Search

Efficient power management in cloud data centers is essential for reducing costs, enhancing performance, and minimizing environmental impact. GPUs, critical for tasks like machine learning (ML) and GenAI, are major contributors to power consumption. NVIDIA's Multi-Instance GPU (MIG) technology improves GPU utilization by enabling isolated partitions with per-partition resource tracking, facilitating GPU sharing by multiple tenants. However, accurately apportioning GPU power consumption among MIG instances remains challenging due to a lack of hardware support. This paper addresses this challenge by developing software methods to estimate power usage per MIG partition. We analyze NVIDIA GPU utilization metrics and find that light-weight methods with good accuracy can be difficult to construct. We hence explore the use of ML-based power models to enable accurate, partition-level power estimation. Our findings reveal that a single generic offline power model or modeling method is not applicable across diverse workloads, especially with concurrent MIG usage, and that online models constructed using partition-level utilization metrics of workloads under execution can significantly improve accuracy. Using NVIDIA A100 GPUs, we demonstrate this approach for accurate partition-level power estimation for workloads including matrix multiplication and Large Language Model inference, contributing to transparent and fair carbon reporting.

Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) and eventually to AD. Accurate identification of these stages, especially distinguishing LMCI from EMCI, is crucial for developing pre-dementia treatments but remains challenging due to subtle and overlapping imaging features. This study proposes a minimal-feature machine learning framework that leverages structural MRI data, focusing on the hippocampus and amygdala as regions of interest. The framework addresses the curse of dimensionality through feature selection, utilizes region-specific voxel information, and implements innovative data organization to enhance classification performance by reducing noise. The methodology integrates dimensionality reduction techniques such as PCA and t-SNE with state-of-the-art classifiers, achieving the highest accuracy of 88.46%. This framework demonstrates the potential for efficient and accurate staging of AD progression while providing valuable insights for clinical applications.

Vertical field effect transistors (VFETs) show many advantages such as high switching speed, low operating voltage, low power consumption, and miniaturization over lateral FETs. However, VFET still faces the main challenges of high off-state current. Graphene (Gr) and transition metal di-chalcogenides (TMDs) are attractive materials for the next generation electronics. In this simulation work, the bulk molybdenum disulfide (MoS2) is sandwiched between perforated monolayer Gr which acts as the source electrode, and aluminum (Al) as the top drain electrode. In addition to this, the minimization of the off-state current is carried out by modifications in the source contact geometry by insulating some part of the source electrode and introducing the extra MoS2 layer between the source and gate dielectric named as buried layer. After the modification, the results show an improvement in OFF current, hence the ON/OFF ratio. The highest ON/OFF ratio of 109 is achieved with top side insulated source contact and thinnest buried layer of 02 nm with top and sidewall insulation. These results would support low voltage operation with high switching speed in complete 2D material based VFETs and further miniaturize its geometry., Comment: 15 pages

The limitations of the Standard Model in explaining neutrino masses and neutrino mixing lead to the exploration of frameworks beyond the Standard Model (BSM). The possibility of neutrinos interacting with fermions via a scalar mediator is one of the interesting prospects. The study of neutrino non-standard interactions (NSI) is a well-motivated phenomenological scenario to explore new physics beyond the Standard Model. These new interactions may alter the standard neutrino oscillation probabilities, potentially leading to observable effects in experiments. It also allows for the exploration of absolute neutrino masses via oscillation experiments. It can modify the oscillation probabilities, which in turn can affect the physics sensitivities in long-baseline experiments. The linear scaling of the effects of scalar NSI with matter density also motivates its exploration in long-baseline (LBL) experiments. We will present our study on the impact of a scalar-mediated NSI on the mass ordering (MO) sensitivities of three long-baseline neutrino experiments, i.e., DUNE, HK and KNO. We study the impact on MO sensitivities at these experiments assuming that scalar NSI parameters are present in nature and are known from other non-LBL experiments. The presence of scalar NSI can notably impact the MO sensitivities of these experiments. Furthermore, we analyze the potential synergy by combining data from DUNE with HK and HK+KNO, thereby exploring a broader parameter space., Comment: Conference Proceedings NuFact2024-32, Based on arXiv:2309.12249 (Contribution to the 25th International Workshop on Neutrinos from Accelerators)

Retinal image analysis is crucial for diagnosing and treating eye diseases, yet generating accurate medical reports from images remains challenging due to variability in image quality and pathology, especially with limited labeled data. Previous Transformer-based models struggled to integrate visual and textual information under limited supervision. In response, we propose a novel vision-language model for retinal image captioning that combines visual and textual features through a guided context self-attention mechanism. This approach captures both intricate details and the global clinical context, even in data-scarce scenarios. Extensive experiments on the DeepEyeNet dataset demonstrate a 0.023 BLEU@4 improvement, along with significant qualitative advancements, highlighting the effectiveness of our model in generating comprehensive medical captions., Comment: This paper has been accepted for presentation at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)

The solar corona is highly structured by bunches of magnetic field lines forming either loops, or twisted flux ropes representing prominences/filaments, or very dynamic structures such as jets. The aim of this paper is to understand the interaction between filament channels and jets. We use high-resolution H$\alpha$ spectra obtained by the ground-based Telescope Heliographique pour lEtude du Magnetisme et des Instabilites Solaires (THEMIS) in Canary Islands, and data from Helioseismic Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). In this paper we present a multi-wavelength study of the interaction of filaments and jets. They both consist of cool plasma embedded in magnetic structures. A jet is particularly well studied in all the AIA channels with a flow reaching 100-180 km s$^{-1}$. Its origin is linked to cancelling flux at the edge of the active region. Large Dopplershifts in H$\alpha$ are derived in a typical area for a short time (order of min). They correspond to flows around 140 km s$^{-1}$. In conclusion we conjecture that these flows correspond to some interchange of magnetic field lines between the filament channel and the jets leading to cool plasmoid ejections or reconnection jets perpendicularly to the jet trajectory., Comment: 13 Figures, 14 pages

Dzyaloshinskii-Moriya interaction (DMI) plays a crucial role to stabilize the exotic topologically stable skyrmion spin-textures in the noncentrosymmetric crystals. The recent discovery of biskyrmions and skyrmions in the globally centrosymmetric crystals has raised debate about the role of the DMI in causing the spin textures, since DMI vanishes in such crystal structures. Theoretical studies, on the other hand, suggest non-vanishing DMI even if there is local inversion symmetry breaking in an otherwise globally centrosymmetric crystal structure. Motivated by such theoretical predictions, we present here the results of a systematic crystal structure study of two skyrmion-hosting Ni2In-type centrosymmetric hexagonal compounds, MnNiGa and MnPtGa, using the atomic pair distribution function (PDF) technique. Our result provides information about structural correlations in the short-range (SR), medium-range (MR) and long-range (LR) regimes simultaneously. The analysis of the experimental PDFs, obtained from high flux, high energy and high-Q synchrotron x-ray powder diffraction patterns, reveal that the local SR structure of both MnNiGa and MnPtGa compounds corresponds to the noncentrosymmetric trigonal space group P3m1, while the structure in the MR+LR regimes remains hexagonal in the centrosymmetric P63/mmc space group. These findings are also supported by theoretical DFT calculations. Our results in conjunction with the previous theoretical predictions, provide a rationale for the genesis of skyrmions in centrosymmetric materials in terms of non-vanishing DMI due to local inversion symmetry breaking. We believe that our findings would encourage a systematic search of skyrmionic textures and other topological phenomena in a vast family of centrosymmetric materials.

For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) has started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to significantly improve clinical outcomes. However, they also present unique challenges related to data integration and ethical issues. This survey reviews the development of ML and DL methods for the early diagnosis and treatment of mental health issues. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for diagnosing diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness progression is further discussed, focusing on the role of risk prediction models and longitudinal studies. Key findings highlight how ML and DL can improve diagnostic accuracy and treatment outcomes while addressing methodological inconsistencies, data integration challenges, and ethical concerns. The study emphasizes the importance of building real-time monitoring systems for individualized treatment, enhancing data fusion techniques, and fostering interdisciplinary collaboration. Future research should focus on overcoming these obstacles to ensure the valuable and ethical application of ML and DL in mental health services., Comment: 20 pages, 3 figures, 3 tables

Magnetic kagome metals have attracted tremendous research interests recently, because they represent an ideal playground for exploring the fascinating interplay between their intrinsically inherited topologically non-trivial electron band structures, magnetism and electronic correlation effects, and the resultant novel electronic/magnetic states and emergent excitations. In this work, we report a comprehensive single-crystal neutron diffraction investigation of the ground-state magnetic structures of the recently discovered V-based topological kagome metals RV$_6$Sn$_6$ (R = Tb, Dy, Ho, Er). Furthermore, the sample synthesis details and our systematic studies of crystal structure, low-temperature magnetic and thermodynamic properties of these compounds via various in-house characterization techniques are also reported. It can be revealed that RV$_6$Sn$_6$ (R = Tb, Dy, Ho) have a collinear ferromagnetic order in the ground state, with the ordered magnetic moment aligned along the c axis for R = Tb, Ho, while approximately 20${^\circ}$ tilted off from the c axis for R = Dy. In contrast, ErV$_6$Sn$_6$ shows an A-type antiferromagnetic structure with a magnetic propagation vector k = (0, 0, 0.5), and with the ordered magnetic moment aligned in the ab plane. A comparison of the low-temperature magnetic structures for both the extensively investigated topological kagome metal series of RV$_6$Sn$_6$ and RMn$_6$Sn$_6$ is given in details. This allows to gain new insights into the complex magnetic interactions, diverse single-ion magnetic anisotropies and spin dynamics in these compounds. The reported ground-state magnetic structures in RV$_6$Sn$_6$ (R = Tb, Dy, Ho, Er) can pave the way for further explorations of the possible interplay between magnetism and topologically non-trivial electron band structures in the magnetically ordered phase regime., Comment: To appear in Phys. Rev. Research

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: Updating author information, the submission remains largely unchanged. 98 pages total

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

We present the observations of a quiescent filament eruption and its deflection from the radial direction. The event occurred in the southern solar hemisphere on 2021 May 9 and was observed by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), by the STEREO A Observatory and GONG. Part of the filament erupted in the west direction, while major part of the filament deviated towards east direction. LASCO observed a very weak CME towards the west direction where it faded quickly. Moreover, the eruption was associated with CME observed by STEREO A COR1 and COR2. Our observations provide the evidence that the filament eruption was highly non-radial in nature., Comment: Accepted in the Proceedings of IAUS 388

In this study, we seek to understand how macroeconomic factors such as GDP, inflation, Unemployment Insurance, and S&P 500 index; as well as microeconomic factors such as health, race, and educational attainment impacted the unemployment rate for about 20 years in the United States. Our research question is to identify which factor(s) contributed the most to the unemployment rate surge using linear regression. Results from our studies showed that GDP (negative), inflation (positive), Unemployment Insurance (contrary to popular opinion; negative), and S&P 500 index (negative) were all significant factors, with inflation being the most important one. As for health issue factors, our model produced resultant correlation scores for occurrences of Cardiovascular Disease, Neurological Disease, and Interpersonal Violence with unemployment. Race as a factor showed a huge discrepancies in the unemployment rate between Black Americans compared to their counterparts. Asians had the lowest unemployment rate throughout the years. As for education attainment, results showed that having a higher education attainment significantly reduced one chance of unemployment. People with higher degrees had the lowest unemployment rate. Results of this study will be beneficial for policymakers and researchers in understanding the unemployment rate during the pandemic.

Quantum theory contravenes classical macrorealism by allowing a system to be in a superposition of two or more physically distinct states, producing physical consequences radically different from that of classical physics. We show that a system, upon subjecting to transform under superposition of unitary operators, exhibits enhanced non-macrorealistic feature - as quantified by violation of the Leggett-Garg inequality (LGI) beyond the temporal Tsirelson bound. Moreover, this superposition of unitaries also provides robustness against decoherence by allowing the system to violate LGI and thereby retain its non-macrorealistic behavior for a strikingly longer duration. Using an NMR register, we experimentally demonstrate the superposition of unitaries with the help of an ancillary qubit and verify these theoretical predictions.

Relativistic accretors are cosmic objects that pull matter from their surroundings at speeds almost equal to the light's speed. Because of the tremendous gravitational force from the accretors and the angular momentum of infalling material, which often result in discs of gas and dust that are heated to extremely high temperatures. We encounter strong radiation throughout the electromagnetic spectrum, including intense X-rays. The X-ray view provides a unique window into the behavior of accretors. In this review, we discuss different accretors, particularly binaries, and their origin, involved mechanisms, and properties, including energy spectra and the variability of X-rays from the accretors. This X-ray perspective gives a unique insight into the evolution and connections of these systems with their environment. Future research in this area is necessary to fully understand the process underlying X-ray emission from relativistic accretors., Comment: 8 pages, 3 figures, 2 tables, The manuscript is accepted for publication as a conference paper in National Conference "Physics Frontiers: Bridging Theory and Experiment-2024" organized by Bhawanipur Anchalik College, Assam, India

Scalar Non-Standard Interactions (SNSI) in neutrinos can arise when a scalar mediator couples to both neutrinos and standard model fermions. This beyond the Standard Model (BSM) scenario is particularly interesting as the SNSI contribution appears as a density-dependent perturbation to the neutrino mass, rather than appearing as a matter-induced potential, and the neutrino oscillation probabilities uniquely depend on the absolute neutrino masses. In this work, we show the complex dependence of the SNSI contributions on the neutrino masses and discuss how the mass of the lightest neutrino would regulate any possible SNSI contribution in both mass ordering scenarios. We derive the analytic expressions for neutrino oscillation probabilities, employing the Cayley-Hamilton theorem, in the presence of diagonal elements of SNSI. The expressions are compact and shows explicit dependence on matter effects and the absolute neutrino masses. The analytic expressions calculated here allow us to obtain the dependence of the SNSI contribution on mass terms of the form $m_1 + m_2$, $m_2 - m_1$, $m_1c_{12}^2 + m_2s_{12}^2,$ $ m_1s_{12}^2 + m_2c_{12}^2$, and $m_3$. We then explore the non-trivial impact of neutrino mass ordering on the SNSI contribution. The dependence of the SNSI contribution on the 3$\nu$ parameters is then thoroughly explored using our analytic expressions., Comment: 40 pages, 11 figures

In this work, we present the transverse momentum spectra of prompt and decay photons in Au-Au collisions for $\sqrt{s_{NN}}=$ 200 GeV, 62.4 GeV, 39 GeV, and 27 GeV. The major sources of the photons in Angantyr include hard processes, Parton showers, and resonance decay. The multiparton interactions and hadronic rescatterings significantly increase the photon yield. The model shows a good match with the available experimental data at high $p_T$. The difference in yield at low $p_T $ suggests that Quark Gluon Plasma of $T_{eff}$ = 0.167 GeV/c in central Au-Au collision at 200 GeV is formed, the new effective temperature is less than the ones extracted without removing background photons. At low $p_T$ the decay photon spectra scales with $(\frac{dN_{ch}}{d\eta})^{1.25}$, the scaling is independent of collision energy and system size. The scaling no longer holds at high $p_T$ and the spectra become beam energy dependent. The scaled $p_T$ spectra of p-p and d-Au collisions show an opposite trend at high $p_T$, their scaled yield is greater than the Au-Au collision at the same energy.

Accretion physics studies the process of gravitational capture of ambient matter by massive stars. The background processes are very challenging to observe and measure due to the extreme conditions in these systems. Numerical simulations play a crucial role in accretion physics because they provide the only practical method to model the complex processes occurring in accretion disks. In this review, we outline different branches of numerical simulations, such as hydrodynamic simulations, magnetohydrodynamic simulations, and Monte-Carlo simulations, and their methodology, and we discuss possible implications for modeling accretion physics around black holes, neutron stars, and protoplanetary disks., Comment: The manuscript is accepted for publication as a Conference paper in Physics Frontiers, Bhawanipur Anchalik College, 2024, 5 pages, 1 figure

Accretion disc outbursts are re-occurring events observed in various astrophysical systems, including X-ray binaries and cataclysmic variables. These outbursts are characterized by a sudden increase in luminosity due to various instabilities in the accretion disc. We need to investigate the time-dependent accretion flow models to understand the mechanisms driving these outbursts. Time-dependent models incorporate the disc's time evolution and can capture the build-up of instabilities. This review aims to give a basic overview of accretion disc outburst and stability analysis. The paper highlights the necessity of considering the hierarchy of different timescales, dynamical, viscous, and thermal, when investigating the instabilities occurring in the accretion disc. The importance and observational implications of studying these accretion disc outbursts are also discussed., Comment: 4 pages, 1 figure, The manuscript is accepted for publication as a Conference paper in National Conference "Physics Frontiers: Bridging Theory and Experiment-2024" organized by Bhawanipur Anchalik College, Assam, India

For the present work, we have used the HYDJET++ model to explore the production of pure multi-strange hadrons in Pb+Pb collisions at $\sqrt{s_{NN}}$= 2.76 TeV and $\sqrt{s_{NN}}$= 5.02 TeV collision energies, respectively. We have simulated the $p_{T}$-spectra and elliptic flow ($v_{2}$) of $\phi$-meson and $\Omega$-baryons comparing our results with ALICE experimental data and several phenomenological models (HIJING/$B\overline{B}$, VISHNU, EPOS, AMPT, and Krakow) across various centrality intervals. Additionally, we have calculated the nuclear modification factors ($R_{AA}$ and $R_{CP}$), which provide a perception of jet quenching phenomena. Hence, our findings enable the study of the energy and system dependence of $\phi$ and $\Omega$ hadrons production over a wide range of ultra-relativistic collision energies. We also present the particle ratios ($\Omega/\phi$, $\overline{\Omega}^{+}/\Omega^{-}$, $\Omega/\pi$, and $\phi/\pi$), offering insights on the strangeness enhancement and chemical properties of the medium at both LHC collision energies., Comment: 12 pages, 12 figures, and 2 tables

In this paper, an algorithm for Quantum Inverse Fast Fourier Transform (QIFFT) is developed to work for quantum data. Analogous to a classical discrete signal, a quantum signal can be represented in Dirac notation, application of QIFFT is a tensor transformation from frequency domain to time domain. If the tensors are merely complex entries, then we get the classical scenario. We have included the complete formulation of QIFFT algorithm from the classical model and have included butterfly diagram. QIFFT outperforms regular inversion of Quantum Fourier Transform (QFT) in terms of computational complexity, quantum parallelism and improved versatility., Comment: 3 pages, 4 figures, works for vectors with manual calculations, need to build a model with Qiskit and update it if needed

The ocean ecology is badly impacted by large-scale oil spills, plastic waste, and chemical pollution, which destroy ecosystems and endanger marine life. Acknowledging the detrimental effects of oil spills on ecosystems, our research aims to establish the foundation for creative methods to lessen their impact. With an emphasis on the containment and prediction of oil spills, this research investigates the potential of acoustic levitation as a cutting-edge technique for environmental cleanup. Effectively separating and eliminating pollutants without causing additional ecological harm is a major issue for traditional oil spill cleanup techniques. Acoustic levitation provides a non-invasive, accurate, and effective alternative by using sound waves to precisely and subtly separate oil droplets from water in controlled environments. This proposed approach can reduce the negative effects on the environment and increase the efficacy of cleanup efforts. The findings have been examined and assessed by proof of concept experiments with oil droplets, identifying the relationship between the intensity of ultrasonic pressure and the proportion of oil droplets collected.

Recent increases in endpoint-based security events and threats compelled enterprise operations to switch to virtual desktop infrastructure and web-based applications. In addition to reducing potential hazards, this has guaranteed a consistent desktop environment for every user. On the other hand, the attack surface is greatly increased because all endpoints are connected to the company network, which could harbor malware and other advanced persistent threats. This results in a considerable loss of system resources on each individual endpoint. Hence our work proposes a standard called Colaboot that enables machines throughout a company to boot from a single operating system in order to address these problems and guarantee a consistent operating system environment that could be easily updated to the most recent security patches across all work stations.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.