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Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines., Comment: 19 Pages, 6 Figures

Fluids under extreme confinement exhibit distinctly new properties compared to their bulk analogs. Understanding the structure and intermolecular bonding of confined water lays the foundation for creating and improving applications at the water-energy nexus. However, probing confined water experimentally at the length scale of intermolecular and surface forces has remained a challenge. Here, we report a combined experiment/theory framework to reveal changes in H-bonding environment and the underlying molecular structure of confined water inside individual carbon nanotubes. H-bonding is directly probed through the O-H stretch frequency with vibrational electron energy-loss spectroscopy and compared to spectra from molecular-dynamics simulations based on density-functional-theory. Experimental spectra show that water in larger carbon nanotubes exhibit the bonded O-H vibrations of bulk water, but at smaller diameters, the frequency blueshifts to near the 'free' O-H stretch found in water vapor and hydrophobic surfaces. The matching simulations reveal that, in addition to steric confinement, the tube's vibrations play a key role in breaking up the H-bond network, resulting in an orientationally-dispersed, non-H-bonded phase. Furthermore, the temperature-dependence of the vibrations is investigated, providing insights into phase transitions and the confined-water density. This research demonstrates the potential of the experiment/theory framework to explore unprecedented aspects of structure and bonding in confined fluids.

This paper uses Lusztig varieties to give central elements of the Iwahori-Hecke algebra corresponding to unipotent conjugacy classes in the finite Chevalley group $GL_n(\mathbb{F}_q)$. We explain how these central elements are related to Macdonald polynomials and how this provides a framework for generalizing integral form and modified Macdonald polynomials to Lie types other than $GL_n$. The key steps are to recognize (a) that counting points in Lusztig varieties is equivalent to computing traces on the Hecke algebras, (b) that traces on the Hecke algebra determine elements of the center of the Hecke algebra, (c) that the Geck-Rouquier basis elements of the center of the Hecke algebra produce an `expansion matrix', (d) that the parabolic subalgebras of the Hecke algebra produce a `contraction matrix' and (e) that the combination `expansion-contraction' is the plethystic transformation that relates integral form Macdonald polynomials and modified Macdonald polynomials.

Ferrofluids kept in place by permanent magnet quadrupoles can act as liquid walls to surround a second non-magnetic inside, resulting in a liquid fluidic channel with diameter size ranging from mm down to less than 10 micrometer. Micro particle tracking velocimetry (micro PTV) experiments and modeling show that near ideal plug flow is possible in such liquid-in-liquid channels due to the reduced friction at the walls. The measured fluids velocity profiles agree with the predictions of a hydrodynamic model of cylindrical symmetry with a minimal set of hypotheses. By introducing symmetry breaking elements in the system, we show how unique velocity and flow properties can be obtained. Our liquid-in-liquid confinement opens new possibilities for < 10 micrometer-sized microfluidics with low pressures and low shear, with flow characteristics not attainable in comparable solid-wall devices., Comment: 12 pages 7 figures

The accuracy of end-to-end (E2E) automatic speech recognition (ASR) models continues to improve as they are scaled to larger sizes, with some now reaching billions of parameters. Widespread deployment and adoption of these models, however, requires computationally efficient strategies for decoding. In the present work, we study one such strategy: applying multiple frame reduction layers in the encoder to compress encoder outputs into a small number of output frames. While similar techniques have been investigated in previous work, we achieve dramatically more reduction than has previously been demonstrated through the use of multiple funnel reduction layers. Through ablations, we study the impact of various architectural choices in the encoder to identify the most effective strategies. We demonstrate that we can generate one encoder output frame for every 2.56 sec of input speech, without significantly affecting word error rate on a large-scale voice search task, while improving encoder and decoder latencies by 48% and 92% respectively, relative to a strong but computationally expensive baseline., Comment: Accepted to 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)

Motivated by systems where a high temperature non-Fermi liquid gives way to low temperature $\mathbb{Z}_3$ Potts nematic order, we studied a three-orbital Sachdev-Ye-Kitaev (SYK) model in the large-$N$ limit. In the single-site limit, this model exhibits a spontaneous orbital-selective transition which preserves average particle-hole symmetry, with two orbitals becoming insulators while the third orbital remains a non-Fermi liquid down to zero temperature. We extend this study to lattice models of three-orbital SYK dots, exploring uniform symmetry broken states on the triangular and cubic lattices. At high temperature, these lattice models exhibit an isotropic non-Fermi liquid metal phase. On the three-dimensional (3D) cubic lattice, the low temperature uniform $\mathbb{Z}_3$ nematic state corresponds to an orbital selective layered state which preserves particle-hole symmetry at small hopping and spontaneously breaks the particle-hole symmetry at large hopping. Over a wide range of temperature, the transport in this layered state shows metallic in-plane resistivity but insulating out-of-plane resistivity. On the 2D triangular lattice, the low temperature state with uniform orbital order is also a correlated $\mathbb{Z}_3$ nematic with orbital-selective transport but it remains metallic in both principal directions. We discuss a Landau theory with $\mathbb{Z}_3$ clock terms which captures salient features of the phase diagram and nematic order in all these models. We also present results on the approximate wavevector dependent orbital susceptibility of the isotropic non-Fermi liquid states., Comment: 23 pages, 23 figures

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary.We also present a study that assesses the quality of explanations created with this approach, Comment: arXiv admin note: substantial text overlap with arXiv:2312.02105

The photoassociation (PA) of rubidium dimer (Rb2) in a dark magneto-optic trap (MOT) is studied using atom-cavity collective strong coupling. This allows non-destructive detection of the molecule formation process as well as rapid and repeated interrogation of the atom-molecule system. The vacuum Rabi splitting (VRS) measurements from the bright MOT are carefully calibrated against equivalent measurements with fluorescence. Further loading rates in dark MOT are determined using VRS. This method provides a reliable, fast, and non-destructive detection scheme for ultracold molecules when the atoms are non-fluorescing using the free atoms coupled to a cavity.

Gd2GaSbO7 and Gd2InSbO7 pyrochlore compounds exhibit quantum fluctuations in a spin ice-like state. These compounds have not been adequately studied based on the concept of magnetic frustration. Here, we have synthesised and characterised Gd2GaSbO7 and Gd2InSbO7 to investigate their ground-state magnetic properties. We confirmed the cubic pyrochlore structure via X-ray powder diffraction. Magnetic and heat capacity measurements show an absence of magnetic order down to 400 mK. Interestingly, the low-temperature magnetic heat capacity of Gd2GaSbO7 and Gd2InSbO7 exhibits a broad maximum around T_{\text{max}} = 0.9 K and T_{\text{max}} = 1 K, respectively. The low-temperature Cmag data fit with the power-law C_{\text{mag}} \approx T^{\frac{d}{n}} with dependencies of T^{1.71} for Gd2GaSbO7 and T^{1.49} for Gd2InSbO7, suggesting spin fluctuations caused by the disorder of magnetic moments at low temperatures. Both compounds retain residual entropy akin to water ice, resembling the spin ice state. Additionally, we analysed the ratio between nearest-neighbour exchange and dipolar interaction for all reported Gd-based pyrochlores, finding that Gd2InSbO7 has the lowest and weakest exchange interaction among them. This indicates that chemical pressure affects exchange interaction due to the different pathways of mediators between magnetic ions. This study reveals robust quantum fluctuations in the ground state in GGSO and GISO, similar to quantum spin ice compounds such as Pr2Zr2O7, Pr2Sn2O7, and Yb2GaSbO7. Other Gd-pyrochlore compounds exhibit long-range magnetic order, which is in stark contrast to this., Comment: NIL

Given multi-category point sets from different place-types, our goal is to develop a spatially-lucid classifier that can distinguish between two classes based on the arrangements of their points. This problem is important for many applications, such as oncology, for analyzing immune-tumor relationships and designing new immunotherapies. It is challenging due to spatial variability and interpretability needs. Previously proposed techniques require dense training data or have limited ability to handle significant spatial variability within a single place-type. Most importantly, these deep neural network (DNN) approaches are not designed to work in non-Euclidean space, particularly point sets. Existing non-Euclidean DNN methods are limited to one-size-fits-all approaches. We explore a spatial ensemble framework that explicitly uses different training strategies, including weighted-distance learning rate and spatial domain adaptation, on various place-types for spatially-lucid classification. Experimental results on real-world datasets (e.g., MxIF oncology data) show that the proposed framework provides higher prediction accuracy than baseline methods., Comment: SIAM International Conference on Data Mining (SDM24)

In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction models. However, the algorithmic aspects of measuring model calibration have remained relatively less well-explored. Motivated by [BGHN23], which proposed a rigorous framework for measuring distances to calibration, we initiate the algorithmic study of calibration through the lens of property testing. We define the problem of calibration testing from samples where given $n$ draws from a distribution $\mathcal{D}$ on $(predictions, binary outcomes)$, our goal is to distinguish between the case where $\mathcal{D}$ is perfectly calibrated, and the case where $\mathcal{D}$ is $\varepsilon$-far from calibration. We make the simple observation that the empirical smooth calibration linear program can be reformulated as an instance of minimum-cost flow on a highly-structured graph, and design an exact dynamic programming-based solver for it which runs in time $O(n\log^2(n))$, and solves the calibration testing problem information-theoretically optimally in the same time. This improves upon state-of-the-art black-box linear program solvers requiring $\Omega(n^\omega)$ time, where $\omega > 2$ is the exponent of matrix multiplication. We also develop algorithms for tolerant variants of our testing problem improving upon black-box linear program solvers, and give sample complexity lower bounds for alternative calibration measures to the one considered in this work. Finally, we present experiments showing the testing problem we define faithfully captures standard notions of calibration, and that our algorithms scale efficiently to accommodate large sample sizes.

In this work, we present the results from a study using the Giant Meterwave Radio Telescope (GMRT) to search for radio {emission} from planets around three evolved stars namely $\alpha$~Tau, $\beta$~UMi, and $\beta$~Gem. Both $\alpha$~Tau and $\beta$~UMi host massive $\sim$ 6 $M_J$ mass planets at about $\sim$1.4 au from the central star, while $\beta$~Gem is host to a 2.9 $M_J$ mass planet at 1.7 au from the host star. We observe $\alpha$~Tau and $\beta$~ UMi at two u(upgraded)GMRT bands; band~3 (250-500~MHz) and band~4 (550-900~MHz). We also analyzed the archival observations from $\beta$ Gem at 150~MHz from GMRT. We did not detect any radio signals from these systems. At 400~MHz, the 3$\sigma$ upper limit is 87 $\mu$Jy/beam for $\alpha$~Tau~{b} and 77.4 $\mu$Jy/beam for $\beta$~UMi~{b}. From our observations at 650~MHz, we place a 3$\sigma$ upper limit of 28.2 $\mu$Jy/beam for $\alpha$~Tau~b and 33.6 $\mu$Jy/beam for $\beta$~UMi~b. For $\beta$ Gem b, at 150~MHz, we place an upper limit of 2.5 mJy. At 400~MHz and 650~MHz, our observations are the deepest radio images for any exoplanetary system., Comment: 9 pages, 6 figures, accepted at MNRAS

Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, existing algorithms achieving these bounds have prohibitive computational costs for high dimensional data. In this paper, we propose a simple and practical BCO algorithm inspired by the online Newton step algorithm. We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that we call $\kappa$-convex. This class contains a wide range of practically relevant loss functions including linear, quadratic, and generalized linear models. In addition to optimal regret, this method is the most efficient known algorithm for several well-studied applications including bandit logistic regression. Furthermore, we investigate the adaptation of our second-order bandit algorithm to online convex optimization with memory. We show that for loss functions with a certain affine structure, the extended algorithm attains optimal regret. This leads to an algorithm with optimal regret for bandit LQR/LQG problems under a fully adversarial noise model, thereby resolving an open question posed in \citep{gradu2020non} and \citep{sun2023optimal}. Finally, we show that the more general problem of BCO with (non-affine) memory is harder. We derive a $\tilde{\Omega}(T^{2/3})$ regret lower bound, even under the assumption of smooth and quadratic losses.

Iris segmentation is a critical component of an iris biometric system and it involves extracting the annular iris region from an ocular image. In this work, we develop a pixel-level iris segmentation model from a foundational model, viz., Segment Anything Model (SAM), that has been successfully used for segmenting arbitrary objects. The primary contribution of this work lies in the integration of different loss functions during the fine-tuning of SAM on ocular images. In particular, the importance of Focal Loss is borne out in the fine-tuning process since it strategically addresses the class imbalance problem (i.e., iris versus non-iris pixels). Experiments on ND-IRIS-0405, CASIA-Iris-Interval-v3, and IIT-Delhi-Iris datasets convey the efficacy of the trained model for the task of iris segmentation. For instance, on the ND-IRIS-0405 dataset, an average segmentation accuracy of 99.58% was achieved, compared to the best baseline performance of 89.75%.

People aptly exhibit general intelligence behaviors in solving a variety of tasks with flexibility and ability to adapt to novel situations by reusing and applying high-level knowledge acquired over time. But artificial agents are more like specialists, lacking such generalist behaviors. Artificial agents will require understanding and exploiting critical structured knowledge representations. We present a metacognitive generalization framework, Knowledge-Interaction-eXecution (KIX), and argue that interactions with objects leveraging type space facilitate the learning of transferable interaction concepts and generalization. It is a natural way of integrating knowledge into reinforcement learning and is promising to act as an enabler for autonomous and generalist behaviors in artificial intelligence systems.

CodeCompose is an AI-assisted code authoring tool powered by large language models (LLMs) that provides inline suggestions to 10's of thousands of developers at Meta. In this paper, we present how we scaled the product from displaying single-line suggestions to multi-line suggestions. This evolution required us to overcome several unique challenges in improving the usability of these suggestions for developers. First, we discuss how multi-line suggestions can have a 'jarring' effect, as the LLM's suggestions constantly move around the developer's existing code, which would otherwise result in decreased productivity and satisfaction. Second, multi-line suggestions take significantly longer to generate; hence we present several innovative investments we made to reduce the perceived latency for users. These model-hosting optimizations sped up multi-line suggestion latency by 2.5x. Finally, we conduct experiments on 10's of thousands of engineers to understand how multi-line suggestions impact the user experience and contrast this with single-line suggestions. Our experiments reveal that (i) multi-line suggestions account for 42% of total characters accepted (despite only accounting for 16% for displayed suggestions) (ii) multi-line suggestions almost doubled the percentage of keystrokes saved for users from 9% to 17%. Multi-line CodeCompose has been rolled out to all engineers at Meta, and less than 1% of engineers have opted out of multi-line suggestions.

Locomotion gaits are fundamental for control of soft terrestrial robots. However, synthesis of these gaits is challenging due to modeling of robot-environment interaction and lack of a mathematical framework. This work presents an environment-centric, data-driven and fault-tolerant probabilistic Model-Free Control (pMFC) framework that allows for soft multi-limb robots to learn from their environment and synthesize diverse sets of locomotion gaits for realizing open-loop control. Here, discretization of factors dominating robot-environment interactions enables an environment-specific graphical representation where the edges encode experimental locomotion data corresponding to the robot motion primitives. In this graph, locomotion gaits are defined as simple cycles that are transformation invariant, i.e., the locomotion is independent of the starting vertex of these periodic cycles. Gait synthesis, the problem of finding optimal locomotion gaits for a given substrate, is formulated as Binary Integer Linear Programming (BILP) problems with a linearized cost function, linear constraints, and iterative simple cycle detection. Experimentally, gaits are synthesized for varying robot-environment interactions. Variables include robot morphology - three-limb and four-limb robots, TerreSoRo-III and TerreSoRo-IV; substrate - rubber mat, whiteboard and carpet; and actuator functionality - simulated loss of robot limb actuation. On an average, gait synthesis improves the translation and rotation speeds by 82% and 97% respectively. The results highlight that data-driven methods are vital to soft robot locomotion control due to the significant influence of unexpected asymmetries in the system and the dependence of optimal gait sequences on the experimental robot-environment interaction., Comment: This work has been submitted to the IEEE Transactions on Robotics for possible publication. Details: 17 pages, 18 figures, 9 tables

There is growing interest in visual data management systems that support queries with specialized operations ranging from resizing an image to running complex machine learning models. With a plethora of such operations, the basic need to receive query responses in minimal time takes a hit, especially when the client desires to run multiple such operations in a single query. Existing systems provide an ad-hoc approach where different solutions are clubbed together to provide an end-to-end visual data management system. Unlike such solutions, the Visual Data Management System (VDMS) natively executes queries with multiple operations, thus providing an end-to-end solution. However, a fixed subset of native operations and a synchronous threading architecture limit its generality and scalability. In this paper, we develop VDMS-Async that adds the capability to run user-defined operations with VDMS and execute operations within a query on a remote server. VDMS-Async utilizes an event-driven architecture to create an efficient pipeline for executing operations within a query. Our experiments have shown that VDMS-Async reduces the query execution time by 2-3X compared to existing state-of-the-art systems. Further, remote operations coupled with an event-driven architecture enables VDMS-Async to scale query execution time linearly with the addition of every new remote server. We demonstrate a 64X reduction in query execution time when adding 64 remote servers.

The disparate nature of thermal-nonthermal energy partition during flares, particularly during weak flares, is still an open issue. Following the Neupert effect, quantifying the relative yield of X-ray emission in different energy bands can enable inferring the underlying energy release mechanism. During September 20-25, 2021, the Solar Orbiter mission - being closer to the Sun ($\sim$0.6 AU) and having a moderate separation angle (<40$^{\circ}$) from the Sun-Earth line provided a unique opportunity to analyze multi-wavelength emission from $\sim$200 (mostly weak) flares, commonly observed by the Spectrometer Telescope for Imaging X-rays (STIX), STEREO-A, GOES, and SDO observatories. Associating the quotient (q$_{f}$) of hard X-ray fluence (12-20 keV) and soft X-ray flux (4-10 keV) with the peak SXR flux enabled us to identify strongly non-thermal flares. Multi-wavelength investigation of spectral and imaging mode observations of the 20 strongly non-thermal weak flares reveals an inverse relationship of q$_{f}$ with the emission measure (EM) (and density), and a positive relationship with the flare plasma temperature. This indicates that plasma in tenuous loops attains higher temperatures compared to that in the denser loops, in response to nonthermal energy deposition. This is in agreement with the plasma parameters of the coronal loops, as derived by applying the one-dimensional Palermo Harvard (PH) hydrodynamical code to the coronal loop plasma having different initial coronal loop base pressures when subjected to similar heating input. Our investigation, therefore, indicates that the plasma parameters of the flaring loop in the initial phase have a decisive role in thermal-nonthermal energy partitioning., Comment: The Astrophysical Journal - Accepted for Publication

Availability of representations from pre-trained models (PTMs) have facilitated substantial progress in speech emotion recognition (SER). Particularly, representations from PTM trained for paralinguistic speech processing have shown state-of-the-art (SOTA) performance for SER. However, such paralinguistic PTM representations haven't been evaluated for SER in linguistic environments other than English. Also, paralinguistic PTM representations haven't been investigated in benchmarks such as SUPERB, EMO-SUPERB, ML-SUPERB for SER. This makes it difficult to access the efficacy of paralinguistic PTM representations for SER in multiple languages. To fill this gap, we perform a comprehensive comparative study of five SOTA PTM representations. Our results shows that paralinguistic PTM (TRILLsson) representations performs the best and this performance can be attributed to its effectiveness in capturing pitch, tone and other speech characteristics more effectively than other PTM representations., Comment: Accepted to INTERSPEECH 24

Accurate and robust navigation in unstructured environments requires fusing data from multiple sensors. Such fusion ensures that the robot is better aware of its surroundings, including areas of the environment that are not immediately visible but were visible at a different time. To solve this problem, we propose a method for traversability prediction in challenging outdoor environments using a sequence of RGB and depth images fused with pose estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for Traversability with Enhanced Robustness), uses experience data recorded from a receding horizon estimator to train a self-supervised neural network for traversability prediction, eliminating the need for heuristics. Our experiments demonstrate that our method excels at avoiding obstacles, and correctly detects that traversable terrains, such as tall grass, can be navigable. By using a sequence of images, WayFASTER significantly enhances the robot's awareness of its surroundings, enabling it to predict the traversability of terrains that are not immediately visible. This enhanced awareness contributes to better navigation performance in environments where such predictive capabilities are essential., Comment: Accepted for the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

The advent of Large Models marks a new era in machine learning, significantly outperforming smaller models by leveraging vast datasets to capture and synthesize complex patterns. Despite these advancements, the exploration into scaling, especially in the audio generation domain, remains limited, with previous efforts didn't extend into the high-fidelity (HiFi) 44.1kHz domain and suffering from both spectral discontinuities and blurriness in the high-frequency domain, alongside a lack of robustness against out-of-domain data. These limitations restrict the applicability of models to diverse use cases, including music and singing generation. Our work introduces Enhanced Various Audio Generation via Scalable Generative Adversarial Networks (EVA-GAN), yields significant improvements over previous state-of-the-art in spectral and high-frequency reconstruction and robustness in out-of-domain data performance, enabling the generation of HiFi audios by employing an extensive dataset of 36,000 hours of 44.1kHz audio, a context-aware module, a Human-In-The-Loop artifact measurement toolkit, and expands the model to approximately 200 million parameters. Demonstrations of our work are available at https://double-blind-eva-gan.cc.

This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with human conversations across 118 linguistic categories. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone, reinforcing recent findings of LLMs being "more human than human." However, no significant difference was found in positive or negative affect between ChatGPT and human dialogues. Classifier analysis of dialogue embeddings indicates implicit coding of the valence of affect despite no explicit mention of affect in the conversations. The research also contributes a novel, companion ChatGPT-generated dataset of conversations between two independent chatbots, which were designed to replicate a corpus of human conversations available for open access and used widely in AI research on language modeling. Our findings enhance understanding of ChatGPT's linguistic capabilities and inform ongoing efforts to distinguish between human and LLM-generated text, which is critical in detecting AI-generated fakes, misinformation, and disinformation., Comment: Proceedings of the 4th International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI), Jeju, Korea, 2024

The short Gamma Ray Bursts (GRBs) are the aftermath of the merger of binary compact objects (neutron star -- neutron star or neutron star -- black hole systems). With the simultaneous detection of Gravitational Wave (GW) signal from GW 170817 and GRB 170817A, the much-hypothesized connection between GWs and short GRBs has been proved beyond doubt. The resultant product of the merger could be a millisecond magnetar or a black hole depending upon the binary masses and their equation of state. In the case of a magnetar central engine, fraction of the rotational energy deposited to the emerging ejecta produces late time synchrotron radio emission from the interaction with the ambient medium. In this paper, we present an analysis of a sample of short GRBs located at a redshift of $z \leq 0.16$ which were observed at the late time to search for the emission from merger ejecta. Our sample consists of 7 short GRBs which have radio upper limits available from VLA and ATCA observations. We generate the model lightcurves using the standard magnetar model incorporating the relativistic correction. Using the model lightcurves and upper limits we constrain the number density of the ambient medium to be $10^{-5} - 10^{-3} cm^{-3}$ for rotational energy of the magnetar $E_{rot} \sim 5\times10^{51}$ erg. Variation of ejecta mass does not play a significant role in constraining the number density.

Cobalt is used in Li-ion batteries, but it is expensive and could be replaced by nickel to deliver better performance at a lower cost. With this motivation, we discuss how the character of redox orbitals of LiNiO$_2$ can be ascertained through x-ray Compton scattering measurements combined with parallel first-principles simulations. Our analysis reveals the nature of hole states in Li-doped NiO resulting from the hybridization of O 2$p$ and Ni 3$d$ orbitals. Our study also gives insight into the ferromagnetic ground state and provides a pathway toward the rational design of next-generation battery materials., Comment: 5 pages, 5 figures

This review explores machine unlearning (MUL) in recommendation systems, addressing adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL dynamically adjusts system knowledge based on shifts in user preferences and ethical considerations. The paper critically examines MUL's basics, real-world applications, and challenges like algorithmic transparency. It sifts through literature, offering insights into how MUL could transform recommendations, discussing user trust, and suggesting paths for future research in responsible and user-focused artificial intelligence (AI). The document guides researchers through challenges involving the trade-off between personalization and privacy, encouraging contributions to meet practical demands for targeted data removal. Emphasizing MUL's role in secure and adaptive machine learning, the paper proposes ways to push its boundaries. The novelty of this paper lies in its exploration of the limitations of the methods, which highlights exciting prospects for advancing the field., Comment: In Proceedings of 7th INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATION 2024 (https://icicc-conf.com/)

We give a procedure for computing group-level $(\epsilon, \delta)$-DP guarantees for DP-SGD, when using Poisson sampling or fixed batch size sampling. Up to discretization errors in the implementation, the DP guarantees computed by this procedure are tight (assuming we release every intermediate iterate)., Comment: v2: Added links to open-source implementation of PLD accounting for MoG mechanisms

Recent studies on reservoir computing essentially involve a high dimensional dynamical system as the reservoir, which transforms and stores the input as a higher dimensional state, for temporal and nontemporal data processing. We demonstrate here a method to predict temporal and nontemporal tasks by constructing virtual nodes as constituting a reservoir in reservoir computing using a nonlinear map, namely the logistic map, and a simple finite trigonometric series. We predict three nonlinear systems, namely Lorenz, Rossler, and Hindmarsh-Rose, for temporal tasks and a seventh order polynomial for nontemporal tasks with great accuracy. Also, the prediction is made in the presence of noise and found to closely agree with the target. Remarkably, the logistic map performs well and predicts close to the actual or target values. The low values of the root mean square error confirm the accuracy of this method in terms of efficiency. Our approach removes the necessity of continuous dynamical systems for constructing the reservoir in reservoir computing. Moreover, the accurate prediction for the three different nonlinear systems suggests that this method can be considered a general one and can be applied to predict many systems. Finally, we show that the method also accurately anticipates the time series of the all the three variable of Rossler system for the future (self prediction)., Comment: Accepted for publication in Physical Review E

Cloud gaming, wherein game graphics is rendered in the cloud and streamed back to the user as real-time video, expands the gaming market to billions of users who do not have gaming consoles or high-power graphics PCs. Companies like Nvidia, Amazon, Sony and Microsoft are investing in building cloud gaming platforms to tap this large unserved market. However, cloud gaming requires the user to have high bandwidth and stable network connectivity - whereas a typical console game needs about 100-200 kbps, a cloud game demands minimum 10-20 Mbps. This makes the Internet Service Provider (ISP) a key player in ensuring the end-user's good gaming experience. In this paper we develop a method to detect Nvidia's GeForce NOW cloud gaming sessions over their network infrastructure, and measure associated user experience. In particular, we envision ISPs taking advantage of our method to provision network capacity at the right time and in the right place to support growth in cloud gaming at the right experience level; as well as identify the role of contextual factors such as user setup (browser vs app) and connectivity type (wired vs wireless) in performance degradation. We first present a detailed anatomy of flow establishment and volumetric profiles of cloud gaming sessions over multiple platforms, followed by a method to detect gameplay and measure key experience aspects such as latency, frame rate and resolution via real-time analysis of network traffic. The insights and methods are also validated in the lab for XBox Cloud Gaming platform. We then implement and deploy our method in a campus network to capture gameplay behaviors and experience measures across various user setups and connectivity types which we believe are valuable for network operators., Comment: This paper is accepted at Passive and Active Measurement (PAM) conference Mar 2024

Crowd counting finds direct applications in real-world situations, making computational efficiency and performance crucial. However, most of the previous methods rely on a heavy backbone and a complex downstream architecture that restricts the deployment. To address this challenge and enhance the versatility of crowd-counting models, we introduce two lightweight models. These models maintain the same downstream architecture while incorporating two distinct backbones: MobileNet and MobileViT. We leverage Adjacent Feature Fusion to extract diverse scale features from a Pre-Trained Model (PTM) and subsequently combine these features seamlessly. This approach empowers our models to achieve improved performance while maintaining a compact and efficient design. With the comparison of our proposed models with previously available state-of-the-art (SOTA) methods on ShanghaiTech-A ShanghaiTech-B and UCF-CC-50 dataset, it achieves comparable results while being the most computationally efficient model. Finally, we present a comparative study, an extensive ablation study, along with pruning to show the effectiveness of our models.

Grover's quantum search algorithm promises a quadratic speedup for unstructured search over its classical counterpart. But this advantage is gradually reduced with noise acting on the search space. In this article, we demonstrate that a quantum switch can act as a resource operation in mitigating the effect of the noise in the search space. In this scenario, fault-tolerant model quantum computing is costly. In addition to the noise modeled by a depolarizing channel, which coherently acts on the entire quantum register, such an error correction method can not be trivially implemented. We show that a quantum switch can significantly add value by reducing this error. In particular, we propose two frameworks for the application of switches. In the first framework, we apply the superposition of channels' orders in the form of a switch and do a post-selection at every iteration of the applications of the Grover operator. In the second framework, we delay the post-selection until the very end. In other words, if we want to look at the switch's action at the kth step, we already have k-1 post-selection measurements in place for the first framework. In the second case, we only have a single measurement. The number of post selections is minimal in the second scenario, so its effect is more credited to the switch. It also gives a significant advantage regarding the success probability of Grover's algorithm. We take the success probability as the sole quantifier of the switch's action in diminishing the effect of noise in search space., Comment: 26 pages (14 + 12), 7 figures

Measurement incompatibility has proved to be an important resource for information-processing tasks. In this work, we analyze various levels of incompatibility of measurement sets. We provide operational classification of measurement incompatibility with respect to two elementary classical operations, viz., coarse-graining of measurement outcomes and convex mixing of different measurements. We derive analytical criteria for determining when a set of projective measurements is fully incompatible with respect to coarse-graining or convex mixing. Robustness against white noise is investigated for mutually unbiased bases that can sustain full incompatibility. Furthermore, we propose operational witnesses for different levels of incompatibility subject to classical operations, using the input-output statistics of Bell-type experiments as well as experiments in the prepare-and-measure scenario., Comment: 11 pages, Preliminary draft, Comments are welcome

The notions of predictability and visibility are essential in the mathematical formulation of wave particle duality. The work of Jakob and Bergou [Phys. Rev. A 76, 052107] generalises these notions for higher-dimensional quantum systems, which were initially defined for qubits, and subsequently proves a complementarity relation between predictability and visibility. By defining the single-party information content of a quantum system as the addition of predictability and visibility, and assuming that entanglement in a bipartite system in the form of concurrence mutually excludes the single-party information, the authors have proposed a complementarity relation between the concurrence and the single-party information content. We show that the information content of a quantum system defined by Jakob and Bergou is nothing but the Hilbert-Schmidt distance between the state of the quantum system of our consideration and the maximally mixed state. Motivated by the fact that the trace distance is a good measure of distance as compared to the Hilbert-Schmidt distance from the information theoretic point of view, we, in this work, define the information content of a quantum system as the trace distance between the quantum state and the maximally mixed state. We then employ the quantum Pinsker's inequality and the reverse Pinsker's inequality to derive a new complementarity and a reverse complementarity relation between the single-party information content and the entanglement present in a bipartite quantum system in a pure state. As a consequence of our findings, we show that for a bipartite system in a pure state, its entanglement and the predictabilities and visibilities associated with the subsystems cannot be arbitrarily small as well as arbitrarily large., Comment: 9 pages, no figures

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

With the third data release of the Gaia mission $Gaia$ DR3 with its precise photometry and astrometry, it is now possible to study the behaviour of stars at a scale never seen before. In this paper, we developed new criteria to identify T-Tauri stars (TTS) candidates using UV and optical CMDs by combining the GALEX and Gaia surveys. We found 19 TTS candidates and 5 of them are newly identified TTS in the Taurus Molecular Cloud (TMC), not catalogued before as TMC members. For some of the TTS candidates, we also obtained optical spectra from several Indian telescopes. We also present the analysis of the distance and proper motion of young stars in the Taurus using data from $Gaia$ DR3. We found that the stars in Taurus show a bimodal distribution with distance, having peaks at $130.17_{-1.24}^{1.31}$ pc and $156.25_{-5.00}^{1.86}$ pc. The reason for this bimodality, we think, is due to the fact that different clouds in the TMC region are at different distances. We further show that the two populations have similar ages and proper motion distribution. Using the $Gaia$ DR3 colour-magnitude diagram, we show that the age of Taurus is consistent with 1 Myr., Comment: 13 pages, 10 figures