arXiv subjects

Sandeep Kumar

Publications and source records attributed to Sandeep Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2024

Taming Server Memory TCO with Multiple Software-Defined Compressed Tiers

Memory accounts for 33 - 50% of the total cost of ownership (TCO) in modern data centers. We propose a novel solution to tame memory TCO through the novel creation and judicious management of multiple software-defined compressed memory tiers. As opposed to the state-of-the-art solutions that employ a 2-Tier solution, a single compressed tier along with DRAM, we define multiple compressed tiers implemented through a combination of different compression algorithms, memory allocators for compressed objects, and backing media to store compressed objects. These compressed memory tiers represent distinct points in the access latency, data compressibility, and unit memory usage cost spectrum, allowing rich and flexible trade-offs between memory TCO savings and application performance impact. A key advantage with ntier is that it enables aggressive memory TCO saving opportunities by placing warm data in low latency compressed tiers with a reasonable performance impact while simultaneously placing cold data in the best memory TCO saving tiers. We believe our work represents an important server system configuration and optimization capability to achieve the best SLA-aware performance per dollar for applications hosted in production data center environments. We present a comprehensive and rigorous analytical cost model for performance and TCO trade-off based on continuous monitoring of the application's data access profile. Guided by this model, our placement model takes informed actions to dynamically manage the placement and migration of application data across multiple software-defined compressed tiers. On real-world benchmarks, our solution increases memory TCO savings by 22% - 40% percentage points while maintaining performance parity or improves performance by 2% - 10% percentage points while maintaining memory TCO parity compared to state-of-the-art 2-Tier solutions.

cs.OS↗

arXiv2024

Oscillatory Hall effect from magnetoelectronic coupling in flexoelectronic silicon

The magnetoelectronic coupling can be defined as cross-domain coupling between electronic and magnetic properties, where modulation in magnetic properties changes the electronic properties. In this letter, an explicit experimental evidence of magnetoelectronic coupling is presented, which is uncovered from oscillatory Hall effect response in Hall measurement. The strain gradient in a MgO (1.8 nm)/p-Si (~400 nm) freestanding sample leads to transfer of electrons (~5X10^18 cm^-3) from valence to conduction band due to flexoelectronic charge separation in the p-Si layer. The resulting flexoelectronic polarization gives rise to temporal magnetic moment from dynamical multiferroicity. The external magnetic field changes the net temporal magnetic moment, which causes modulations in charge carrier concentration and oscillatory Hall effect. The period of oscillatory Hall response is 1.12 T, which is attributed to the magnitude of temporal magnetic moment. The discovery of oscillatory Hall effect adds a new member to the family of Hall effects.

cond-mat.mes-hall↗

arXiv2024

From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility

This groundbreaking study explores the expanse of Large Language Models (LLMs), such as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) across varied domains ranging from technology, finance, healthcare to education. Despite their established prowess in Natural Language Processing (NLP), these LLMs have not been systematically examined for their impact on domains such as fitness, and holistic well-being, urban planning, climate modelling as well as disaster management. This review paper, in addition to furnishing a comprehensive analysis of the vast expanse and extent of LLMs' utility in diverse domains, recognizes the research gaps and realms where the potential of LLMs is yet to be harnessed. This study uncovers innovative ways in which LLMs can leave a mark in the fields like fitness and wellbeing, urban planning, climate modelling and disaster response which could inspire future researches and applications in the said avenues.

cs.CL↗

arXiv2024

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.

cs.CV↗

arXiv2024

Heating from Above in Non-scattering Suspensions: Phototactic Bioconvection under Collimated Irradiation

Examining phototactic bioconvection in non-scattering suspensions with upper heating and collimated irradiation, this study delves into the intricate dynamics influenced by light and microorganisms. The study focuses on the linear stability of the basic state, examining neutral curves. The numerical analysis involves solving a system of equations using the MATLAB bvp4c solver. The investigation considers the impact of parameters, such as the thermal Rayleigh number, critical total intensity, and Lewis number, on the critical bioconvection Rayleigh number. As the critical total intensity varies, a transition from a stationary to an oscillatory solution (and vice versa) is observed. Phototactic microorganisms are incorporated into the model, and results show how varying parameters affect convection patterns and stability. The findings reveal interesting phenomena, including Hopf bifurcations and limit cycles.

math.DS↗

arXiv2023

No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.) at the central server. This paper addresses the pervasive issue of inherent bias within RSs for different demographic groups without compromising the privacy of sensitive user attributes in FL environment with the graph-based model. To address the group bias, we propose F2PGNN (Fair Federated Personalized Graph Neural Network), a novel framework that leverages the power of Personalized Graph Neural Network (GNN) coupled with fairness considerations. Additionally, we use differential privacy techniques to fortify privacy protection. Experimental evaluation on three publicly available datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47% - 99% compared to the state-of-the-art while preserving privacy and maintaining the utility. The results validate the significance of our framework in achieving equitable and personalized recommendations using GNN within the FL landscape.

cs.IR↗

arXiv2023

Best practices for machine learning in antibody discovery and development

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to antibody discovery and help accelerate the development of drug products using a reduced number of experiments and hence cost. Over the past few years, we have observed rapid developments in the field of ML-guided antibody discovery and development (D&D). However, many of the results are difficult to compare or hard to assess for utility by other experts in the field due to the high diversity in the datasets and evaluation techniques and metrics that are across industry and academia. This limitation of the literature curtails the broad adoption of ML across the industry and slows down overall progress in the field, highlighting the need to develop standards and guidelines that may help improve the reproducibility of ML models across different research groups. To address these challenges, we set out in this perspective to critically review current practices, explain common pitfalls, and clearly define a set of method development and evaluation guidelines that can be applied to different types of ML-based techniques for therapeutic antibody D&D. Specifically, we address in an end-to-end analysis, challenges associated with all aspects of the ML process and recommend a set of best practices for each stage.

q-bio.BM↗

arXiv2023

Telescope: Telemetry at Terabyte Scale

Data-hungry applications that require terabytes of memory have become widespread in recent years. To meet the memory needs of these applications, data centers are embracing tiered memory architectures with near and far memory tiers. Precise, efficient, and timely identification of hot and cold data and their placement in appropriate tiers is critical for performance in such systems. Unfortunately, the existing state-of-the-art telemetry techniques for hot and cold data detection are ineffective at the terabyte scale. We propose Telescope, a novel technique that profiles different levels of the application's page table tree for fast and efficient identification of hot and cold data. Telescope is based on the observation that, for a memory- and TLB-intensive workload, higher levels of a page table tree are also frequently accessed during a hardware page table walk. Hence, the hotness of the higher levels of the page table tree essentially captures the hotness of its subtrees or address space sub-regions at a coarser granularity. We exploit this insight to quickly converge on even a few megabytes of hot data and efficiently identify several gigabytes of cold data in terabyte-scale applications. Importantly, such a technique can seamlessly scale to petabyte-scale applications. Telescope's telemetry achieves 90%+ precision and recall at just 0.009% single CPU utilization for microbenchmarks with a 5 TB memory footprint. Memory tiering based on Telescope results in 5.6% to 34% throughput improvement for real-world benchmarks with a 1-2 TB memory footprint compared to other state-of-the-art telemetry techniques.

cs.OS↗

arXiv2023

Thermal-bioconvection in a non-scattering suspension of phototactic microorganisms

This article investigates the linear stability of thermal-bioconvection within a suspension containing phototactic microorganisms heated from below. In suspension, the upper surface is taken as stress-free, while the lower surface is taken as rigid. The resulting eigenvalue problem, including the bioconvection Rayleigh and thermal Rayleigh numbers, is resolved numerically. Changes in the critical total intensity and Lewis number do not impact the critical threshold of the thermal Rayleigh number; however, they notably influence the critical bioconvection Rayleigh number. The critical total intensity and Lewis number destabilize the suspension. It is observed that heating from below enhances the instability of the layer. At higher temperatures, Rayleigh-B$\acute{e}$nard convection dominates bioconvection, resulting in a single convection cell.

physics.flu-dyn↗

arXiv2023

Effect of thermal-bioconvection in a rotating phototactic medium

In this article, thermal bioconvection in a rotating phototactic medium is analyzed with a stress-free top and rigid upper boundary. The suspension rotates with a uniform angular velocity around a vertical axis. Utilizing MATLAB's bvp$4$c solver, neutral and growth rate curves are analyzed, emphasizing the impacts of parameters such as the Taylor number, thermal Rayleigh number, Lewis number, and critical total intensity. It is observed that generally critical bioconvection Rayleigh number increases with increasing Taylor number and Lewis number while decreasing with higher thermal Rayleigh number and critical total intensity. The critical thermal Rayleigh number appears to remain unaffected by variations in the critical total intensity and Lewis number. However, it is notably influenced by changes in the Taylor number.

physics.flu-dyn↗

arXiv2023

On the Effective throughput of Shadowed Beaulieu-Xie fading channel

Given the imperative for advanced wireless networks in the next generation and the rise of real-time applications within wireless communication, there is a notable focus on investigating data rate performance across various fading scenarios. This research delved into analyzing the effective throughput of the shadowed Beaulieu-Xie (SBX) composite fading channel using the PDF-based approach. To get the simplified relationship between the performance parameter and channel parameters, the low-SNR and the high-SNR approximation of the effective rate are also provided. The proposed formulations are evaluated for different values of system parameters to study their impact on the effective throughput. Also, the impact of the delay parameter on the EC is investigated. Monte-Carlo simulations are used to verify the facticity of the deduced equations.

cs.IT↗

arXiv2023

Ultrafast THz probing of nonlocal orbital current in transverse multilayer metallic heterostructures

THz generation from femtosecond photoexcited spintronic heterostructures has recently become a versatile tool for investigating ultrafast spin-transport and transient charge-current in a non-contact and non-invasive manner. The same from the orbital effects is still in the primitive stage. Here, we experimentally demonstrate orbital-to-charge current conversion in metallic heterostructures, consisting of a ferromagnetic layer adjacent to either a light or a heavy metal layer, through detection of the emitted THz pulses. Temperature-dependent experiments help to disentangle the orbital and spin components that are manifested in the respective Hall-conductivities, contributing to THz emission. NiFe/Nb shows the strongest inverse orbital Hall effect with an experimentally extracted value of effective Hall-conductivity, σ_SOH^int^eff ~ 195 Ω^(-1){cm}^(-1), while CoFeB/Pt shows maximum contribution from the inverse spin Hall effect. In addition, we observe nearly ten-fold enhancement in the THz emission due to pronounced orbital-transport in W-insertion heavy metal layer in CoFeB/W/Ta heterostructure as compared to the CoFeB/Ta bilayer counterpart.

cond-mat.mtrl-sci↗

arXiv2023

Pseudorandomness of the Schrödinger map equation

We present the random behaviour of the Schrödinger map equation, a geometric partial differential equation, by considering its evolution for regular polygonal curves in both Euclidean and hyperbolic spaces. The results obtained are consistent with those for the vortex filament equation, an equivalent form of the Schrödinger map equation in the Euclidean space, and thus, provide a novel extension to its usefulness as a pseudorandom number generator.

math-ph↗

arXiv2023

When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews

To this date, the efficacy of the scientific publishing enterprise fundamentally rests on the strength of the peer review process. The journal editor or the conference chair primarily relies on the expert reviewers' assessment, identify points of agreement and disagreement and try to reach a consensus to make a fair and informed decision on whether to accept or reject a paper. However, with the escalating number of submissions requiring review, especially in top-tier Artificial Intelligence (AI) conferences, the editor/chair, among many other works, invests a significant, sometimes stressful effort to mitigate reviewer disagreements. Here in this work, we introduce a novel task of automatically identifying contradictions among reviewers on a given article. To this end, we introduce ContraSciView, a comprehensive review-pair contradiction dataset on around 8.5k papers (with around 28k review pairs containing nearly 50k review pair comments) from the open review-based ICLR and NeurIPS conferences. We further propose a baseline model that detects contradictory statements from the review pairs. To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically. We make our dataset and code public for further investigations.

cs.CL↗

arXiv2023

Rotation of a Stealth CME on 2012 October 5 Observed in the Inner Heliosphere

Coronal Mass Ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties. This is because CMEs interact with the ambient solar wind and other large-scale magnetic field structures. In this work, we report on the observations of the 2012 October 5 stealth CME using coronagraphic and heliospheric images. We find clear evidence of a continuous rotation of the CME, i.e., an increase in the tilt angle, estimated using the Graduated Cylindrical Shell (GCS) reconstruction at different heliocentric distances, up to 58 solar radii. We find a further increase in the tilt at L1 estimated from the toroidal and cylindrical flux rope fitting on the in situ observations of IMF and solar wind parameters. This study highlights the importance of observations of Heliospheric Imager (HI), onboard the Solar TErrestrial RElations Observatory (STEREO). In particular, the GCS reconstruction of CMEs in HI field-of-view promises to bridge the gap between the near-Sun and in-situ observations at the L1. The changes in the CME tilt has significant implications for the space weather impact of stealth CMEs.

astro-ph.SR↗

arXiv2023

Inhibitory feedback from the motor circuit gates mechanosensory processing in C. elegans

Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously we developed high throughput methods to probe neural activity in populations of Caenorhabditis elegans and discovered that the animal's mechanosensory processing is rapidly modulated by the animal's locomotion. Specifically we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here we report that C. elegans use inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning associated neurons SAA, RIV and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude that C. elegans rely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas.

q-bio.NC↗

arXiv2023

Free Lunch for Privacy Preserving Distributed Graph Learning

Learning on graphs is becoming prevalent in a wide range of applications including social networks, robotics, communication, medicine, etc. These datasets belonging to entities often contain critical private information. The utilization of data for graph learning applications is hampered by the growing privacy concerns from users on data sharing. Existing privacy-preserving methods pre-process the data to extract user-side features, and only these features are used for subsequent learning. Unfortunately, these methods are vulnerable to adversarial attacks to infer private attributes. We present a novel privacy-respecting framework for distributed graph learning and graph-based machine learning. In order to perform graph learning and other downstream tasks on the server side, this framework aims to learn features as well as distances without requiring actual features while preserving the original structural properties of the raw data. The proposed framework is quite generic and highly adaptable. We demonstrate the utility of the Euclidean space, but it can be applied with any existing method of distance approximation and graph learning for the relevant spaces. Through extensive experimentation on both synthetic and real datasets, we demonstrate the efficacy of the framework in terms of comparing the results obtained without data sharing to those obtained with data sharing as a benchmark. This is, to our knowledge, the first privacy-preserving distributed graph learning framework.

cs.LG↗

arXiv2023

Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

cond-mat.mtrl-sci↗