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We present a minimal cosmological solution to the hierarchy problem. Our model consists of a light pseudoscalar and an extra Higgs doublet in addition to the field content of the Standard Model. We consider a landscape of vacua with varying values of the electroweak vacuum expectation value (VEV). The vacuum energy in our model peaks in a region of the landscape where the electroweak VEV is non-zero and much smaller than the cutoff. During inflation, due to exponential expansion, such regions of the landscape with maximal vacuum energy, dominate the universe in volume, thus explaining the smallness of the electroweak scale with respect to the cutoff. The pseudoscalar potential in our model is that of a completely generic pseudogoldstone boson$-$not requiring the clockwork mechanism$-$and its field value never exceeds its decay constant or the Planck scale. Our mechanism is robust to the variation of other model parameters in the landscape along with the electroweak VEV. It also predicts a precise and falsifiable relationship between the masses and couplings of the different Higgs boson mass-eigenstates. Moreover, the pseudoscalar in our model can account for the observed dark matter relic density., Comment: 28 pages, 4 figures

The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, and therefore, it would be beneficial to authors if title generation can be automated. In this paper, we fine-tune pre-trained and large language models to generate titles of papers from their abstracts. We also use ChatGPT in a zero-shot setting to generate paper titles. The performance of the models is measured with ROUGE, METEOR, MoverScore, BERTScore and SciBERTScore metrics.

Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices. This paper presents an end-to-end pipeline for detecting and localizing fruits and vegetables in real-world scenarios. To achieve this, we have curated a dataset named FRUVEG67 that includes images of 67 classes of fruits and vegetables captured in unconstrained scenarios, with only a few manually annotated samples per class. We have developed a semi-supervised data annotation algorithm (SSDA) that generates bounding boxes for objects to label the remaining non-annotated images. For detection, we introduce the Fruit and Vegetable Detection Network (FVDNet), an ensemble version of YOLOv7 featuring three distinct grid configurations. We employ an averaging approach for bounding-box prediction and a voting mechanism for class prediction. We have integrated Jensen-Shannon divergence (JSD) in conjunction with focal loss to better detect smaller objects. Our experimental results highlight the superiority of FVDNet compared to previous versions of YOLO, showcasing remarkable improvements in detection and localization performance. We achieved an impressive mean average precision (mAP) score of 0.78 across all classes. Furthermore, we evaluated the efficacy of FVDNet using open-category refrigerator images, where it demonstrates promising results., Comment: 24 pages, 8 figures, 6 tables, Scientia Horticulturae

In his seminal work in 1923, Neyman studied the variance estimation problem for the difference-in-means estimator of the average treatment effect in completely randomized experiments. He proposed a variance estimator that is conservative in general and unbiased when treatment effects are homogeneous. While widely used under complete randomization, there is no unique or natural way to extend this estimator to more complex designs. To this end, we show that Neyman's estimator can be alternatively derived in two ways, leading to two novel variance estimation approaches: the imputation approach and the contrast approach. While both approaches recover Neyman's estimator under complete randomization, they yield fundamentally different variance estimators for more general designs. In the imputation approach, the variance is expressed as a function of observed and missing potential outcomes and then estimated by imputing the missing potential outcomes, akin to Fisherian inference. In the contrast approach, the variance is expressed as a function of several unobservable contrasts of potential outcomes and then estimated by exchanging each unobservable contrast with an observable contrast. Unlike the imputation approach, the contrast approach does not require separately estimating the missing potential outcome for each unit. We examine the theoretical properties of both approaches, showing that for a large class of designs, each produces conservative variance estimators that are unbiased in finite samples or asymptotically under homogeneous treatment effects.

The third-order anomalous Hall effect (TOAHE) driven by Berry connection polarizability in Dirac materials offers a promising avenue for exploring quantum geometric phenomena. We investigate the role of impurity scattering on TOAHE using the semiclassical Boltzmann framework, via a comparison of the intrinsic contributions (stemming from the Berry connection polarizability effect) with the extrinsic contributions caused by the disorder. To validate our theoretical findings, we employ a generalized two-dimensional low-energy Dirac model to analytically assess the intrinsic and extrinsic contributions to the TOAHE. Our analysis reveals distinct disorder-mediated effects, including skew scattering and side jump contributions. We also elucidate their intriguing dependencies on Fermi surface anisotropy and discuss opportunities for experimental exploration., Comment: 6 Pages, 1 Figure

The maritime industry stands at a critical juncture, where the imperative for technological advancement intersects with the pressing need for robust cybersecurity measures. Maritime cybersecurity refers to the protection of computer systems and digital assests within the maritime industry, as well as the broader network of interconnected components that make up the maritime ecosystem. In this survey, we aim to identify the significant domains of maritime cybersecurity and measure their effectiveness. We have provided an in-depth analysis of threats in key maritime systems, including AIS, GNSS, ECDIS, VDR, RADAR, VSAT, and GMDSS, while exploring real-world cyber incidents that have impacted the sector. A multi-dimensional taxonomy of maritime cyber attacks is presented, offering insights into threat actors, motivations, and impacts. We have also evaluated various security solutions, from integrated solutions to component specific solutions. Finally, we have shared open challenges and future solutions. In the supplementary section, we have presented definitions and vulnerabilities of vessel components that have discussed in this survey. By addressing all these critical issues with key interconnected aspects, this review aims to foster a more resilient maritime ecosystem., Comment: 36 pages, survey paper, submitting to ACM journals

We present the first explicit construction of two-sided lossless expanders in the unbalanced setting (bipartite graphs that have many more nodes on the left than on the right). Prior to our work, all known explicit constructions in the unbalanced setting achieved only one-sided lossless expansion. Specifically, we show that the one-sided lossless expanders constructed by Kalev and Ta-Shma (RANDOM'22) -- that are based on multiplicity codes introduced by Kopparty, Saraf, and Yekhanin (STOC'11) -- are, in fact, two-sided lossless expanders. Using our unbalanced bipartite expander, we easily obtain lossless (non-bipartite) expander graphs with high degree and a free group action. As far as we know, this is the first explicit construction of lossless (non-bipartite) expanders with $N$ vertices and degree $\ll N$.

Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-grained categorization. Moreover, foundational models trained on one domain do not generalize well on a different domain without fine-tuning. We attribute these to the limitations of the VLM's semantic representations and attempt to improve their fine-grained visual awareness using generative modeling. Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe). Utilizing text-to-image diffusion models, GCPL significantly improves the visio-linguistic synergy in class embeddings by conditioning on few-shot exemplars with learnable class prompts. CoMPLe builds on this foundation by introducing a contrastive learning component that encourages inter-class separation during the generative optimization process. Our empirical results demonstrate that such a generative class prompt learning approach substantially outperform existing methods, offering a better alternative to few shot image recognition challenges. The source code will be made available at: https://github.com/soumitri2001/GCPL., Comment: Accepted in BMVC 2024

The distribution of entanglement in a multiparty system can be described through the principles of monogamy or polygamy. Monogamy is a fundamental characteristic of entanglement that restricts its distribution among several number of parties(more than two). In this work, our aim is to explore how quantum entanglement can be distributed in accordance with monogamy relations by utilizing both the genuine multipartite entanglement measures and bipartite entanglement measures. Specifically, we treat source entanglement as the genuine multipartite entanglement measure and use the entanglement of formation specifically for bipartite cases. For GHZ class states, we analytically demonstrate that the square of the source entanglement serves as an upper bound for the sum of the squares of the entanglement of formation of the reduced subsystems, with some exceptions for specific non-generic GHZ states. We also present numerical evidence supporting this result for W class states. Additionally, we explore the monogamy relation by using accessible entanglement as an upper bound., Comment: 17 pages, 22 figures, revtex, comments welcome

For a Markov and stationary stochastic process described by the well-known classical master equation, we introduce complex transition rates instead of real transition rates to study the pre-thermal oscillatory behaviour in complex probabilities. Further, for purely imaginary transition rates we obtain persistent infinitely long lived oscillations in complex probability whose nature depends on the dimensionality of the state space. We also take a peek into cases where we perturb the relaxation matrix for a dichotomous process with an oscillatory drive where the relative sign of the angular frequency of the drive decides whether there will be dissipation in the complex probability or not., Comment: 6 pages, 4 figures

Optimizing tail latency while efficiently managing computational resources is crucial for delivering high-performance, latency-sensitive services in edge computing. Emerging applications, such as augmented reality, require low-latency computing services with high reliability on user devices, which often have limited computational capabilities. Consequently, these devices depend on nearby edge servers for processing. However, inherent uncertainties in network and computation latencies stemming from variability in wireless networks and fluctuating server loads make service delivery on time challenging. Existing approaches often focus on optimizing median latency but fall short of addressing the specific challenges of tail latency in edge environments, particularly under uncertain network and computational conditions. Although some methods do address tail latency, they typically rely on fixed or excessive redundancy and lack adaptability to dynamic network conditions, often being designed for cloud environments rather than the unique demands of edge computing. In this paper, we introduce SafeTail, a framework that meets both median and tail response time targets, with tail latency defined as latency beyond the 90^th percentile threshold. SafeTail addresses this challenge by selectively replicating services across multiple edge servers to meet target latencies. SafeTail employs a reward-based deep learning framework to learn optimal placement strategies, balancing the need to achieve target latencies with minimizing additional resource usage. Through trace-driven simulations, SafeTail demonstrated near-optimal performance and outperformed most baseline strategies across three diverse services., 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

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

We study small non-trivial solutions of quadratic congruences of the form $x_1^2+\alpha_2x_2^2+\alpha_3x_3^2\equiv 0 \bmod{q}$, with $q$ being an odd natural number, in an average sense. This extends previous work of the authors in which they considered the case of prime power moduli $q$. Above, $\alpha_2$ is arbitrary but fixed and $\alpha_3$ is variable, and we assume that $(\alpha_2\alpha_3,q)=1$. We show that for all $\alpha_3$ modulo $q$ which are coprime to $q$ except for a small number of $\alpha_3$'s, an asymptotic formula for the number of solutions $(x_1,x_2,x_3)$ to the congruence $x_1^2+\alpha_2x_2^2+\alpha_3x_3^2\equiv 0 \bmod{q}$ with $\max\{|x_1|,|x_2|,|x_3|\}\le N$ and $(x_3,q)=1$ holds if $N\ge q^{11/24+\varepsilon}$ and $q$ is large enough. It is of significance that we break the barrier 1/2 in the above exponent. Key tools in our work are Burgess's estimate for character sums over short intervals and Heath-Brown's estimate for character sums with binary quadratic forms over small regions whose proofs depend on the Riemann hypothesis for curves over finite fields. We also formulate a refined conjecture about the size of the smallest solution of a ternary quadratic congruence, using information about the Diophantine properties of its coefficients., Comment: 14 Pages

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them., Comment: To appear for publication in the FIE 2024 conference proceedings

The performance of fiber-fed astronomical spectrographs is highly influenced by the properties of fibers. The near-field and far-field scrambling characteristics have a profound impact on the line spread function (LSF) of the spectra. Focal ratio degradation (FRD) influences the output beam size, thereby affecting the throughput, as well as the size of the collimator and dispersion elements. While previous research has indicated that these properties depend on the shape of the fiber core and showed that non-circular core fibers can yield uniform near-field scrambling, the result remains inconclusive for far-field. In this study, we investigate the near-field and far-field scrambling properties, along with the FRD, of 50-micron core fibers with different core geometries. We find that in addition to excellent near-field scrambling, octagonal-core fibers can also produce more uniform far-field output when compared to circular-core fibers. They also have less FRD effect when being fed with a f/3 beam., Comment: 13 pages, 11 figures, SPIE proceedings, Ground-based and Airborne Instrumentation for Astronomy X

Sensory stimuli in animals are encoded into spike trains by neurons, offering advantages such as sparsity, energy efficiency, and high temporal resolution. This paper presents a signal processing framework that deterministically encodes continuous-time signals into biologically feasible spike trains, and addresses the questions about representable signal classes and reconstruction bounds. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons using a convolve-then-threshold mechanism with various convolution kernels. A closed-form solution to the inverse problem, from spike trains to signal reconstruction, is derived in the Hilbert space of shifted kernel functions, ensuring sparse representation of a generalized Finite Rate of Innovation (FRI) class of signals. Additionally, inspired by real-time processing in biological systems, an efficient iterative version of the optimal reconstruction is formulated that considers only a finite window of past spikes, ensuring robustness of the technique to ill-conditioned encoding; convergence guarantees of the windowed reconstruction to the optimal solution are then provided. Experiments on a large audio dataset demonstrate excellent reconstruction accuracy at spike rates as low as one-fifth of the Nyquist rate, while showing clear competitive advantage in comparison to state-of-the-art sparse coding techniques in the low spike rate regime., Comment: 22 pages, including a 9-page appendix, 8 figures. A GitHub link to the project implementation is embedded in the paper

For an algebraic number field $K$, the P\'{o}lya group of $K$, denoted by $Po(K),$ is the subgroup of the ideal class group $Cl_{K}$ generated by the ideal classes of the products of prime ideals of same norm. The number field $K$ is said to be P\'{o}lya if $Po(K)$ is trivial. Motivated by several recent studies on the group $Po(K)$ when $K$ is a totally real bi-quadratic field, we investigate the same with five distinct odd primes ramifying in $K/\mathbb{Q}$. This extends the previous results on this problem, where the number of distinct ramified primes was at most four., Comment: 7 - 2epsilon pages, comments are welcome

The current work reports a study on bounce cosmology with a highly generalized holographic dark fluid inspired by S. Nojiri and S. D. Odintsov, 2017, European Physical Journal C, 77, pp.1-8. The holographic dark fluid that is mostly used for late-time acceleration has been implemented to reconstruct towards realisation of cosmological bounce. We first used the most generalized Nojiri-Odintsov(NO) cutoff to implement the holographic dark fluid. Accordingly, we have reconstructed this dark fluid via some solutions of scale factors. With those solutions, we have explored the evolution of different cosmological parameters. We have examined the effects of each reconstructed parameter in the context of the realization of the cosmic bounce. Next, we use the analytical inferences of the scalar spectral index, tensor-to-scalar ratio, and slow-roll characteristics of the model to study a bounce inflationary scenario. Since inflation is usually associated with the existence of scalar fields, we looked at a possible relationship between NO generalized holographic dark energy and scalar field models. Plotting the evolution of the potential that results from the scalar fields against time. Finally, we investigated the GSL of thermodynamics in the pre-and post-bounce scenarios., Comment: 31 pages, 16 figures

We consider the problem of source sampling and transmission scheduling for age-of-information minimization in a system consisting of multiple energy harvesting (EH) sources and a sink node. At each time, one of the sources is selected by the scheduler and the quality of its channel to the sink is measured. This probed channel quality is then used to decide whether a source will sample an observation and transmit the packet to the sink in that time slot. We formulate this problem as a constrained Markov decision process (CMDP) assuming i.i.d. energy arrival and channel fading processes, and relax it using a Lagrange multiplier. We apply a near optimal Whittle's index policy to decide the node to be probed. Next, for the probed node, we derive an optimal threshold policy, which recommends source sampling and observation transmission from the probed source only when the measured channel quality is above a threshold. Our proposed policy is called Whittle's index and threshold based source scheduling and sampling (WITS3) policy. However, in order to calculate Whittle's indices, one must be aware of the underlying processes' transition matrices, which are occasionally concealed from the scheduler. Therefore, we further propose a variant Q-WITS3 of WITS3 based on Q-learning assisted by two timescale asynchronous stochastic approximation, which seeks to learn Whittle's indices and optimal policies for the case with unknown channel states and EH characteristics. Numerical results demonstrate the efficacy of our algorithms over two baseline policies., Comment: 10 pages, 7 figures

Cybercrime and the market for cyber-related compromises are becoming attractive revenue sources for state-sponsored actors, cybercriminals and technical individuals affected by financial hardships. Due to burgeoning cybercrime on new technological frontiers, efforts have been made to assist digital forensic investigators (DFI) and law enforcement agencies (LEA) in their investigative efforts. Forensic tool innovations and ontology developments, such as the Unified Cyber Ontology (UCO) and Cyber-investigation Analysis Standard Expression (CASE), have been proposed to assist DFI and LEA. Although these tools and ontologies are useful, they lack extensive information sharing and tool interoperability features, and the ontologies lack the latest Smart City Infrastructure (SCI) context that was proposed. To mitigate the weaknesses in both solutions and to ensure a safer cyber-physical environment for all, we propose the Smart City Ontological Paradigm Expression (SCOPE), an expansion profile of the UCO and CASE ontology that implements SCI threat models, SCI digital forensic evidence, attack techniques, patterns and classifications from MITRE. We showcase how SCOPE could present complex data such as SCI-specific threats, cybercrime, investigation data and incident handling workflows via an incident scenario modelled after publicly reported real-world incidents attributed to Advanced Persistent Threat (APT) groups. We also make SCOPE available to the community so that threats, digital evidence and cybercrime in emerging trends such as SCI can be identified, represented, and shared collaboratively.

Based on the conviction of switching information from locally accessible to locally hidden environs, the concept of hidden nonlocality activation has been recently highlighted by Bandyopadhyay et al. in [Phys. Rev. A 104, L050201 (2021)]. They demonstrate that a certain locally distinguishable set of pure quantum states can be transformed into an indistinguishable set with certainty by allowing local operations and classical communication(LOCC). As this transformation makes the set locally inaccessible to every subsystem, it is defined as the activation of genuine hidden nonlocality. In this paper, we observe that one class exhibits the previously mentioned nonlocal characteristics, harnessing local operation by a single party, while in contrast, another class demands cooperative endeavors among multiple local observers to reveal its nonlocal attributes. From this vantage point, we discern a stronger manifestation of locality, asserting that the latter class is inherently more local than the former. This analysis sheds light on the nuanced interplay between local and nonlocal phenomena within the framework of quantum state discrimination. Furthermore, we also explore their significant applications in the context of locally hiding information. Additionally, we introduce the concept of \emph{``strong local"} set and examine its comparison with different activable sets in terms of locality., Comment: 15 pages, 7 figures, latex2e, revtex, comments welcome

This article presents a new model for anisotropic compact stars that are confined to physical dark matter in the background of $f(T)$ teleparallel gravity. The model is based on the equation of state (EoS) of the bag model type and the Bose-Einstein dark matter density profile. The derived solutions meet the energy conditions, the causality conditions, and the required conditions on the stability factor and adiabatic index, indicating that they are physically well-behaved and represent the physical and stable matter configuration. We also determine the maximum mass, surface redshift, and compactness parameter at the surface. Interestingly, all of these numbers fall within the specified range, supporting the physical viability of our proposal. Additionally, the various masses that are derived for varying the model parameter $k$ correspond to five compact, realistic compact objects, including LMC X-4, Her X-1, 4U 1538-52, SAX J1808.4-3658, and Cen X-3. We have also illustrated the radially symmetric profiles of energy density and the moment of inertia for non-rotating stars., Comment: 23 pages, 28 figures

Background: Understanding the nuclear reactions between light charged nuclei at sub-coulomb energy region holds significant importance in several astrophysical processes. Determination of the precise reaction cross-section within the astrophysically important Gamow range is difficult because of electron screening. Various polynomial fits, R-Matrix and Indirect Trojan horse method estimate much higher electron screening energies as compared to the adiabatic limit. Purpose: Obtain the bare astrophysical S-factor of 6 Li(p,{\alpha})3 He using Multi-Layer Perceptron based Artificial Neural Network based analysis and extract the electron screening energies. Methods: Experimental S-factor of 6 Li(p,{\alpha})3 He, available in literature, are reanalyzed using the Multi-LayerPerceptron based Artificial Neural Network based algorithm to obtain the energy dependent astrophysical S-factor. Bare astrophysical S-factor is also calculated using the same Feed-forward Artificial Neural Network from the data range above 60 keV where the electron screening effect is expected to be negligible. Electron screening potential is then obtained by taking the ratio of total shielded S-factor with the bare S-factor. Results and Conclusions: The electron screening potential obtained from the Present work through the Artificial Neural network based algorithm is found to be 220 eV. The extracted electron screening potential through the present analysis indicates that the Artificial Neural Network might be an alternative tools for estimation the electron screening potential involving light nuclei., Comment: arXiv admin note: substantial text overlap with arXiv:2407.20049

In recent decades, there has been a significant increase in the measurement of complete fusion cross-sections for various reactions, with particular emphasis on the reactions involving weakly bound projectile. It has been well established that the complete fusion cross-section involving weakly bound nuclei is suppressed at above barrier energies due to the breakup effect. Accurate determination of suppression factor is essential to understand the effect of breakup on complete fusion suppression. In this study, Feedforward Artificial Neural Network (ANN) methods based on Multilayer Perceptron is utilized to estimate the complete fusion suppression factor for reactions involving 6,7 Li projectile from the comparison of ANN predicted reduced fusion functions (F (x)) with the Universal Fusion Function(F0 (x)). Average suppression factor has been estimated as 0.68 and 0.74 for 6 Li and 7 Li induced reactions respectively. Normalized Mean Squared Error(NMSE) has been calculated as 1.85% for training and 1.92% for testing data of ANN for 6 Li case, while for 7 Li cases, the same is for 3.73% and 6.48% respectively. Present results have been compared with three other alternative methods Support Vector Regression, Random Forest Regression and Gaussian Process Regression. Relevant performance matrices has been estimated. It has been observed that, ANN algorithm is giving the better accuracy as compared with other. This results indicate that ANN might be an alternative tool for estimation of complete fusion suppression factor for weakly bound projectile.

We undertake extensive analysis of English Premier League data over the period 2009/10 to 2017/18 to identify and rank key factors affecting the economic and footballing performances of the teams. Alternative end-of-season league tables are generated by re-ranking the teams based on five different descriptors - total expenditure, total funds spent on players, total funds spent on foreign players, the ratio of foreign to British players and the overall profit. The unequal distribution of resources and expenditure between the clubs is analyzed through Lorenz curves. A comparative analysis of the differences between the alternative tables and the conventional end-of-season league table establishes the most likely factors to influence the performances of the teams that we also rank using Principal Component Analysis. We find that the top teams in the league are also those that tend to have the highest expenditure overall, for all players, including foreign players; they also have the highest ratios of foreign to British players. Our statistical and machine learning study also indicates that successful performance on the field may not guarantee healthy profits at the end of the season., Comment: Graphical Abstract & 8 figures in the main text, 3 Appendices with additional figures and tables

The study explores Artificial Intelligence (AI) powered modeling to predict the evolution of cancer tumor cells in mice under different forms of treatment. The AI models are analyzed against varying ambient and systemic parameters, e.g. drug dosage, volume of the cancer cell mass, and time taken to destroy the cancer cell mass. The data required for the analysis have been synthetically extracted from plots available in both published and unpublished literature (primarily using a Matlab architecture called "Grabit"), that are then statistically standardized around the same baseline for comparison. Three forms of treatment are considered - saline (multiple concentrations used), magnetic nanoparticles (mNPs) and fluorodeoxyglycose iron oxide magnetic nanoparticles (mNP-FDGs) - analyzed using three Machine Learning (ML) algorithms, Decision Tree (DT), Random Forest (RF), Multilinear Regression (MLR), and a Deep Learning (DL) module, the Adaptive Neural Network (ANN). The AI models are trained on 60-80% data, the rest used for validation. Assessed over all three forms of treatment, ANN consistently outperforms other predictive models. Our models predict mNP-FDG as the most potent treatment regime that kills the cancerous tumor completely in ca 13 days from the start of treatment. The models can be generalized to other forms of cancer treatment regimens., Comment: 7 figures, 24 pages

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray satellite telescopes, such as the probe-class Advanced X-ray Imaging Satellite (AXIS), will require excellent soft energy response in their imaging detectors to enable maximum discovery potential. In order to characterize Charge-Coupled Device (CCD) and Single Electron Sensitive Read Output (SiSeRO) detectors in the soft X-ray region, the X-ray Astronomy and Observational Cosmology (XOC) group at Stanford has developed, assembled, and commissioned a 2.5-meter-long X-ray beamline test system. The beamline is designed to efficiently produce monoenergetic X-ray fluorescence lines in the 0.3-10 keV energy range and achieve detector temperatures as low as 173 K. We present design and simulation details of the beamline, and discuss the vacuum, cooling, and X-ray fluorescence performance achieved. As a workhorse for future detector characterization at Stanford, the XOC beamline will support detector development for a broad range of X-ray astronomy instruments., Comment: To appear in SPIE Astronomical Telescopes and Instrumentation 2024 proceedings. 17 pages, 13 figures

The Advanced X-ray Imaging Satellite (AXIS) is a NASA probe class mission concept designed to deliver arcsecond resolution with an effective area ten times that of Chandra (at launch). The AXIS focal plane features an MIT Lincoln Laboratory (MIT-LL) X-ray charge-coupled device (CCD) detector working in conjunction with an application specific integrated circuit (ASIC), denoted the Multi-Channel Readout Chip (MCRC). While this readout ASIC targets the AXIS mission, it is applicable to a range of potential X-ray missions with comparable readout requirements. Designed by the X-ray astronomy and Observational Cosmology (XOC) group at Stanford University, the MCRC ASIC prototype (MCRC-V1.0) uses a 350 nm technology node and provides 8 channels of high speed, low noise, low power consumption readout electronics. Each channel implements a current source to bias the detector output driver, a preamplifier to provide gain, and an output buffer to interface directly to an analog-to-digital (ADC) converter. The MCRC-V1 ASIC exhibits comparable performance to our best discrete electronics implementations, but with ten times less power consumption and a fraction of the footprint area. In a total ionizing dose (TID) test, the chip demonstrated a radiation hardness equal or greater to 25 krad, confirming the suitability of the process technology and layout techniques used in its design. The next iteration of the ASIC (MCRC-V2) will expand the channel count and extend the interfaces to external circuits, advancing its readiness as a readout-on-a-chip solution for next generation X-ray CCD-like detectors. This paper summarizes our most recent characterization efforts, including the TID radiation campaign and results from the first operation of the MCRC ASIC in combination with a representative MIT-LL CCD., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Future strategic X-ray astronomy missions will require unprecedentedly sensitive wide-field imagers providing high frame rates, low readout noise and excellent soft energy response. To meet these needs, our team is employing a multi-pronged approach to advance several key areas of technology. Our first focus is on advanced readout electronics, specifically integrated electronics, where we are collaborating on the VERITAS readout chip for the Athena Wide Field Imager, and have developed the Multi-Channel Readout Chip (MCRC), which enables fast readout and high frame rates for MIT-LL JFET (junction field effect transistor) CCDs. Second, we are contributing to novel detector development, specifically the SiSeRO (Single electron Sensitive Read Out) devices fabricated at MIT Lincoln Laboratory, and their advanced readout, to achieve sub-electron noise performance. Hardware components set the stage for performance, but their efficient utilization relies on software and algorithms for signal and event processing. Our group is developing digital waveform filtering and AI methods to augment detector performance, including enhanced particle background screening and improved event characterization. All of these efforts make use of an efficient, new X-ray beamline facility at Stanford, where components and concepts can be tested and characterized., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events., Comment: Proceedings of the SPIE, Astronomical Telescopes and Instrumentation, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detection technology that can, in principle, provide significantly greater responsivity and improved noise performance than traditional charge coupled device (CCD) readout circuitry. The SiSeRO, developed by MIT Lincoln Laboratory, uses a p-MOSFET transistor with a depleted back-gate region under the transistor channel; as charge is transferred into the back gate region, the transistor current is modulated. With our first generation SiSeRO devices, we previously achieved a responsivity of around 800 pA per electron, an equivalent noise charge (ENC) of 4.5 electrons root mean square (RMS), and a full width at half maximum (FWHM) spectral resolution of 130 eV at 5.9 keV, at a readout speed of 625 Kpixel/s and for a detector temperature of 250 K. Importantly, since the charge signal remains unaffected by the SiSeRO readout process, we have also been able to implement Repetitive Non-Destructive Readout (RNDR), achieving an improved ENC performance. In this paper, we demonstrate sub-electron noise sensitivity with these devices, utilizing an enhanced test setup optimized for RNDR measurements, with excellent temperature control, improved readout circuitry, and advanced digital filtering techniques. We are currently fabricating new SiSeRO detectors with more sensitive and RNDR-optimized amplifier designs, which will help mature the SiSeRO technology in the future and eventually lead to the pathway to develop active pixel sensor (APS) arrays using sensitive SiSeRO amplifiers on each pixel. Active pixel devices with sub-electron sensitivity and fast readout present an exciting option for next generation, large area astronomical X-ray telescopes requiring fast, low-noise megapixel imagers., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

In cognitive systems, recent emphasis has been placed on studying the cognitive processes of the subject whose behavior was the primary focus of the system's cognitive response. This approach, known as inverse cognition, arises in counter-adversarial applications and has motivated the development of inverse Bayesian filters. In this context, a cognitive adversary, such as a radar, uses a forward Bayesian filter to track its target of interest. An inverse filter is then employed to infer the adversary's estimate of the target's or defender's state. Previous studies have addressed this inverse filtering problem by introducing methods like the inverse Kalman filter (I-KF), inverse extended KF (I-EKF), and inverse unscented KF (I-UKF). However, these filters typically assume additive Gaussian noise models and/or rely on local approximations of non-linear dynamics at the state estimates, limiting their practical application. In contrast, this paper adopts a global filtering approach and presents the development of an inverse particle filter (I-PF). The particle filter framework employs Monte Carlo (MC) methods to approximate arbitrary posterior distributions. Moreover, under mild system-level conditions, the proposed I-PF demonstrates convergence to the optimal inverse filter. Additionally, we propose the differentiable I-PF to address scenarios where system information is unknown to the defender. Using the recursive Cramer-Rao lower bound and non-credibility index (NCI), our numerical experiments for different systems demonstrate the estimation performance and time complexity of the proposed filter., Comment: 13 pages, 4 figures

In this paper, we report the excitation of coherent density and potential fluctuations induced by magnetohydrodynamic (MHD) activity in the edge plasma region of ADITYA-U Tokamak. When the amplitude of the MHD mode, mainly the m/n = 2/1, increases beyond a threshold value of 0.3-0.4 %, coherent oscillations in the density and potential fluctuations are observed having the same frequency as that of the MHD mode. The mode numbers of these MHD induced density and potential fluctuations are obtained by Langmuir probes placed at different radial, poloidal, and toroidal locations in the edge plasma region. Detailed analyses of these Langmuir probe measurements reveal that the coherent mode in edge potential fluctuation has a mode structure of m/n = 2/1 whereas the edge density fluctuation has an m/n = 1/1 structure. It is further observed that beyond the threshold, the coupled power fraction scales almost linearly with the magnitude of magnetic fluctuations. Furthermore, the rise rates of the coupled power fraction for coherent modes in density and potential fluctuations are also found to be dependent on the growth rate of magnetic fluctuations. The disparate mode structures of the excited modes in density and plasma potential fluctuations suggest that the underlying mechanism for their existence is most likely due to the excitation of the global high-frequency branch of zonal flows occurring through the coupling of even harmonics of potential to the odd harmonics of pressure due to 1/R dependence of the toroidal magnetic field.

We analyse the impact of dark matter density spike around the Milky Way's supermassive black hole (SMBH), Sgr A$^*$, in probing the Bino-dominated neutralino dark matter (DM) $\tilde \chi_1^0$ within the MSSM, which typically produces relatively faint signals in the conventional DM halos. In particular, we explore the indirect search prospects of sub-TeV Bino-Higgsino and Bino-Wino-Higgsino DM in the MSSM, consistent with the supersymmetric predictions required to explain the anomalous magnetic moment of the muon. Typical over-abundance of Bino DM is ameliorated with slepton and/or Wino coannihilations. The lightest neutralino, thus, may be associated with a compressed supersymmetric particle spectrum, which, in general, is difficult to probe at conventional LHC searches. Similarly, for a rather tiny Higgsino mixing, $\tilde \chi_1^0$ does not offer much prospect to assess its predictions at dark matter direct detection searches. Accommodating the inclusive effects of density spike, here, we present the requisite boost factor to facilitate $\gamma-$ray searches of Bino-dominated DM in the MSSM, especially focusing on the Fermi-LAT and HESS observations., Comment: 29 pages, 11 figures, comments are welcome

Smart contracts are increasingly used in critical use cases (e.g., financial transactions). Thus, it is pertinent to ensure that end-users understand the transfer risks in smart contracts. To address this, we investigate end-user comprehension of risks in the most popular Ethereum smart contract (i.e., USD Tether (USDT)) and their prevalence in the top ERC-20 smart contracts. We focus on five transfer risks with severe impact on transfer outcomes and user objectives, including users being blacklisted, contract being paused, and contract being arbitrarily upgraded. Firstly, we conducted a user study investigating end-user comprehension of smart contract transfer risks with 110 participants and USDT/MetaMask. Secondly, we performed manual and automated source code analysis of the next top (78) ERC-20 smart contracts (after USDT) to identify the prevalence of these risks. Results show that end-users do not comprehend real risks: most (up to 71.8% of) users believe contract upgrade and blacklisting are highly severe/surprising. More importantly, twice as many users find it easier to discover successful outcomes than risky outcomes using the USDT/MetaMask UI flow. These results hold regardless of the self-rated programming and Web3 proficiency of participants. Furthermore, our source code analysis demonstrates that the examined risks are prevalent in up to 19.2% of the top ERC-20 contracts. Additionally, we discovered (three) other risks with up to 25.6% prevalence in these contracts. This study informs the need to provide explainable smart contracts, understandable UI and relevant information for risky outcomes.

This study simulates the characteristics of spherically symmetric, anisotropic compact stellar bodies with electrical charge within the context of the $f(Q)$ theory of gravity. Employing the Krori-Barua metric ansatz (K.D. Krori, J. Barua, J. Phys. A: Math. Gen. 8 (1975) 508) along with a linear form of $f(Q)$ model, {\it viz.}, $f(Q)=\alpha_{0}+\alpha_{1}Q$, we obtain a tractable set of exact relativistic solutions of the field equations. A specific form of charge $(q=q_{0}r^{3})$ is considered here for the present analysis. Using the data sets of $\rho$ and $p_{r}$ along with the method of curve fitting, we have obtained the best fit equation of state in the model, which is incorporated in the numerical solutions of the TOV equations to determine the maximum mass and radius in this scenario. With increasing charge intensity $(q_{0})$ from 0.0002 to 0.0006, the maximum mass ranges from $2.838-2.869~M_{\odot}$, and the corresponding radii range from $12.0039-12.0822~Km$. Moreover, the predicted radii of some recently observed pulsars and GW 190814 show that our model also complies with the estimated radii based on the observational results. Our model is found to satisfy all the characteristic features, such as behaviour of matter variables, causality condition, energy constraints and stability criteria, which are pertinent in the context of a stable stellar configuration to emerge as a viable and physically acceptable stellar model in the framework of $f(Q)$ gravity., Comment: 20 pages, 16 figures

We investigate the effects of prior selection on the inferred mass and spin parameters of the neutron star-black hole merger GW230529\_181500. Specifically, we explore models motivated by astrophysical considerations, including massive binary and pulsar evolution. We examine mass and spin distributions of neutron stars constrained by radio pulsar observations, alongside black hole spin observations from previous gravitational wave detections. We show that the inferred mass distribution highly depends upon the spin prior. Specifically, under the most restrictive, binary stellar evolution models, we obtain narrower distributions of masses with a black hole mass of $4.1^{+0.2}_{-0.3} \,M_\odot$ and neutron star mass of $1.3^{+0.1}_{-0.1} \,M_\odot$ where, somewhat surprisingly, it is the prior on component spins which has the greatest impact on the inferred mass distributions. Re-weighting using neutron star mass and spin priors from observations of radio pulsars, with black hole spins from observations of gravitational waves, yields the black hole and the neutron star masses to be $3.8^{+0.5}_{-0.6} \,M_\odot$ and $1.4^{+0.2}_{-0.1} \,M_\odot$ respectively. The sequence of compact object formation -- whether the neutron star or the black hole formed first -- cannot be determined at the observed signal-to-noise ratio. However, there is no evidence that the black hole was tidally spun up., Comment: Submitted to MNRAS Letters

Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. In this work, we construct a dataset of images and goals across different scenarios such as searching through kitchens or navigating outdoors. We then investigate how grounded instruction generation methods can provide contextually-relevant navigational guidance to users in these instances. Through a sighted user study, we demonstrate that large pretrained language models can produce correct and useful instructions perceived as beneficial for BLV users. We also conduct a survey and interview with 4 BLV users and observe useful insights on preferences for different instructions based on the scenario., Comment: Accepted as RO-MAN 2024 Late Breaking Report

In a warming world, heatwaves over India have become intense and are causing severe health impacts. Studies have identified the presence of amplified Rossby waves and their association with the intensification of heatwaves. Earlier studies have identified two dominant modes of temperature variability in India and their possible role in the development of dry (mode 1) and moist (mode 2) heatwaves. These modes are associated with midlatitude Rossby waves intruding over the Indian region. However the role of regional forcing and the teleconnection behind the intensification of the heatwaves over India is missing. The present study has analyzed the dynamical mechanisms for the regional intensification of the circulation features associated with the dominant moist heatwave mode (mode 2). Considering the predominant barotropic nature of the observed circulation features of the mode, a simple barotropic vorticity equation model forced with extratropical and regional vorticity sources is used to understand the intensification of the heat waves. It was found that a wave response initiated by a cyclonic vorticity over the Bay of Bengal superimposes with the mid-latitude anticyclonic vorticity generated Rossby waves intruding over India. This superimposition results in the amplification and persistence of the anticyclonic vorticity phase over the Northwest Indian region, leading to the intensification of circulation. It was also found that the barotropically forced intensified circulation leads to the intensification of the heat stress. Under a climate change scenario, different circulation regimes, characterized by zonal stationary wave number and jet speed, which can favor the intensification are also identified.

Complex biological networks, comprising metabolic reactions, gene interactions, and protein interactions, often exhibit scale-free characteristics with power-law degree distributions. However, empirical studies have revealed discrepancies between observed biological network data and ideal power-law fits, highlighting the need for improved modeling approaches. To address this challenge, we propose a novel family of distributions, building upon the baseline Burr distribution. Specifically, we introduce the compounded Burr (CBurr) distribution, derived from a continuous probability distribution family, enabling flexible and efficient modeling of node degree distributions in biological networks. This study comprehensively investigates the general properties of the CBurr distribution, focusing on parameter estimation using the maximum likelihood method. Subsequently, we apply the CBurr distribution model to large-scale biological network data, aiming to evaluate its efficacy in fitting the entire range of node degree distributions, surpassing conventional power-law distributions and other benchmarks. Through extensive data analysis and graphical illustrations, we demonstrate that the CBurr distribution exhibits superior modeling capabilities compared to traditional power-law distributions. This novel distribution model holds great promise for accurately capturing the complex nature of biological networks and advancing our understanding of their underlying mechanisms.

In this work, we systematically expose and measure the inconsistency and knowledge gaps of Large Language Models (LLMs). Specifically, we propose an automated testing framework (called KONTEST) which leverages a knowledge graph to construct test cases. KONTEST probes and measures the inconsistencies in the LLM's knowledge of the world via a combination of semantically-equivalent queries and test oracles (metamorphic or ontological oracle). KONTEST further mitigates knowledge gaps via a weighted LLM model ensemble. Using four state-of-the-art LLMs (Falcon, Gemini, GPT3.5, and Llama2), we show that KONTEST generates 19.2% error inducing inputs (1917 errors from 9983 test inputs). It also reveals a 16.5% knowledge gap across all tested LLMs. KONTEST's mitigation method reduces LLM knowledge gap by 32.48%. Our ablation study further shows that GPT3.5 is not suitable for knowledge-based consistency testing because it is only 60%-68% effective in knowledge construction., Comment: 14 pages, 6 figures, 14 tables, Submitted to ACL ARR (June 2024)

We establish higher order trace formulas for pairs of contractions along a multiplicative path generated by a self-adjoint operator in a Schatten-von Neumann ideal, removing earlier stringent restrictions on the kernel and defect operator of the contractions and enlarging the set of admissible functions. We also derive higher order trace formulas for maximal dissipative operators under relaxed assumptions and new simplified trace formulas for unitary and resolvent comparable self-adjoint operators. The respective spectral shift measures are absolutely continuous and, in the case of contractions, the set of admissible functions for the $n$th order trace formula on the unit circle includes the Besov class $B^n_{\infty, 1}(\T)$. Both aforementioned properties are new in the mentioned generality., Comment: Results are extended to an admissible function class that includes Besov spaces

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication

Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. They significantly undermine the ability of high-performance neural networks by forcing misclassifications. These attacks introduce minute and structured perturbations or alterations in the test samples, imperceptible to human annotators in general, but trained neural networks and other models are sensitive to it. Historically, adversarial attacks have been first identified and studied in the domain of image processing. In this paper, we study adversarial examples in the field of natural language processing, specifically text classification tasks. We investigate the reasons for adversarial vulnerability, particularly in relation to the inherent dimensionality of the model. Our key finding is that there is a very strong correlation between the embedding dimensionality of the adversarial samples and their effectiveness on models tuned with input samples with same embedding dimension. We utilize this sensitivity to design an adversarial defense mechanism. We use ensemble models of varying inherent dimensionality to thwart the attacks. This is tested on multiple datasets for its efficacy in providing robustness. We also study the problem of measuring adversarial perturbation using different distance metrics. For all of the aforementioned studies, we have run tests on multiple models with varying dimensionality and used a word-vector level adversarial attack to substantiate the findings., Comment: This paper is accepted for publication at EURASIP Journal on Information Security in 2024

This study investigates the nonlinear stability and dynamics of gravity-driven viscous films on a vertical rotating cylinder, considering both outer and inner surface flows with slip conditions at the cylinder wall. We develop an asymptotic model for the combined effects of rotation and wall slippage. Linear stability analysis indicates that wall slippage enhances instability on both surfaces, while rotation has differing impacts: it amplifies instability due to slip for outer surface flow but reduces it for inner surface flow. A weakly nonlinear stability analysis is then conducted to explore the combined impact of rotation and wall slip on flow stability beyond the linear regime, including the bifurcation of the nonlinear evolution equation for both surfaces. The traveling wave solution of the model is analyzed, showing how rotation affects nonlinear wave speed with a slippery wall. A stability analysis of the traveling wave solutions is also performed. Numerical simulations of the nonlinear evolution of the free surface reveal that increasing slip length enhances the choke phenomenon in inner surface flow, while rotation can delay this effect. Additionally, simulations show that for flow along the outer surface of a slippery rotating cylinder, the film tends to break up into droplets in the presence of rotation., Comment: 23 pages, 15 figures

Most optical spectropolarimeters built to date operate as long-slit or point-source instruments; they are inefficient for observations of extended objects such as galaxies and nebulae. 2D spectropolarimetry technique development is a major challenge in astronomical instrumentation. At the South African Astronomical Observatory (SAAO) FiberLab, we are developing a spectropolarimetry capable Integral Field front-end called FiberPol(-6D) for the existing SpUpNIC spectrograph on the SAAO 1.9 m telescope. SpUpNIC is a general purpose 2 arc-minute long-slit spectrograph with a grating suite covering the wavelength range from 350 to 1000 nm and at spectral resolutions between 500 and 6000. FiberPol generates 6D observational data: x-y spatial dimensions, wavelength, and the three linear Stokes parameters $I$, $q$ and $u$. Using a rotating half-wave plate and a Wollaston prism, FiberPol executes two-channel polarimetry, and each channel is fed to an array of 14 fibers, corresponding to a field of view of $10\times20~arcseconds^2$ sampled with 2.9 arcsecond diameter fiber cores. These fiber arrays are then rerouted to form a pseudo-slit input to SpUpNIC. FiberPol aims to achieve a polarimetric accuracy of 0.1 % per spectral resolution bin. Further, it can also function as a non-polarimetric integral-field unit. The instrument design has been completed and it is currently being assembled and characterized in the lab. It is scheduled for on-sky commissioning in the second half of 2024. In this paper, we present the scientific and technical goals of FiberPol, its overall design and initial results from the lab assembly and testing. FiberPol is a low cost technology demonstrator, and the entire system predominantly employs small size, commercial off-the-shelve optics and optomechanical components. It can be modified and replicated for use on any existing spectrograph, especially on bigger telescopes., Comment: 13 pages; presented at 2024 SPIE Astronomical Telescopes + Instrumentation conference

We present an experimental investigation of the magnetic ground state in Ba$_4$NbIr$_3$O$_{12}$, a fractional valent trimer iridate. X-ray absorption and photoemission spectroscopy show that the Ir valence lies between 3+ and 4+ while Nb is pentavalent. Combined dc/ac magnetization, specific heat, and muon spin rotation/relaxation ($\mu$SR) measurements reveal no magnetic phase transition down to 0.05~K. Despite a significant Weiss temperature ($\Theta_{\mathrm{W}} \sim -15$ to $-25$~K) indicating antiferromagnetic correlations, a quantum spin-liquid (QSL) phase emerges and persists down to 0.1~K. This state likely arises from geometric frustration in the edge-sharing equilateral triangle Ir network. Our $\mu$SR analysis reveals a two-component depolarization, arising from the coexistence of rapidly (90\%) and slowly (10\%) fluctuating Ir moments. Powder x-ray diffraction and Ir-L$_3$edge x-ray absorption fine structure spectroscopy identify ~8-10\% Nb/Ir site-exchange, reducing frustration within part of the Ir network, and likely leading to the faster muon spin relaxation, while the structurally ordered Ir ions remain highly geometrically frustrated, giving rise to the rapidly spin-fluctuating QSL ground state. At low temperatures, the magnetic specific heat varies as $\gamma T + \alpha T^2$, indicating gapless spinon excitations, and possible Dirac QSL features with linear spinon dispersion, respectively., Comment: 27 pages, 15 figures