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

Collision-free Frequency Hopping Sequences (FHS) are crucial for maintaining the throughput of Frequency Hopping Multiple Access (FHMA) communication systems. When multiple FHS deployed in the same geographical area hop into the same frequency spot or location, it can result in multiple access interference (MAI), leading to performance degradation of the co-habiting FHMA systems. Orthogonal FHS (O-FHS) could be used to avoid such collisions even while maintaining the pseudo-random hopping property. We propose a new Collision-Free Balanced Frequency Hopping Sequence (CFB-FHS) set based on $GF(p)$. The proposed algorithm generates a significantly longer O-FHS and ensures full-band hopping and uniform usage of all the available frequency spots, even while preserving the pseudo-random nature of the sequences. The Hamming correlation and these related properties of the proposed sequence set are analyzed and compared with the existing sequence sets. The length of this CFB-FHS can be chosen based on the application requirements, and once selected, the length remains fixed irrespective of the family size of the sequence set.

Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, is an enabling concept for faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of the MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and serving electric vehicle charging loads during evacuation before disaster events. Simulation results show that MBB effectively decouples the power quality issues across networks and helps maintain good power quality in the power quality sensitive network based on the operational scenario., Comment: This paper is accepted for publication in IEEE IECON 2024, Chicago, IL. The complete copyright version will be available on IEEE Xplore when the conference proceedings are published

$\text{RuO}_2$, a transition metal oxide, is attracting attention in spintronics for its unique altermagnetic properties, which influence spin currents. Its ability to produce large spin-orbit torques and spin Hall effects is key for energy-efficient magnetic memory and logic devices. Additionally, the tunable thickness and crystallinity of $\text{RuO}_2$ thin films optimize torque efficiency for low-power switching. Spin pumping, a versatile method for investigating spin dynamics in $\text{RuO}_2$ thin films, has garnered considerable interest because of its straightforward, non-invasive and uncomplicated approach to addressing impedance mismatch and direct measurement of spintronic parameters. Here we present a systematic and detailed analysis on the efficient spin to charge conversion in (110)-oriented $\text{RuO}_2$ films with amorphous CoFeB as spin source. The spin Hall angle, and spin diffusion length were estimated to be 0.14 $\pm$ 0.01 and 4.58 $\pm$ 0.40 nm, respectively. The spin Hall conductivity of 998.89 $\pm$ 58.23 $\hbar \cdot \frac{\Omega^{-1} \, \text{cm}^{-1}}{e}$ has been estimated which is theoretically predicted to be of the similar order. The interfacial spin transparency has been achieved to be 90%. We have shown that the spin memory loss at the $\text{RuO}_2$/CoFeB interface is 15%, which is very small.

We present results from the molecular dynamics (MD) simulation of surface-directed spinodal decomposition (SDSD) in binary fluid mixtures ($A+B$) with off-critical compositions. The aim is to elucidate the role of composition ratio in the early-time wetting kinetics under the influence of long-range surface potential. In our simulations, the attractive part of surface potential varies as $V(z)= -\epsilon_a/z^{n}$, with $\epsilon_{a}$ being the surface-potential strength. The surface prefers `$A$' species to form the wetting layer. Its thickness [$R_1(t)$] for the majority wetting (number of $A$-type particles [$N_A$] > number of $B$-type particles [$N_B$]), grows as a power-law with an exponent $1/(n+2)$. This is consistent with the early-time kinetics in the form of potential-dependent growth present in the Puri-Binder model. However, for minority wetting ($N_A$ < $N_B$), the growth exponent in $R_1(t)$ is less than $1/(n+2)$. Furthermore, on decreasing the field strength $\epsilon_{a}$, we recover $1/(n+2)$ for a minority wetting case. We provide phenomenological arguments to explain the early-time wetting kinetics for both cases., Comment: 11 pages, 7 figures

Microgrids offer increased self-reliance and resilience at the grid's edge. They promote a significant transition to decentralized and renewable energy production by optimizing the utilization of local renewable sources. However, to maintain stable operations under all conditions and harness microgrids' full economic and technological potential, it is essential to integrate with the bulk grid and neighboring microgrids seamlessly. In this paper, we explore the capabilities of Back-to-Back (BTB) converters as a pivotal technology for interfacing microgrids, hybrid AC/DC grids, and bulk grids, by leveraging a comprehensive phasor-domain model integrated into GridLAB-D. The phasor-domain model is computationally efficient for simulating BTB with bulk grids and networked microgrids. We showcase the versatility of BTB converters (an integrated Microgrid Building Block) by configuring a two-microgrid network from a modified IEEE 13-node distribution system. These microgrids are equipped with diesel generators, photovoltaic units, and Battery Energy Storage Systems (BESS). The simulation studies are focused on use cases demonstrating dynamic decoupling and controlled support that a microgrid can provide via a BTB converter., Comment: This paper is accepted for publication in IEEE PES Grid Edge Technologies Conference & Exposition 2025, San Diego, CA. The complete copyright version will be available on IEEE Xplore when the conference proceedings are published

The concept of ownership in high level languages can aid both the programmer and the compiler to reason about the validity of memory operations. Previously, ownership semantics has been used successfully in high level automatic program verification to model a reference to data by a first order logic (FOL) representation of data instead of maintaining an address map. However, ownership semantics is not used in low level program verification. We have identified two challenges. First, ownership information is lost when a program is compiled to a low level intermediate representation (e.g., in LLVM IR). Second, pointers in low level programs point to bytes using an address map (e.g., in unsafe Rust) and thus the verification condition (VC) cannot always replace a pointer by its FOL abstraction. To remedy the situation, we develop ownership semantics for an LLVM like low level intermediate representation. Using these semantics, the VC can opportunistically model some memory accesses by a direct access of a pointer cache that stores byte representation of data. This scheme reduces instances where an address map must be maintained, especially for mostly safe programs that follow ownership semantics. For unsafe functionality, memory accesses are modelled by operations on an address map and we provide mechanisms to keep the address map and pointer cache in sync. We implement these semantics in SEABMC, a bit precise bounded model checker for LLVM. For evaluation, the source programs are assumed to be written in C. Since C does not have ownership built in, suitable macros are added that introduce and preserve ownership during translation to LLVM like IR for verification. This approach is evaluated on mature open source C code. For both handcrafted benchmarks and practical programs, we observe a speedup of $1.3x-5x$ during SMT solving., Comment: FMCAD 2024 conference

Open quantum dynamics can be categorized in several ways, including according to their divisibility. For any quantum channel acting for any finite time period, we propose measures of P-indivisibility and CP-indivisibility, where P and CP stand for positivity and complete positivity respectively. Subsequently, we also propose two quantities to measure the resourcefulness - with respect to an arbitrary quantum resource - of any quantum channel within any finite time interval. Moreover, we find a bridge between these two classes of metrics, viz. those quantifying divisibilities of quantum channels and those gauging their resourcefulness, by identifying two separate relations between elements of one class with those of the other. Lastly, we verify the two relations using quantum non-Markovianity as a channel resource., Comment: 7 pages, 3 figures

A quantum switch is a superoperator that, in general, creates a superposition of various causal orders of two or more quantum dynamics that are all divisible in the complete positivity (CP) sense. We introduce a process that we term as the universal quantum switch (UQS), which unlike conventional quantum switches, allows for the construction of a quantum switch that can superpose different causal orders of any set of quantum dynamics, regardless of their CP-divisibility. Our approach also enables the construction of a quantum switch while considering a single environment connected with the system, in contrast to the traditional one. Moreover, we show the UQS provides more advantages in performance for a certain state discrimination task compared to traditional quantum switches. The next question that we address is the following: What is the CP-divisibility characteristic of a dynamics built by acting a quantum switch on CP-divisible or -indivisible dynamics? In this regard, an example is presented where the dynamics created by the action of the UQS on two CP-indivisible dynamics is CP-indivisible. Additionally, we prove a necessary and sufficient condition for the channel created by acting the traditional quantum switch on two CP-divisible dynamics to be CP-divisible. Furthermore, we present some examples of CP-divisible dynamics on which, when the usual quantum switch is operated, the resulting dynamics not only becomes CP-indivisible but also turns into P-indivisible. Our findings demonstrate that quantum switches can build CP-divisible, CP-indivisible, and even P-indivisible dynamics from CP-divisible dynamics, underscoring the versatility of this technique., Comment: 12 pages, 6 figures

Schizophrenia is a globally prevalent psychiatric disorder that severely impairs daily life. Schizophrenia is caused by dopamine imbalances in the fronto-striatal pathways of the brain, which influences fine motor control in the cerebellum. This leads to abnormalities in handwriting. The goal of this study was to develop an accurate, objective, and accessible computational method to be able to distinguish schizophrenic handwriting samples from non-schizophrenic handwriting samples. To achieve this, data from Crespo et al. (2019) was used, which contains images of handwriting samples from schizophrenic and non-schizophrenic patients. The data was preprocessed and augmented to produce a more robust model that can recognize different types of handwriting. The data was used to train several different convolutional neural networks, and the model with the base architecture of InceptionV3 performed the best, differentiating between the two types of image with a 92% accuracy rate. To make this model accessible, a secure website was developed for medical professionals to use for their patients. Such a result suggests that handwriting analysis through computational models holds promise as a non-invasive and objective method for clinicians to diagnose and monitor schizophrenia., Comment: 5 pages, 8 figures

We present extensive photometric and spectroscopic observations of the nearby Type Ia supernova (SN) 2023wrk at a distance of about 40 Mpc. The earliest detection of this SN can be traced back to a few hours after the explosion. Within the first few days the light curve shows a bump feature, while the B - V color is blue and remains nearly constant. The overall spectral evolution is similar to that of an SN 1991T/SN 1999aa-like SN Ia, while the C II $\lambda6580$ absorption line appears to be unusually strong in the first spectrum taken at $t \approx -$15.4 days after the maximum light. This carbon feature disappears quickly in subsequent evolution but it reappears at around the time of peak brightness. The complex evolution of the carbon line and the possible detection of Ni III absorption around 4700 {\AA} and 5300 {\AA} in the earliest spectra indicate macroscopic mixing of fuel and ash. The strong carbon lines is likely related to collision of SN ejecta with unbound carbon, consistent with the predictions of pulsational delayed-detonation or carbon-rich circumstellar-matter interaction models. Among those carbon-rich SNe Ia with strong C II $\lambda6580$ absorption at very early times, the line-strength ratio of C II to Si II and the B-V color evolution are found to exhibit large diversity, which may be attributed to different properties of unbound carbon and outward-mixing $^{56}$Ni., Comment: Accepted for publication in the Astrophysical Journal (27 pages, 14 figures, 7 tables)

Widefield quantum diamond microscopy is a powerful technique for imaging magnetic fields with high sensitivity and spatial resolution. However, current methods to approach the ultimate spatial resolution ($<500\,$nm) are impractical for routine use as they require time-consuming fabrication or transfer techniques to precisely interface the diamond sensor with the sample to be imaged. To address this challenge, we have designed a co-axial sensor holder that enables simple, repeatable sensor-sample interfacing while being compatible with high numerical aperture (NA) optics. With our new design we demonstrate low standoffs $<500\,$nm with a millimetre sized sensor. We also explore the relationship between spatial resolution and NA spanning from 0.13 to 1.3. The spatial resolution shows good agreement with the optical diffraction limit at low NA but deviates at high NA, which is shown to be due to optical aberrations. Future improvements to our design are discussed, which should enable magnetic imaging with $<500\,$nm resolution in an accessible, easy-to-use instrument.

Optically detected magnetic resonance (ODMR) has been observed from emitters in hexagonal boron nitride across a broad range of wavelengths, but so far an understanding of their microscopic structure and the physical origin of ODMR has been lacking. Here we perform comprehensive measurements and modelling of the spin-resolved photodynamics of ensembles and single emitters, and uncover a universal model that accounts, and provides an intuitive physical explanation, for all key experimental features. The model assumes a pair of nearby point defects -- a primary optically active defect and a secondary defect. Charge transfer between the two defects creates a metastable weakly coupled spin pair with ODMR naturally arising from selection rules. Our optical-spin defect pair (OSDP) model resolves several previously open questions including the asymmetric envelope of the Rabi oscillations, the large variability in ODMR contrast amplitude and sign, and the wide spread in emission wavelength. It may also explain similar phenomena observed in other wide bandgap semiconductors such as GaN. The presented framework will be instrumental in guiding future theoretical and experimental efforts to study and engineer solid-state spin pairs and unlock their potential for quantum technology applications.

In practice, it is quite challenging to detect a quantum property, a microscopic property, in a macroscopic system. In our work, we construct general proxy witnesses of quantum properties to detect their presence in quantum systems and we do so for quantum systems which may possibly be large. In particular, we discuss proxy witnesses for quantum properties like unextendibility, quantum coherence, activation, steerability, and entanglement. We apply these proxy witnesses in some widely considered examples of many-body systems, viz., the quantum Heisenberg models, the quantum J1-J2 model., Comment: 15 pages, 1 figures

We provide a brief review of the fundamentals of quantum computation and quantum error correction for the participants of the first Quantum Information Knowledge (QuIK) workshop at the 2024 IEEE International Symposium on Information Theory (ISIT 2024). While this is not a comprehensive review, we provide many references for the reader to delve deeper into the concepts and research directions., Comment: 11 pages, double column, IEEE format, 2 figures

Access to granular demand data is essential for the net zero transition; it allows for accurate profiling and active demand management as our reliance on variable renewable generation increases. However, public release of this data is often impossible due to privacy concerns. Good quality synthetic data can circumnavigate this issue. Despite significant research on generating synthetic smart meter data, there is still insufficient work on creating a consistent evaluation framework. In this paper, we investigate how common frameworks used by other industries leveraging synthetic data, can be applied to synthetic smart meter data, such as fidelity, utility and privacy. We also recommend specific metrics to ensure that defining aspects of smart meter data are preserved and test the extent to which privacy can be protected using differential privacy. We show that standard privacy attack methods like reconstruction or membership inference attacks are inadequate for assessing privacy risks of smart meter datasets. We propose an improved method by injecting training data with implausible outliers, then launching privacy attacks directly on these outliers. The choice of $\epsilon$ (a metric of privacy loss) significantly impacts privacy risk, highlighting the necessity of performing these explicit privacy tests when making trade-offs between fidelity and privacy., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

An accurate expression of the kinetic energy density of an electronic distribution in terms of a single particle reduced density matrix for atomic/molecular systems is a long-standing problem in electronic structure theory. Existing kinetic energy density functionals are based on model potentials and require empirical parametrizations to fit the accurate values of the kinetic energy of atoms. Moreover, the correct kinetic energy density which produces proper atomic shell structure is still an unsolved problem. In this work, we have developed an exact methodology which can be used to systematically derive kinetic energy of an electronic system. We have also shown how the kinetic energy is dependent upon number of electrons $N$ and the atomic charge $Z$ using a qualitatively similar model system. We have also connected our methodology with other forms of Green's functions.

Robot-assisted feeding has the potential to improve the quality of life for individuals with mobility limitations who are unable to feed themselves independently. However, there exists a large gap between the homogeneous, curated plates existing feeding systems can handle, and truly in-the-wild meals. Feeding realistic plates is immensely challenging due to the sheer range of food items that a robot may encounter, each requiring specialized manipulation strategies which must be sequenced over a long horizon to feed an entire meal. An assistive feeding system should not only be able to sequence different strategies efficiently in order to feed an entire meal, but also be mindful of user preferences given the personalized nature of the task. We address this with FLAIR, a system for long-horizon feeding which leverages the commonsense and few-shot reasoning capabilities of foundation models, along with a library of parameterized skills, to plan and execute user-preferred and efficient bite sequences. In real-world evaluations across 6 realistic plates, we find that FLAIR can effectively tap into a varied library of skills for efficient food pickup, while adhering to the diverse preferences of 42 participants without mobility limitations as evaluated in a user study. We demonstrate the seamless integration of FLAIR with existing bite transfer methods [19, 28], and deploy it across 2 institutions and 3 robots, illustrating its adaptability. Finally, we illustrate the real-world efficacy of our system by successfully feeding a care recipient with severe mobility limitations. Supplementary materials and videos can be found at: https://emprise.cs.cornell.edu/flair ., Comment: RSS 2024

We introduce a novel neural network structure called Strongly Constrained Theory-Guided Neural Network (SCTgNN), to investigate the behaviours of the localized solutions of the generalized nonlinear Schr\"{o}dinger (NLS) equation. This equation comprises four physically significant nonlinear evolution equations, namely, (i) NLS equation, Hirota equation Lakshmanan-Porsezian-Daniel (LPD) equation and fifth-order NLS equation. The generalized NLS equation demonstrates nonlinear effects up to quintic order, indicating rich and complex dynamics in various fields of physics. By combining concepts from the Physics-Informed Neural Network (PINN) and Theory-Guided Neural Network (TgNN) models, SCTgNN aims to enhance our understanding of complex phenomena, particularly within nonlinear systems that defy conventional patterns. To begin, we employ the TgNN method to predict the behaviours of localized waves, including solitons, rogue waves, and breathers, within the generalized NLS equation. We then use SCTgNN to predict the aforementioned localized solutions and calculate the mean square errors in both SCTgNN and TgNN in predicting these three localized solutions. Our findings reveal that both models excel in understanding complex behaviours and provide predictions across a wide variety of situations., Comment: 27 pages,9 figures

The intersection of cloud computing, blockchain technology, and the impending era of quantum computing presents a critical juncture for data security. This research addresses the escalating vulnerabilities by proposing a comprehensive framework that integrates Quantum Key Distribution (QKD), CRYSTALS Kyber, and Zero-Knowledge Proofs (ZKPs) for securing data in cloud-based blockchain systems. The primary objective is to fortify data against quantum threats through the implementation of QKD, a quantum-safe cryptographic protocol. We leverage the lattice-based cryptographic mechanism, CRYSTALS Kyber, known for its resilience against quantum attacks. Additionally, ZKPs are introduced to enhance data privacy and verification processes within the cloud and blockchain environment. A significant focus of this research is the performance evaluation of the proposed framework. Rigorous analyses encompass encryption and decryption processes, quantum key generation rates, and overall system efficiency. Practical implications are scrutinized, considering factors such as file size, response time, and computational overhead. The evaluation sheds light on the framework's viability in real-world cloud environments, emphasizing its efficiency in mitigating quantum threats. The findings contribute a robust quantum-safe and ZKP-integrated security framework tailored for cloud-based blockchain storage. By addressing critical gaps in theoretical advancements, this research offers practical insights for organizations seeking to secure their data against quantum threats. The framework's efficiency and scalability underscore its practical feasibility, serving as a guide for implementing enhanced data security in the evolving landscape of quantum computing and blockchain integration within cloud environments., Comment: 15 pages, 5 figures

Retention for online learners is declining so much that higher education institutions are graduating more learners than they are enrolling. The purpose of this mixed-methods study was to find ways to improve retention for first-term non-traditional learners in an online undergraduate program. Fourteen non-traditional learners participated in the study. Findings suggest that technology knowledge impacted programmatic difficulty for learners, and although learners were not receptive to a mandated orientation, orientation was effective. By incorporating technology and rigor into an orientation course, first-term non-traditional learners will be better prepared for their higher education journey. More research is needed.

Classical and quantum machine learning are being increasingly applied to various tasks in quantum information technologies. Here, we present an experimental demonstration of quantum control using a physics-informed neural network (PINN). PINN's salient feature is how it encodes the entire control sequence in terms of its network parameters. This feature enables the control sequence to be later adopted to any hardware with optimal time discretization, which contrasts with conventional methods involving a priory time discretization. Here, we discuss two important quantum information tasks: gate synthesis and state preparation. First, we demonstrate quantum gate synthesis by designing a two-qubit CNOT gate and experimentally implementing it on a heteronuclear two-spin NMR register. Second, we demonstrate quantum state preparation by designing a control sequence to efficiently transfer the thermal state into the long-lived singlet state and experimentally implement it on a homonuclear two-spin NMR register. We present a detailed numerical analysis of the PINN control sequences regarding bandwidth, discretization levels, control field errors, and external noise.

Computational approach to politeness is the task of automatically predicting and generating politeness in text. This is a pivotal task for conversational analysis, given the ubiquity and challenges of politeness in interactions. The computational approach to politeness has witnessed great interest from the conversational analysis community. This article is a compilation of past works in computational politeness in natural language processing. We view four milestones in the research so far, viz. supervised and weakly-supervised feature extraction to identify and induce politeness in a given text, incorporation of context beyond the target text, study of politeness across different social factors, and study the relationship between politeness and various sociolinguistic cues. In this article, we describe the datasets, approaches, trends, and issues in computational politeness research. We also discuss representative performance values and provide pointers to future works, as given in the prior works. In terms of resources to understand the state-of-the-art, this survey presents several valuable illustrations, most prominently, a table summarizing the past papers along different dimensions, such as the types of features, annotation techniques, and datasets used., Comment: Manuscript accepted at the ACM Computing Surveys (DOI: https://doi.org/10.1145/3654660)

The fast-growing development of optomechanical applications has motivated advancements in Brillouin scattering research. In particular, the study of high frequency acoustic phonons at the nanoscale is interesting due to large range of interactions with other excitations in matter. However, standard Brillouin spectroscopy schemes rely on fixed wavelength filtering, which limits the usefulness for the study of tunable optophononic resonators. It has been recently demonstrated that elliptical optophononic micropillar resonators induce different energy-dependent polarization states for the Brillouin and the elastic Rayleigh scattering, and that a polarization filtering setup could be implemented to increase the contrast between the inelastic and elastic scattering of the light. An optimal filtering configuration can be reached when the polarization states of the laser and the Brillouin signal are orthogonal from each other. In this work, we theoretically investigate the parameters of such polarization-based filtering technique to enhance the efficiency of Brillouin scattering detection. For the filtering optimization, we explore the initial wavelength and polarization state of the incident laser, as well as in the ellipticity of the micropillars, and reach an almost optimal configuration for nearly background-free Brillouin detection. Our findings are one step forward on the efficient detection of Brillouin scattering in nanostructures for potential applications in fields such as optomechanics and quantum communication.

Let $R$ be a finite commutative ring with unity and $x$ be a non-zero element of $R$. In this paper, we calculate the spectrum and energy of the Cayley graph ${\rm Cay}(R,xR^{*})$, and also compute the energy of their compliment graph. It generalizes to Theorem 2.4 and Theorem 4.1 of https://doi.org/10.1016/j.laa.2011.03.015. Further, we characterize the commutative ring $R$ such that the Cayley graph ${\rm Cay}(R,xR^{*})$ is Ramanujan. This generalizes to Theorem 12 of https://doi.org/10.48550/arXiv.1205.5932.

High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized excitations at nanoscale. Recently, strong field excitation of dielectric nanoparticles has emerged as an avenue for studying catalysis in highly ionized environments producing extreme electric fields. While the dynamics of surface ion emission driven by ultrafast laser ionization has been heavily explored, understanding the molecular dynamics leading to fragmentation has remained elusive. To address this, we employed a multiscale approach utilizing non-adiabatic quantum molecular dynamics (NAQMD) simulations on hydrogenated silica surfaces in both bare and wetted environments under field conditions mimicking those of an ionized nanoparticle. Our findings indicate that hole localization drives fragmentation dynamics, leading to surface silanol dissociation within 50 fs and charge transfer-induced water splitting in wetted environments within 150 fs. Further insight into such ultrafast mechanisms is critical for advancement of catalysis on the surface of charged nanosystems.

Recent advancements in nanotechnology have led to the development of surface-enhanced Raman spectroscopy (SERS) based rapid and low-cost technologies for ultra-sensitive label-free detection and identification of molecular analytes. Herein, we utilized the synergistic plasmonic and chemical enhancement effects of Au-WS2 nanohybrids to attain the high-intensity Raman signals of targeted analytes. To develop these nanohybrids, a series of monodispersed Au nanoparticles (NPs) of varying diameters from 20 to 80 nm was chemically synthesized and successively blended with liquid-phase exfoliated WS2 nano-flakes of average lateral size 90 nm. They provided a maximum enhancement factor (EF) of ~1.80 109 corresponding to the characteristic peaks at 1364 cm-1 and 1512 cm-1 for R6G analyte molecules. Theoretical studies based on the finite-difference time-domain simulations on Au-WS2 nanohybrid systems revealed a huge field-intensity enhancement with an EF of more than 1000 at the plasmonic hotspots, which was induced by the strong coupling of individual plasmon oscillations of the adjacent Au NPs upon light interactions. These electromagnetic effects along with the chemical enhancement effects of WS2 nanoflakes were found to be mainly responsible for such huge enhancement in Raman signals. Furthermore, these hybrids were successfully employed for achieving highly sensitive detection of the E. coli ATCC 35218 bacterial strain with a concentration of 104 CFU/mL in phosphate-buffered saline media, indicating their real capabilities for practical scenarios. The findings of the present study will indeed provide vital information in the development of innovative nanomaterial-based biosensors, that will offer new possibilities for addressing critical public health concerns.

We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgrounds, and it explored multiple facets of their perspectives on astronomy. The topics included students' views on the incorporation of astronomy in curricula, their grasp of fundamental astronomical concepts, access to educational resources, cultural connections to astronomy, and their levels of interest and aspirations in the subject. We find notable deficiencies in students' knowledge of basic astronomical principles, with only a minority demonstrating proficiency in key areas such as celestial sizes, distances, and lunar phases. Furthermore, access to resources such as telescopes and planetariums remain limited across the country. Despite these challenges, a significant majority of students expressed a keen interest in astronomy. We further analyze the data along socioeconomic and gender lines. Particularly striking were the socioeconomic disparities, with students from resource-poor backgrounds often having lower levels of access and proficiency. Some differences were observed between genders, although not very pronounced. The insights gleaned from this study hold valuable implications for the development of a more robust astronomy curriculum and the design of effective teacher training programs in the future., Comment: 15 pages, 19 figures

We present $\varepsilon$-retrain, an exploration strategy designed to encourage a behavioral preference while optimizing policies with monotonic improvement guarantees. To this end, we introduce an iterative procedure for collecting retrain areas -- parts of the state space where an agent did not follow the behavioral preference. Our method then switches between the typical uniform restart state distribution and the retrain areas using a decaying factor $\varepsilon$, allowing agents to retrain on situations where they violated the preference. Experiments over hundreds of seeds across locomotion, navigation, and power network tasks show that our method yields agents that exhibit significant performance and sample efficiency improvements. Moreover, we employ formal verification of neural networks to provably quantify the degree to which agents adhere to behavioral preferences.

Classifying 3D MRI images for early detection of Alzheimer's disease is a critical task in medical imaging. Traditional approaches using Convolutional Neural Networks (CNNs) and Transformers face significant challenges in this domain. CNNs, while effective in capturing local spatial features, struggle with long-range dependencies and often require extensive computational resources for high-resolution 3D data. Transformers, on the other hand, excel in capturing global context but suffer from quadratic complexity in inference time and require substantial memory, making them less efficient for large-scale 3D MRI data. To address these limitations, we propose the use of Vision Mamba, an advanced model based on State Space Models (SSMs), for the classification of 3D MRI images to detect Alzheimer's disease. Vision Mamba leverages dynamic state representations and the selective scan algorithm, allowing it to efficiently capture and retain important spatial information across 3D volumes. By dynamically adjusting state transitions based on input features, Vision Mamba can selectively retain relevant information, leading to more accurate and computationally efficient processing of 3D MRI data. Our approach combines the parallelizable nature of convolutional operations during training with the efficient, recurrent processing of states during inference. This architecture not only improves computational efficiency but also enhances the model's ability to handle long-range dependencies within 3D medical images. Experimental results demonstrate that Vision Mamba outperforms traditional CNN and Transformer models accuracy, making it a promising tool for the early detection of Alzheimer's disease using 3D MRI data., Comment: 12 pages with 5 figures and 3 tables, to be submitted as a book chapter

Opinion pieces (or op-eds) can provide valuable perspectives, but they often represent only one side of a story, which can make readers susceptible to confirmation bias and echo chambers. Exposure to different perspectives can help readers overcome these obstacles and form more robust, nuanced views on important societal issues. We designed ArguMentor, a human-AI collaboration system that highlights claims in opinion pieces, identifies counter-arguments for them using a LLM, and generates a context-based summary of based on current events. It further enhances user understanding through additional features like a Q&A bot (that answers user questions pertaining to the text), DebateMe (an agent that users can argue any side of the piece with) and highlighting (where users can highlight a word or passage to get its definition or context). Our evaluation shows that participants can generate more arguments and counter-arguments and have, on average, have more moderate views after engaging with the system.

Transversal gates are logical gate operations on encoded quantum information that are efficient in gate count and depth, and are designed to minimize error propagation. Efficient encoding circuits for quantum codes that admit transversal gates are thus crucial to reduce noise and realize useful quantum computers. The class of punctured Quantum Reed-Muller codes admit transversal gates. We construct resource efficient recursive encoders for the class of quantum codes constructed from Reed-Muller and punctured Reed-Muller codes. These encoders on $n$ qubits have circuit depth of $O(\log n)$ and lower gate counts compared to previous works. The number of CNOT gates in the encoder across bi-partitions of the qubits is found to be equal to the entanglement entropy across these partitions, demonstrating that the encoder is optimal in terms of CNOT gates across these partitions. Finally, connecting these ideas, we explicitly show that entanglement can be extracted from QRM codewords., Comment: 33 pages

Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing moral reasoning in LLMs by exploring decisions' consequences from multiple stakeholder perspectives. Central to SKIG's mechanism is simulating accountability for actions, which, alongside empathy exercises and risk assessment, is pivotal to its effectiveness. We validate SKIG's performance across various moral reasoning benchmarks with proprietary and opensource LLMs, and investigate its crucial components through extensive ablation analyses., Comment: ACL 2024, long paper

In an earlier paper, we determined the morphological types of galaxies in the Coma Cluster using data from the HST/ACS Coma Cluster treasury survey. We found that of the 132 members, 51 are non dwarfs and 81 are dwarfs. We define dwarfs to have a absolute luminosity $M_{F814W}\geq -18.5$ as in \cite{2012ApJ...746..136M}. In this paper, we determine the morphological types of these dwarf galaxies and make a detailed study of their properties. Using GALFIT, we determine the structural properties of our sample and with spectroscopic redshifts, we determined memberships and distances to identify dwarfs. A visual examination of the residual images reveals that our sample of 78 dwarf galaxies comprises of: dwarf lenticular (\textit{dS0}) 22\%, dwarf Elliptical ($dE$) 69\%, dwarf spirals (\textit{dSp}) 4\%, dwarf ring (\textit{dring}) 1\%, dwarf barred spirals (\textit{dSBp}) 3\% and dwarf irregular (\textit{dIrr}) 1\% galaxies. We find that the bulge-disk decomposition (S\'ersic + exponential) fits are good only for the \textit{dS0} galaxies. The remainder of the sample gives good fits only for single S\'ersic fits. The Colour Magnitude Relation (CMR) shows that the $dEs$ are redder and fainter than the rest of the sample (except one $dIrr$ galaxy). The Kormendy relation reveals that $dE$ galaxies have lower surface brightness than the rest of the sample. Our research leads us to the conclusion that dwarf galaxies appear to have a different formation and evolution process than non-dwarf galaxies., Comment: Accepted in JoAA