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The advent of wurtzite ferroelectrics is enabling a new generation of ferroelectric devices for computer memory that has the potential to bypass the von Neumann bottleneck, due to their robust polarization and silicon compatibility. However, the microscopic switching mechanism of wurtzites is still undetermined due to the limitations of density functional theory simulation size and experimental temporal and spatial resolution. Thus, physics-informed materials engineering to reduce coercive field and breakdown in these devices has been limited. Here, the atomistic mechanism of domain wall migration and domain growth in wurtzites is uncovered using molecular dynamics and Monte Carlo simulations of aluminum nitride. We reveal the anomalous switching mechanism of fast 1D single columns of atoms propagating from a slow-moving 2D fractal-like domain wall. We find that the critical nucleus in wurtzites is a single aluminum ion that breaks its bond with one nitrogen and bonds to another nitrogen; this creates a cascade that only flips atoms directly in the same column, due to the extreme locality (sharpness) of the domain walls in wurtzites. We further show how the fractal shape of the domain wall in the 2D plane breaks assumptions in the KAI model and leads to the anomalously fast switching in wurtzite structured ferroelectrics.

In $D$ dimensional de Sitter space, a scalar field has an infinite tower of special tachyonic mass values at which enhanced shift symmetries appear. After modding out by these shift symmetries, these fields correspond to the unitary irreducible representations of the de Sitter group known as the discrete series. We show that in $D=2$ these theories have global conformal symmetry. In all but the massless case, these theories have no stress tensor and the conformal symmetry does not act in the usual way on the scalar field. We find the conformal symmetry by explicitly computing the correlators of the shift invariant local operators and showing that they take conformally invariant forms. We also demonstrate how these fields are self dual in $D=2$, and dual to the shift invariant massive vector fields, which are therefore also conformally invariant., Comment: 30 pages

Free-induction-decay (FID) magnetometers have evolved as simple magnetic sensors for sensitive detection of unknown magnetic fields. However, these magnetometers suffer from a fundamental problem known as a "dead zone," making them insensitive to certain magnetic field directions. Here, we demonstrate a simple experimental scheme for the dead-zone-free operation of a FID atomic magnetometer. Using a single laser beam containing equal strength of linear- and circular-polarization components and amplitude-modulation at a low-duty cycle, we have synchronously pumped the rubidium-87 atoms with both first- and second-order frequency harmonics. Such a novel pumping scheme has enabled us to observe the free Larmor precession of atomic spins at a frequency of $\Omega_L$ (orientation) and/or 2$\Omega_L$ (alignment) in a single FID signal, depending on the direction of the external magnetic field. We observed that the amplitude of the FID signal does not go to zero for any magnetic field direction, proving the absence of dead zones in the magnetometer. The magnetometer has a sensitivity in the range of 3.5 - 7.8 pT/$\sqrt{Hz}$ in all directions. Such a generic experimental scheme can play a crucial role in developing miniaturized atomic magnetometers for various practical applications, including geomagnetic applications., Comment: 5 pages, 4 figures

Let $X$ be a smooth projective variety defined over a number field $k$ that has Brauer-Manin obstruction to the existence of rational points. We study the orders of the Brauer classes which capture the obstruction. We prove that one may require elements of arbitrarily large order to capture the obstruction. In other words, the exponent of the group which captures the obstruction has no upper bound., Comment: 8 pages. Comments are welcome

Small-scale Brightenings (SBs) are commonly observed in the transition region that separates the solar chromosphere from the corona. These brightenings, omnipresent in active region patches known as "moss" regions, could potentially contribute to the heating of active region plasma. In this study, we investigate the properties of SB events in a moss region and their associated chromospheric dynamics, which could provide insights into the underlying generation mechanisms of the SBs. We analyzed the data sets obtained by coordinated observations using the Interface Region Imaging Spectrograph and the Goode Solar Telescope at Big Bear Solar Observatory. We studied 131 SB events in our region of interest and found that 100 showed spatial and temporal matches with the dynamics observed in the chromospheric H$\alpha$ images. Among these SBs, 98 of them were associated with spicules that are observed in H$\alpha$ images. Furthermore, detailed analysis revealed that one intense SB event corresponded to an Ellerman Bomb (EB), while another SB event consisted of several recurring brightenings caused by a stream of falling plasma. We observed that H$\alpha$ far wings often showed flashes of strong brightening caused by the falling plasma, creating an H$\alpha$ spectral profile similar to an EB. However, 31 of the 131 investigated SB events showed no noticeable spatial and temporal matches with any apparent features in H$\alpha$ images. Our analysis indicated that the predominant TR SB events in moss regions are associated with chromospheric phenomena primarily caused by spicules. Most of these spicules display properties akin to dynamic fibrils., Comment: 12 pages, 7 figures, accepted for publication in the ApJ

Pressure-induced emission enhancement and bandgap narrowing in vacancy-ordered halide double perovskite $Cs_2TeBr_6$ are extensively investigated through photoluminescence and absorption experiments. The below bandgap broad emission is attributed to self-trapped excitons recombination. The $Cs_2TeBr_6$ crystal, consisting of undistorted octahedra, exhibits substantial emission enhancement due to the lowering of the energy barrier between $^3P_1$ and self-trapped exciton states, as well as the suppression of nonradiative energy loss with increasing pressure. In the Raman measurements, the observed behavior of full width at half maximum of all Raman modes implies dominant electron-phonon interactions rather than anharmonic interactions between phonons. The pressure-dependent X-ray diffraction measurements reveal an anomalous behaviour in the normalized pressure as a function of the Eulerian strain.

Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. Project details can be found at https://www.mmintlab.com/research/cttp/.

The spontaneous parametric down-conversion (SPDC), an inherently random quantum process, produces a non-deterministic photon-pair with strong temporal and spatial correlations owing to both energy and momentum conservation. Therefore, the SPDC-based photon pairs are used for quantum random number generation (QRNG). Typically, temporal correlation in association with an ideal unbiased beam splitter is used for QRNG without fully exploring the spatial correction. As a result, SPDC-based QRNG has a low bit rate. On the other hand, due to the spatial correlation, the photon pairs in non-collinear phase-matched geometry are generated randomly in diametrically opposite points over an annular ring spatial distribution. Therefore, exploring the temporal correlation between photon pairs from different sections of the annual ring can lead to multi-bit QRNG at a high rate, avoiding the need for a beam splitter. As a proof-of-concept, we report on high-bit-rate QRNG by using spatial correlation of photon-pairs by sectioning the SPDC ring of a non-collinear, degenerate, high-brightness source and temporal correlation between the diametrically opposite sections. Dividing the annular ring of the high-brightness photon-pair source based on a 20 mm long, type-0 phase-matched, periodically-poled KTP crystal into four sections, recording the timestamp of the coincidences (widow of 1 ns) between photons from diametrically opposite sections and assigning bits (0 and 1), we extracted 90 million raw bits over 27.7 s at a pump power of 17 mW. We determined the extraction ratio using the minimum entropy evaluation of more than 95% in our case. Using Toeplitz matrix-based post-processing, we achieved a QRNG with a bit-rate of 3 Mbps, passing all NIST 800-22 and TestU01 test suites. The generic scheme shows the possibility of further enhancement of the bit rate through more sectioning of the SPDC ring.

This work delves into presenting a probabilistic method for analyzing linear process data with weakly dependent innovations, focusing on detecting change-points in the mean and estimating its spectral density. We develop a test for identifying change-points in the mean of data coming from such a model, aiming to detect shifts in the underlying distribution. Additionally, we propose a consistent estimator for the spectral density of the data, contingent upon fundamental assumptions, notably the long-run variance. By leveraging probabilistic techniques, our approach provides reliable tools for understanding temporal changes in linear process data. Through theoretical analysis and empirical evaluation, we demonstrate the efficacy and consistency of our proposed methods, offering valuable insights for practitioners in various fields dealing with time series data analysis. Finally, we implemented our method on bitcoin data for identifying the time points of significant changes in its stock price.

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment searches for evidence of heavy neutrino mass eigenstates in the nuclear electron capture decay of $^7$Be by precisely measuring the recoil energy of the $^7$Li daughter. In Phase-III, the BeEST experiment has been scaled from a single superconducting tunnel junction (STJ) sensor to a 36-pixel array to increase sensitivity and mitigate gamma-induced backgrounds. Phase-III also uses a new continuous data acquisition system that greatly increases the flexibility for signal processing and data cleaning. We have developed procedures for signal processing and spectral fitting that are sufficiently robust to be automated for large data sets. This article presents the optimized procedures before unblinding the majority of the Phase-III data set to search for physics beyond the standard model.

A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that are capable of working in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline., Comment: Accepted to EMNLP 2024

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.

Surface-gated quantum dots (QDs) in semiconductor heterostructures represent a highly attractive platform for quantum computation and simulation. However, in this implementation, the barriers through which the QD is tunnel-coupled to source and drain reservoirs (or neighboring QDs) are usually non-rigid, and capacitively influenced by the plunger gate voltage (VP). In transport spectroscopy measurements, this leads to complete suppression of current and lifting of Coulomb blockade, for large negative and positive values of VP, respectively. Consequently, the charge-occupancy of the QD can be tuned over a rather small range of VP. By dynamically tuning the tunnel barriers to compensate for the capacitive effect of VP, here we demonstrate a protocol which allows the Coulomb blockade to be preserved over a remarkably large span of charge-occupancies, as demonstrated by clean Coulomb diamonds and well-resolved excited state features. The protocol will be highly beneficial for automated tuning and identification of the gatevoltage-space for optimal operation of QDs, in large arrays required for a scalable spin quantum computing architecture., Comment: 5 pages, 5 figures, 1 table

Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.

Positron Emission Tomography (PET) /Computed Tomography (CT) is crucial for diagnosing, managing, and planning treatment for various cancers. Developing reliable deep learning models for the segmentation of tumor lesions in PET/CT scans in a multi-tracer multicenter environment, is a critical area of research. Different tracers, such as Fluorodeoxyglucose (FDG) and Prostate-Specific Membrane Antigen (PSMA), have distinct physiological uptake patterns and data from different centers often vary in terms of acquisition protocols, scanner types, and patient populations. Because of this variability, it becomes more difficult to design reliable segmentation algorithms and generalization techniques due to variations in image quality and lesion detectability. To address this challenge, We trained a 3D Residual encoder U-Net within the no new U-Net framework, aiming to generalize the performance of automatic lesion segmentation of whole body PET/CT scans, across different tracers and clinical sites. Further, We explored several preprocessing techniques and ultimately settled on using the Total Segmentator to crop our training data. Additionally, we applied resampling during this process. During inference, we leveraged test-time augmentations and other post-processing techniques to enhance tumor lesion segmentation. Our team currently hold the top position in the Auto-PET III challenge and outperformed the challenge baseline model in the preliminary test set with Dice score of 0.9627.

The mechanical response of amorphous solids to external strains is riddled with plastic events that create topological charges in the resulting displacement field. It was recently shown that the latter leads to screening phenomena that are accompanied by the breaking of both translational and Chiral symmetries. The screening effects are quantified by two screening parameters $\kappa_e$ and $\kappa_o$, which are inverse characteristic lengths that do not exist in classical elasticity. The screening parameters (and the associated lengths) are emergent, and it is important to understand how they are selected. This Letter explores the mechanism of selection of these characteristic lengths in two examples of strain protocols that allow analytic scrutiny.

Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operated IoT devices. By lowering data sampling frequency, we aim to reduce computational demands RAM usage, energy consumption, latency, and MAC operations by approximately fourfold while maintaining similar classification accuracies. Our experiments with six benchmark datasets (UCIHAR, WISDM, PAMAP2, MHEALTH, MITBIH, and PTB) showed that reducing data acquisition rates significantly cut energy consumption and computational load, with minimal accuracy loss. For example, a 75\% reduction in acquisition rate for MITBIH and PTB datasets led to a 60\% decrease in RAM usage, 75\% reduction in MAC operations, 74\% decrease in latency, and 70\% reduction in energy consumption, without accuracy loss. These results offer valuable insights for deploying efficient TinyML models in constrained environments.

In the rapidly evolving landscape of site reliability engineering (SRE), the demand for efficient and effective solutions to manage and resolve issues in site and cloud applications is paramount. This paper presents an innovative approach to action automation using large language models (LLMs) for script generation, assessment, and refinement. By leveraging the capabilities of LLMs, we aim to significantly reduce the human effort involved in writing and debugging scripts, thereby enhancing the productivity of SRE teams. Our experiments focus on Bash scripts, a commonly used tool in SRE, and involve the CodeSift dataset of 100 tasks and the InterCode dataset of 153 tasks. The results show that LLMs can automatically assess and refine scripts efficiently, reducing the need for script validation in an execution environment. Results demonstrate that the framework shows an overall improvement of 7-10% in script generation., Comment: Under Review

For over two decades, gamma-ray burst (GRB) prompt emission spectra were modelled with smoothly-broken power laws (Band function), and a positive and tight correlation between the spectral rest-frame peak energy $E_p$ and the total isotropic-equivalent luminosity $L_{iso}$ was found, constituting the so-called Yonetoku relation. However, more recent studies show that many prompt emission spectra are well described by the synchrotron radiation model, hence significantly deviating from the Band function. In this work, we test the impact of a more suited spectral model such as an idealized synchrotron spectrum from non-thermal electrons on the Yonetoku relation and its connection with physical parameters. We select GRBs with measured redshift observed by Fermi/GBM together with high energy observations (>30 MeV), and perform spectral analysis dividing them in two samples: the single-bin sample, using the light curve peak spectrum of each GRB, and the multiple-bins sample, where we explore the whole duration of 13 bright bursts with time-resolved spectral analysis. We observed that the $E_p$ of synchrotron spectra in fast-cooling regime ($\nu_m/\nu_c\gg1$) is generally larger than the one provided by the Band function. For this reason, we do not find any $E_p-L_{iso}$ correlation in our samples except for the GRBs in an intermediate-cooling regime ($1<\nu_m/\nu_c<3$), namely where peak and break energies are very close. We instead find in both our samples a new tight correlation between the rest-frame cooling frequency $\nu_{c,z}$ and $L_{iso}$: $\nu_{c,z} \propto L_{iso}^{(0.53 \pm 0.06)}$. These results suggest that, assuming that prompt emission spectra are produced by synchrotron radiation, the physical relation is between $\nu_{c,z}$ and $L_{iso}$. The fit of the Band function to an intrinsic synchrotron spectrum returns peak energy values $E_{p,z}^{Band} \sim \nu_{c,z}$., Comment: Submitted to A&A

Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.

3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a silicon photonic platform. By using additional implant layers, this device provides excellent memory performance compared to the conventional structure (PNPN). TCAD is used to reflect the physical behavior, and the high-speed memory operations are described through the model., Comment: 4 pages, 15 figures

The Hatano-Nelson (HN) Hamiltonian has played a pivotal role in catalyzing research interest in non-Hermitian systems, primarily because it showcases unique physical phenomena that arise solely due to non-Hermiticity. The non-Hermiticity in the HN Hamiltonian, driven by asymmetric hopping amplitudes, induces a delocalization-localization (DL) transition in a one-dimensional (1D) lattice with random disorder, sharply contrasting with its Hermitian counterpart. A similar DL transition occurs in a 1D quasiperiodic HN (QHN) lattice, where a critical quasiperiodic potential strength separates metallic and insulating states, akin to the Hermitian case. In these systems, all states below the critical potential are delocalized, while those above are localized. In this study, we reveal that coupling two 1D QHN lattices can significantly alter the nature of the DL transition. We identify two critical points, $V_{c1} < V_{c2}$, when the nearest neighbors of the two 1D QHN lattices are cross-coupled with strong hopping amplitudes under periodic boundary conditions (PBC). Generally, all states are completely delocalized below $ V_{c1}$ and completely localized above $V_{c2}$, while two mobility edges symmetrically emerge about $\rm{Re[E]} = 0$ between $V_{c1}$ and $V_{c2}$. Notably, under specific asymmetric cross-hopping amplitudes, $V_{c1}$ approaches zero, resulting in localized states even for infinitesimally weak potential. Remarkably, we also find that the mobility edges precisely divide the delocalized and localized states in equal proportions. Furthermore, we observe that the conventional one-to-one correspondence between electronic states under PBC and open boundary conditions (OBC) in 1D HN lattices breaks, Comment: 7 pages, 5 figures

In the standard cosmological framework, neutrinos begin to free-stream after the weak interaction phase ends in the early universe, at a temperature of approximately $T \sim 1 \, {\rm MeV}$. However, the onset of neutrino free-streaming can be delayed if additional interactions occur in the early universe, leaving imprints on both the cosmic microwave background (CMB) angular power spectra and the large-scale structure (LSS) matter power spectra. We present a thorough analysis of early universe neutrino interactions with a fairly generalized parameterization of the interaction rates as a power law in neutrino temperature. In this (6+2) parameter scenario, we constrain the cosmological parameters along with the free-streaming redshift and the sum of the neutrino mass in presence of such interactions, with the help of full shape (FS) galaxy power spectra from BOSS Data Release 12. Our analysis reveals that a combined dataset of FS along with CMB and BAO offers improved constraints on the free-streaming redshift from present data, comparable to the forecast results from future CMB-S4 data. Additionally, we investigate the prospects of future galaxy surveys by forecasting on Euclid mission in combination with Planck and CMB-S4, and find significant improvement on both the free-streaming redshift and the sum of the neutrino mass than the existing constraints as well as than CMB-S4 alone., Comment: 21 pages, 5 sets of figures and 7 tables. Comments are welcome

While topologically stable cosmic strings are disfavored by the recent observation of nHz stochastic gravitational waves (GW) by Pulsar Timing Arrays (PTA), e.g., NANOGrav, cosmic metastable strings and superstrings are not. However, because the gravitational waves from all classes of strings generally span a wide range of frequencies, they contradict LIGO's non-observation of stochastic gravitational waves at the $f\sim $ 25 Hz band for a substantial string-parameter space favoured by the PTA data. Suppose ultralight primordial black holes ($M_{BH}<10^9$ g) existed in the early universe. In this case, they reduce the amplitude of the GWs at higher frequencies by providing an early matter-dominated phase, alleviating the tension between LIGO observation and PTA data. We show that the recent PTA data complemented by future LIGO-Virgo-KAGRA (LVK) runs plus detectors such as LISA and ET would be able to dapple the properties and further search strategies of such ultralight primordial black holes which are otherwise fairly elusive as they evaporate in the early universe by Hawking radiation., Comment: 22 pages, 10 figures, comments are welcome

Semi-holography, originally proposed as a model for conducting lattice electrons coupled to a holographic critical sector, leads to an effective theory of non-Fermi liquids with only a few relevant interactions on the Fermi surface in the large $N$ limit. A refined version of such theories has only two effective couplings which give holographic and Fermi-liquid-like contributions to the self-energy, respectively. We show that a low co-dimension sub-manifold exists in the space of refined semi-holographic theories in which strange metallic behavior is manifested, and which can be obtained just by tuning the ratio of the two couplings. On this sub-manifold, the product of the spectral function and the temperature is approximately independent of the critical exponent, the Fermi energy, and the temperature at all frequencies and near the Fermi surface when expressed in terms of suitably scaled momentum and frequency variables. This quasi-universal behavior leads to linear-in-$T$ dc resistivity and Planckian dissipation over a large range of temperatures, and we also obtain $T^{-3}$ scaling of the Hall conductivity at higher temperatures. The quasi-universal spectral function also fits well with photoemission spectroscopic data without varying the critical exponent with the doping. Combining with the results for optical conductivity, we construct a generalized version of Drude phenomenology for strange-metallic behavior which satisfies non-trivial consistency tests. Finally, we discuss a possible dynamical mechanism for the fine-tuning of the ratio of the two couplings necessary to realize the strange metallic behavior in a typical state., Comment: 52 pages, 17 figures

Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrases. For instance, skillset in freelancer profiles is one such attribute where individual skills are semantically interconnected and indicative of specific domain interests or qualifications. The second challenge of traditional approaches is that, when applied to generate contextually correlated tabular content, besides generating semantically shallow content, they consume huge memory resources and CPU time during the training stage. To address these problems, we introduce a novel framework, CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), which is adept at generating realistic synthetic tabular data where attributes are collections of semantically and contextually coherent words. CTGKrEW is trained and evaluated using a dataset from Upwork, a realworld freelancing platform. Comprehensive experiments were conducted to analyze the variability, contextual similarity, frequency distribution, and associativity of the generated data, along with testing the framework's system feasibility. CTGKrEW also takes around 99\% less CPU time and 33\% less memory footprints than the conventional approach. Furthermore, we developed KrEW, a web application to facilitate the generation of realistic data containing skill-related information. This application, available at https://riyasamanta.github.io/krew.html, is freely accessible to both the general public and the research community.

Cosmic microwave background (CMB) temperature and polarization observations indicate that in the best-fit $\Lambda$ Cold Dark Matter model of the Universe, the local geometry is consistent with at most a small amount of positive or negative curvature, i.e., $\vert\Omega_K\vert\ll1$. However, whether the geometry is flat ($E^3$), positively curved ($S^3$) or negatively curved ($H^3$), there are many possible topologies. Among the topologies of $S^3$ geometry, the lens spaces $L(p,q)$, where $p$ and $q$ ($p>1$ and $0

The following magic trick is at the center of this paper. While the audience writes the first ten terms of a Fibonacci-like sequence (the sequence following the same recursion as the Fibonacci sequence), the magician calculates the sum of these ten terms very fast by multiplying the 7th term by 11. This trick is based on the divisibility properties of partial sums of Fibonacci-like sequences. We find the maximum Fibonacci number that divides the sum of the Fibonacci numbers 1 through $n$. We discuss the generalization of the trick for other second-order recurrences. We show that a similar trick exists for Pell-like sequences and does not exist for Jacobhstal-like sequences., Comment: 26 pages, 9 tables

Altermagnetic (AM) materials have recently attracted significant interest due to the non-relativistic momentum-dependent spin splitting of their electronic band structure which may be useful for antiferromagnetic (AFM) spintronics. So far, however, most research studies have been focused on AM metals which can be utilized in spintronic devices, such as AFM tunnel junctions (AFMTJs). At the same time, AM insulators have remained largely unexplored in the realm of AFM spintronics. Here, we propose to employ AM insulators (AMIs) as efficient spin-filter materials. By analyzing the complex band structure of rutile-type altermagnets $MF_2$ ($M$ = $Fe, Co, Ni$), we demonstrate that the evanescent states in these AMIs exhibit spin- and momentum-dependent decay rates resulting in a substantial momentum-dependent spin polarization of the tunneling current. Using a model of spin-filter tunneling across a spin-dependent potential barrier, we estimate the TMR effect in spin-filter magnetic tunnel junctions (SF-MTJs) that include two magnetically decoupled $MF_2$ (001) barrier layers. We predict a sizable spin-filter TMR ratio of about 150-170% in SF-MTJs based on AMIs $CoF_2$ and $NiF_2$ if the Fermi energy is tuned to be close to the valence band maximum. Our results demonstrate that AMIs provide a viable alternative to conventional ferromagnetic or ferrimagnetic spin-filter materials, potentially advancing the development of next-generation AFM spintronic devices., Comment: 7 pages, 5 figures

In this work, we present TinyTNAS, a novel hardware-aware multi-objective Neural Architecture Search (NAS) tool specifically designed for TinyML time series classification. Unlike traditional NAS methods that rely on GPU capabilities, TinyTNAS operates efficiently on CPUs, making it accessible for a broader range of applications. Users can define constraints on RAM, FLASH, and MAC operations to discover optimal neural network architectures within these parameters. Additionally, the tool allows for time-bound searches, ensuring the best possible model is found within a user-specified duration. By experimenting with benchmark dataset UCI HAR, PAMAP2, WISDM, MIT BIH, and PTB Diagnostic ECG Databas TinyTNAS demonstrates state-of-the-art accuracy with significant reductions in RAM, FLASH, MAC usage, and latency. For example, on the UCI HAR dataset, TinyTNAS achieves a 12x reduction in RAM usage, a 144x reduction in MAC operations, and a 78x reduction in FLASH memory while maintaining superior accuracy and reducing latency by 149x. Similarly, on the PAMAP2 and WISDM datasets, it achieves a 6x reduction in RAM usage, a 40x reduction in MAC operations, an 83x reduction in FLASH, and a 67x reduction in latency, all while maintaining superior accuracy. Notably, the search process completes within 10 minutes in a CPU environment. These results highlight TinyTNAS's capability to optimize neural network architectures effectively for resource-constrained TinyML applications, ensuring both efficiency and high performance. The code for TinyTNAS is available at the GitHub repository and can be accessed at https://github.com/BidyutSaha/TinyTNAS.git.

Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Leveraging inertial measurement unit (IMU) sensors and a customized 1D Convolutional Neural Network (CNN) for personalized HAR, users can tailor activity classes to their unique movement styles with minimal calibration. By utilising TinyML for local computations, the smart band reduces the necessity for constant data transmission and radio communication, which in turn lowers power consumption and reduces carbon footprint. This method also enhances the privacy and security of user data by limiting its transmission. Through transfer learning and fine-tuning on user-specific data, the system achieves a 37\% increase in accuracy over generalized models in personalized settings. Evaluation using three benchmark datasets, WISDM, PAMAP2, and the BandX demonstrates its effectiveness across various activity domains. Additionally, this work presents a cloud-supported framework for the automatic deployment of TinyML models to remote wearables, enabling seamless customization and on-device inference, even with limited target data. By combining personalized HAR with sustainable strategies for on-device continuous inferences, this system represents a promising step towards fostering healthier and more sustainable societies worldwide.

Disordered granular matter exhibits mechanical responses that occupy the boundary between fluids and solids, lacking a complete description within a continuum theoretical framework. Recent studies have shown that, in the quasi-static limit, the mechanical response of disordered solids to external perturbations is anomalous and can be accurately predicted by the theory of odd-dipole-screening. In this work, we investigate responsive granular matter, where grains change size in response to stimuli such as humidity, temperature, or other factors. We develop a geometric theory of odd dipole-screening, incorporating the growth field into the equilibrium equation. Our theory predicts an anomalous displacement field in response to non-uniform growth fields, confirmed by molecular dynamics simulations of granular matter. Although the screening parameters in our theory are phenomenological and not derived from microscopic physics, we identify a surprising relationship between the odd parameter and Poissons ratio. This theory has implications for various experimental protocols, including non-uniform heating or wetting, which lead to spatially varying expansion field.

Volunteer crowdsourcing (VCS) leverages citizen interaction to address challenges by utilizing individuals' knowledge and skills. Complex social tasks often require collaboration among volunteers with diverse skill sets, and their willingness to engage is crucial. Matching tasks with the most suitable volunteers remains a significant challenge. VCS platforms face unpredictable demands in terms of tasks and volunteer requests, complicating the prediction of resource requirements for the volunteer-to-task assignment process. To address these challenges, we introduce the Skill and Willingness-Aware Volunteer Matching (SWAM) algorithm, which allocates volunteers to tasks based on skills, willingness, and task requirements. We also developed a serverless framework to deploy SWAM. Our method outperforms conventional solutions, achieving a 71% improvement in end-to-end latency efficiency. We achieved a 92% task completion ratio and reduced task waiting time by 56%, with an overall utility gain 30% higher than state-of-the-art baseline methods. This framework contributes to generating effective volunteer and task matches, supporting grassroots community coordination and fostering citizen involvement, ultimately contributing to social good.

Crowdsourcing (CS) faces the challenge of managing complex, skill-demanding tasks, which requires effective task assignment and retention strategies to sustain a balanced workforce. This challenge has become more significant in Volunteer Crowdsourcing Services (VCS). This study introduces Workforce Composition Balance (WCB), a novel framework designed to maintain workforce diversity in VCS by dynamically adjusting retention decisions. The WCB framework integrates the Volunteer Retention and Value Enhancement (VRAVE) algorithm with advanced skill-based task assignment methods. It ensures efficient remuneration policy for both assigned and unassigned potential volunteers by incorporating their potential levels, participation dividends, and satisfaction scores. Comparative analysis with three state-of-the-art baselines on real dataset shows that our WCB framework achieves 1.4 times better volunteer satisfaction and a 20% higher task retention rate, with only a 12% increase in remuneration. The effectiveness of the proposed WCB approach is to enhance the volunteer engagement and their long-term retention, thus making it suitable for functioning of social good applications where a potential and skilled volunteer workforce is crucial for sustainable community services.

A spin valve represents a well-established device concept in magnetic memory technologies, whose functionality is determined by electron transmission being controlled by the relative alignment of magnetic moments of the two ferromagnetic layers. Recently, the advent of valleytronics has conceptualized a valley spin valve (VSV) - a device that utilizes the valley degree of freedom and spin-valley locking to achieve a similar valve effect without relying on magnetism. In this study, we propose a non-volatile VSV (n-VSV) based on a two-dimensional (2D) ferroelectric semiconductor where the resistance of the n-VSV is controlled by the ferroelectric domain wall between the two uniformly polarized domains. Focusing on the 1T'' phase of MoS2, which is known to be ferroelectric down to a monolayer and using density functional theory (DFT) combined with the quantum-transport calculations, we demonstrate that switching between the uniformly polarized state and the state with oppositely polarized domains separated by a domain wall results in resistance change of as high as 10^7. This giant VSV effect occurs due to transmission being strongly dependent on matching (mismatching) the valley-dependent spin polarizations in the two domains with the same (opposite) ferroelectric polarization orientations, when the chemical potential of 1T''-MoS2 lies within the spin-split valleys. Our work paves a new route for realizing high-performance nonvolatile valleytronics., Comment: 6 pages, 4 figures

Talbot length, the distance between two consecutive self-image planes along the propagation axis for a periodic diffraction object (grating) illuminated by a plane wave, depends on the period of the object and the wavelength of illumination. This property makes the Talbot effect a straightforward technique for measuring the period of a periodic object (grating) by accurately determining the Talbot length for a given illumination wavelength. However, since the Talbot length scale is proportional to the square of the grating period, traditional Talbot techniques face challenges when dealing with smaller grating periods and minor changes in the grating period. Recently, we demonstrated a Fourier transform technique-based Talbot imaging method that allows for controlled Talbot lengths of a periodic object with a constant period and illumination wavelength. Using this method, we successfully measured periods as small as a few micrometers and detected sub-micrometer changes in the periodic object. Furthermore, by measuring the Talbot length of gratings with varying periods imaged through the combination of a thick lens of short focal length and a thin lens of long focal length and large aperture, we determined the effective focal length of the thick lens in close agreement with the theoretical effective focal length of a thick lens in the presence of spherical aberration. These findings establish the Talbot effect as an effective and simple technique for various sensing applications in optics and photonics through the measurement of any physical parameter influencing the Talbot length of a periodic object.

In this paper we introduce a quotient structure on topological ternary semigroup by defining a congruence suitably. We have found conditions under which this quotient structure becomes a topological ternary semigroup. We have also obtained conditions that make this quotient a topological ternary group, whenever the base structure is a topological ternary group.

This study presents a novel approach for enhancing American Sign Language (ASL) recognition using Graph Convolutional Networks (GCNs) integrated with successive residual connections. The method leverages the MediaPipe framework to extract key landmarks from each hand gesture, which are then used to construct graph representations. A robust preprocessing pipeline, including translational and scale normalization techniques, ensures consistency across the dataset. The constructed graphs are fed into a GCN-based neural architecture with residual connections to improve network stability. The architecture achieves state-of-the-art results, demonstrating superior generalization capabilities with a validation accuracy of 99.14%., Comment: To be submitted in G2-SP CV 2024. Contains 7 pages, 5 figures

The discovery of ferroelectricity in hafnia-based materials have boosted the potential of incorporating ferroelectrics in advanced electronics, thanks to their compatibility with silicon technology. However, comprehending why these materials defy the common trend of reduced ferroelectric ordering at the nanoscale, and the mechanism that stabilizes the ferroelectric phase (absent in hafnia phase diagram) presents significant challenges to traditional knowledge of ferroelectricity. In this work, we show that the formation of the orthorhombic ferroelectric phase (o-FE, space group Pca21) of the single-crystalline epitaxial films of 10% La-doped HfO2 (LHO) on (111)-oriented yttria stabilized zirconia (YSZ) relies on the stability of the high-pressure orthorhombic antiferroelectric phase (o-AFE, space group Pbca). Our detailed structural characterizations demonstrate that as-grown LHO films represent largely the o-AFE phase being thermodynamically stabilized by the compressive strain. Our Kelvin probe force microscopy studies show, under mechanical poling, the o-AFE phase is converted to the o-FE phase which remains stable under ambient conditions. We find that the orthorhombic phase stability is enhanced in thinner films down to one-unit-cell thickness, a trend that is unknown in any other ferroelectric films. This is due to the vanishing depolarization field of the o-AFE phase and the isomorphic LHO/YSZ interface, supporting strain-enhanced ferroelectricity in the ultrathin films. This results in an unprecedented increase of the Curie temperature up to 850 {\deg}C, the highest reported for sub-nanometer-thick ferroelectrics. Overall, our findings opens the way for advanced engineering of hafnia-based materials for ferroelectric applications and heralding a new frontier of high-temperature ferroelectrics at the two-dimensional limit.