying device. Consequently, we observe a highly gate-tunable negative differential resistance (NDR), with a gate-coupling efficiency of $\eta \simeq 0.5$ and a peak-to-valley ratio of $\sim$ 3 down to 150K. By employing density functional theory and exploring the density of states, we determine that interband tunneling within the valence bands is the cause of the observed NDR characteristics. The combination of band-to-band tunneling and gate controllability of NDR signal open the pathway for realizing gate-tunable 2D material-based neuromorphic and energy-efficient electronics., Comment: 22 pages, 5 figures

Spatial understanding is a critical aspect of most robotic tasks, particularly when generalization is important. Despite the impressive results of deep generative models in complex manipulation tasks, the absence of a representation that encodes intricate spatial relationships between observations and actions often limits spatial generalization, necessitating large amounts of demonstrations. To tackle this problem, we introduce a novel policy class, ActionFlow. ActionFlow integrates spatial symmetry inductive biases while generating expressive action sequences. On the representation level, ActionFlow introduces an SE(3) Invariant Transformer architecture, which enables informed spatial reasoning based on the relative SE(3) poses between observations and actions. For action generation, ActionFlow leverages Flow Matching, a state-of-the-art deep generative model known for generating high-quality samples with fast inference - an essential property for feedback control. In combination, ActionFlow policies exhibit strong spatial and locality biases and SE(3)-equivariant action generation. Our experiments demonstrate the effectiveness of ActionFlow and its two main components on several simulated and real-world robotic manipulation tasks and confirm that we can obtain equivariant, accurate, and efficient policies with spatially symmetric flow matching. Project website: https://flowbasedpolicies.github.io/

We present a study of the semileptonic decay $D^0\rightarrow \pi^-\pi^0e^{+}\nu_{e}$ using an $e^+e^-$ annihilation data sample of $7.93~\mathrm{fb}^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of $D^0\to \rho(770)^-e^+\nu_e$ is measured to be $(1.439 \pm 0.033(\rm stat.) \pm 0.027(\rm syst.)) \times10^{-3}$, which is a factor 1.6 more precise than previous measurements. By performing an amplitude analysis, we measure the hadronic form-factor ratios of $D^0\to \rho(770)^-e^+\nu_e$ at $q^2=0$ assuming the single-pole-dominance parametrization: $r_{V}=V(0)/A_1(0)=1.548\pm0.079(\rm stat.)\pm0.041(\rm syst.)$ and $r_{2}=A_2(0)/A_1(0)=0.823\pm0.056(\rm stat.)\pm0.026(\rm syst.)$., Comment: 12 pages, 3 figures

Randomly distributed traps at the MoS$_2$/SiO$_2$ interface result in non-ideal transport behavior, including hysteresis in MoS$_2$/SiO$_2$ field effect transistors (FETs). Thus traps are mostly detrimental to the FET performance but they also offer some application potential. Our STM/S measurements on atomically resolved few-layer and single-layer MoS$_2$ on SiO$_2$ show n-doped behavior with the expected band gap close to 2.0 and 1.4 eV, respectively. The local tunnel conductance with gate-voltage $V_{\rm g}$ sweep exhibits a turn-on/off at a threshold $V_{\rm g}$ at which the tip's Fermi-energy nearly coincides with the local conduction band minimum. This threshold value is found to depend on $V_{\rm g}$ sweep direction amounting to local hysteresis. The hysteresis is, expectedly, found to depend on both the extent and rate of $V_{\rm g}$-sweep. Further, the spatial variation in the local $V_{\rm g}$ threshold and the details of tunnel conductance Vs $V_{\rm g}$ behavior indicate inhomogenieties in both the traps' density and their energy distribution. The latter even leads to the pinning of the local Fermi energy in some regions. Further, some rare locations exhibit a p-doping with both p and n-type $V_{\rm g}$-thresholds in local conductance and an unusual hysteresis., Comment: 10 pages, 8 figures

Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL) based workflows poses challenges in resource governance for reliability, and confidentiality for model/data privacy protection. There is a need for approaches to handle the volatility of volunteer edge node availability, and also to scale the confidential data-intensive workflow execution across a large number of VEC nodes. In this paper, we present VECA, a reliable and confidential VEC resource clustering solution featuring three-fold methods tailored for executing ML/DL-based scientific workflows on VEC resources. Firstly, a capacity-based clustering approach enhances system reliability and minimizes VEC node search latency. Secondly, a novel two-phase, globally distributed scheduling scheme optimizes job allocation based on node attributes and using time-series-based Recurrent Neural Networks. Lastly, the integration of confidential computing ensures privacy preservation of the scientific workflows, where model and data information are not shared with VEC resources providers. We evaluate VECA in a Function-as-a-Service (FaaS) cloud testbed that features OpenFaaS and MicroK8S to support two ML/DL-based scientific workflows viz., G2P-Deep (bioinformatics) and PAS-ML (health informatics). Results from tested experiments demonstrate that our proposed VECA approach outperforms state-of-the-art methods; especially VECA exhibits a two-fold reduction in VEC node search latency and over 20% improvement in productivity rates following execution failures compared to the next best method., Comment: Accepted - IC2E 2024 Conference

Unsolicited telephone calls that facilitate fraud or unlawful telemarketing continue to overwhelm network users and the regulators who prosecute them. The first step in prosecuting phone abuse is traceback -- identifying the call originator. This fundamental investigative task currently requires hours of manual effort per call. In this paper, we introduce J\"ager, a distributed secure call traceback system. J\"ager can trace a call in a few seconds, even with partial deployment, while cryptographically preserving the privacy of call parties, carrier trade secrets like peers and call volume, and limiting the threat of bulk analysis. We establish definitions and requirements of secure traceback, then develop a suite of protocols that meet these requirements using witness encryption, oblivious pseudorandom functions, and group signatures. We prove these protocols secure in the universal composibility framework. We then demonstrate that J\"ager has low compute and bandwidth costs per call, and these costs scale linearly with call volume. J\"ager provides an efficient, secure, privacy-preserving system to revolutionize telephone abuse investigation with minimal costs to operators., Comment: In Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24), October 14---18, 2024, Salt Lake City, UT, USA. ACM, New York, NY, USA, 24 pages

In this article, we investigate the focal locus of closed (not necessarily compact) submanifolds in a forward complete Finsler manifold. The main goal is to show that the associated normal exponential map is \emph{regular} in the sense of F.W. Warner (\textit{Am. J. of Math.}, 87, 1965). As a consequence, we show that the normal exponential is non-injective near any tangent focal point. Extending the ideas of Warner, we study the connected components of the regular focal locus. This allows us to identify an open and dense subset, on which the focal time maps are smooth, provided they are finite. We explicitly compute the derivative at a point of differentiability. As an application of the local form of the normal exponential map, following R.L. Bishop's work (\textit{Proc. Amer. Math. Soc.}, 65, 1977), we express the tangent cut locus as the closure of a certain set of points, called the separating tangent cut points. This strengthens the results from the present authors' previous work (\textit{J. Geom. Anal.}, 34, 2024)., Comment: 30 Pages, Comments are welcome!

Using $7.9~\rm{fb^{-1}}$ of $e^+e^-$ collision data collected at $\sqrt{s}=3.773$ GeV with the BESIII detector at the BEPCII collider, we search for the massless dark photon with the flavor-changing neutral current processes $D^0\to\omega\gamma'$ and $D^0\to\gamma\gamma'$ for the first time. No significant signals are observed, and the upper limits at the 90% confidence level on the massless dark photon branching fraction are set to be $1.1\times10^{-5}$ and $2.0\times10^{-6}$ for $D^0\to\omega\gamma'$ and $D^0\to\gamma\gamma'$, respectively. These results provide the most stringent constraint on the new physics energy scale associated with $cu\gamma'$ coupling in the world, with the new physics energy scale related parameter $|\mathbb{C}|^2+|\mathbb{C}_5|^2<8.2\times10^{-17}~\rm{GeV}^{-2}$ at the 90% confidence level., Comment: 10 pages, 3 figures

Billboard Advertisement has gained popularity due to its significant outrage in return on investment. To make this advertisement approach more effective, the relevant information about the product needs to be reached to the relevant set of people. This can be achieved if the relevant set of tags can be mapped to the correct slots. Formally, we call this problem the Tag Assignment Problem in Billboard Advertisement. Given trajectory, billboard database, and a set of selected billboard slots and tags, this problem asks to output a mapping of selected tags to the selected slots so that the influence is maximized. We model this as a variant of traditional bipartite matching called One-To-Many Bipartite Matching (OMBM). Unlike traditional bipartite matching, a tag can be assigned to only one slot; in the OMBM, a tag can be assigned to multiple slots while the vice versa can not happen. We propose an iterative solution approach that incrementally allocates the tags to the slots. The proposed methodology has been explained with an illustrated example. A complexity analysis of the proposed solution approach has also been conducted. The experimental results on real-world trajectory and billboard datasets prove our claim on the effectiveness and efficiency of the proposed solution., Comment: This Paper has been accepted at The 25th International Web Information Systems Engineering Conference (WISE-2024)

Micro-ring resonators (MRRs) "trap" incoming light, and therefore, have been shown to achieve extremely high local intensities of light. Thus, they can be used to facilitate highly non-linear optical signals. By embedding materials that host non-linear optical processes inside the MRR, we expect to observe an enhancement in the strength of the non-linear optical signal. This concept is demonstrated here by extracting the Raman signature of graphene that is placed inside a MRR. A highly doped silica MRR which features an optical bus waveguide coupled to a loop (ring) tuned to near-infrared wavelengths is used. Raman signal with an excitation wavelength of 522 nm via third harmonic generation inside the MRR is observed. Higher order Raman signal of the embedded graphene at the 1597.6 nm excitation wavelength is also observed. This work demonstrates the feasibility of the MRR as a non-linear signal enhancer using novel MRR device setups., Comment: Main text 6 pages with 3 figures. Supplementary text 4 pages with 3 figures and 1 table

Using a data sample of $e^+e^-$ collisions corresponding to an integrated luminosity of 7.93 $\rm fb^{-1}$ collected with the BESIII detector at the center-of-mass energy 3.773~GeV, we perform the first amplitude analysis of the decay $D^{+} \to K_{S}^{0} K_{S}^{0} \pi^{+}$. The absolute branching fraction of $D^{+} \to K_{S}^{0}K_{S}^{0} \pi^{+}$ is measured to be $(2.97 \pm 0.09_{\rm stat.} \pm 0.05_{\rm syst.})\times10^{-3}$. The dominant intermediate process is $D^{+} \to K_{S}^{0}K^{*}(892)^{+}$, whose branching fraction is determined to be $(8.72 \pm 0.28_{\rm stat.} \pm 0.15_{\rm syst.}) \times 10^{-3}$, including all the $K^*(892)^+$ decays.