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Kalb-Ramond (KR) gravity is an intriguing model incorporating local Lorentz violation, and black hole (BH) solutions are known to exist. In this study, we investigate some crucial aspects of BHs endowed with a global monopole charge in the self-interacting KR field. Specifically, we study the quasinormal modes (QNMs) corresponding to scalar, electromagnetic, and gravitational perturbations; derive rigorous bounds for the greybody factors (GBFs); and examine the sparsity of Hawking radiation. The effects of the model parameters $\ell$ (Lorentz-violating parameter in KR gravity) and $\eta$ (monopole charge) on these phenomena are elaborated. First, QNMs are evaluated with high precision using the 13\textsuperscript{th}-order Pad\'{e}-averaged WKB method and cross-examined via time-domain analyses within an acceptable parameter space. The results show that the estimated QNMs are more sensitive to $\ell$; however, both model parameters influence the frequency spectra. The derived bounds on the GBFs aid in further constraining the parameter space. It is shown that $\ell$ and $\eta$ have a similar effect on the greybody bounds. Furthermore, positive and negative values of $\ell$ have opposing effects in that the bounds are reversed for the two cases. The analyses of the Hawking radiation sparsity highlight the effect of $\ell$, and two scenarios are noted: either the radiation emitted is less sparse than Hawking radiation, or it is more sparse during the evaporation phase. Thus, this work presents a comprehensive account of BHs in KR gravity with a global monopole charge.

Teaching applied ethics in computer science has shifted from a perspective of teaching about professional codes of conduct and an emphasis on risk management towards a broader understanding of the impacts of computing on humanity and the environment and the principles and practices of responsible computing. One of the primary shifts in the approach to teaching computing ethics comes from research in the social sciences and humanities. This position is grounded in the idea that all computing artifacts, projects, tools, and products are situated within a set of ideas, attitudes, goals, and cultural norms. This means that all computing endeavors have embedded within them a set of values. To teach responsible computing always requires us to first recognize that computing happens in a context that is shaped by cultural values, including our own professional culture and values. The purpose of this paper is to highlight current scholarship, principles, and practices in the teaching of responsible computing in undergraduate computer science settings. The paper is organized around four primary sections: 1) a high-level rationale for the adoption of different pedagogical approaches based on program context and course learning goals, 2) a brief survey of responsible computing pedagogical approaches; 3) illustrative examples of how topics within the CS 2023 Social, Ethical, and Professional (SEP) knowledge area can be implemented and assessed across the broad spectrum of undergraduate computing courses; and 4) links to examples of current best practices, tools, and resources for faculty to build responsible computing teaching into their specific instructional settings and CS2023 knowledge areas., Comment: This work was done as part of ACM/IEEE-CS/AAAI Computer Science Curricula 2023 (csed.acm.org)

Van der Waals polytypes are commensurate configurations of two-dimensional layers with discrete crystalline symmetries and distinct stacking-dependent properties. In graphitic polytypes, the different stacking arrangements of graphene sheets exhibit rich electronic phases, such as intrinsic electric polarizations, orbital magnetizations, superconductivity, and anomalous fractional Hall states. Switching between these metastable periodic configurations by controlling interlayer shifts unlocks intriguing multiferroic responses. Here, we report super-lubricant arrays of polytypes (SLAP) devices, with nanometer-scale islands of Bernal polytypes that switch into Rhombohedral crystals and vice versa under a shear force as low as 6 nano-Newtons. We assemble these four-layer SLAP structures by aligning a pair of graphene bilayers above and under circular cavities in a misaligned spacer layer. Using local current measurements, we detect the shifts between the active bilayers and reveal long-range elastic relaxations outside the cavities that enable efficient nucleation and spontaneous sliding of stacking dislocation inside the islands. We demonstrate configurable, deterministic, and robust polytype switching by confining these boundary strips in narrow cavity channels that connect the islands. Such controlled switching between elastically-coupled single-crystalline islands is appealing for novel multiferroic SlideTronic applications.

Dynamic analysis and especially fuzzing are challenging tasks for embedded firmware running on modern low-end Microcontroller Units (MCUs) due to performance overheads from instruction emulation, the difficulty of emulating the vast space of available peripherals, and low availability of open-source embedded firmware. Consequently, efficient security testing of MCU firmware has proved to be a resource- and engineering-heavy endeavor. EmbedFuzz introduces an efficient end-to-end fuzzing framework for MCU firmware. Our novel firmware transplantation technique converts binary MCU firmware to a functionally equivalent and fuzzing-enhanced version of the firmware which executes on a compatible high-end device at native performance. Besides the performance gains, our system enables advanced introspection capabilities based on tooling for typical Linux user space processes, thus simplifying analysis of crashes and bug triaging. In our evaluation against state-of-the-art MCU fuzzers, EmbedFuzz exhibits up to eight-fold fuzzing throughput while consuming at most a fourth of the energy thanks to its native execution.

The search for heavy $W'$ bosons in their decay modes to a lepton and a heavy neutrino offers a promising avenue for probing new physics beyond the Standard Model. This work focuses on such a signature with an energetic lepton plus a fat jet, originating from the heavy neutrino and containing a lepton. We have employed the jet substructure techniques to isolate the embedded lepton as a subjet of the fat jet. The Lepton Subjet Fraction ($LSF$) and Lepton Mass Drop ($LMD$) variables constructed from the lepton subjet help in separating the signal region from the background. We further study the polarization properties of the $W'$ coupling to the lepton and heavy neutrino through the decay products of the neutrino. Instead of relying on a specific model, we employ generic couplings and explore the discrimination power. Jet substructure-based angular variables $z_\ell$, $z_\theta$, and $z_k$ are combined to form BDT scores to obtain better separation power between left-chiral ($V-A$) and right-chiral ($V+A$) coupling configurations. By using $CL_s$ type profile likelihood estimator, we could achieve $\sim$ 2$\sigma$ $-$ 3$\sigma$ significance of excluding one coupling configuration in favour of the other., Comment: 28 pages, 10 figures, 5 tables

Aiming to uncover the CP properties of spin-0 particle Dark Matter (DM), we explore a two-component DM scenario within the framework of 3-Higgs Doublet Models (3HDMs), a well-motivated set-up previously studied due to the complementarity of its collider and astrophysical probes. We devise benchmark points in which the two components of DM have same CP in one case and opposite CP in another. We then show several cross section distributions of observables at collider experiments where the two cases are clearly distinguishable., Comment: 27 pages, 10 figures

Compared to electronic phase transitions, structural phase transitions of crystals are challenging to control due to the energy cost of breaking dense solid bonds. Recently, however, electric field switching of stacking configuration between honeycomb layers, held together by relatively weak van der Waals (vdW) attractions, was demonstrated. In response to the external fields, the layers slide between commensurate meta-stable configurations with discrete symmetries and distinct lattice orientations. These 2D vdW polytypes host diverse electronic orders such as ferroelectricity and magnetism, providing multiferroic switching via lubricant sliding of incommensurate boundary strips. Ahead, we address recent observations in honeycomb polytypes and identify remaining challenges for extending this conceptual "SlideTronics" mechanism into rapid, local, and practical multiferroic devices. The stacking energies, symmetries, and orbital overlaps that underlie the band structures and internal charge distributions are discussed, along with poly-properties like interfacial-ferroelectricity, ladder-like cumulative polarization, superconductivity, and orbital magnetic orders. Distinct from conventional 3D multiferroic crystals, the 2D vdW assembly and the sliding switching mechanism open poly-opportunities for novel device concepts.

Over the last four decades, a number of modified gravity theories have been proposed to study cosmological phenomena as they can provide solutions for some of the shortcomings of Einstein's gravity in explaining early and late time accelerations of the observed Universe, the existence of dark matter, singularities at center of Black holes etc. The theoretical and observational challenges faced by the $\Lambda$CDM model also point towards the necessity for looking beyond General Relativity. In this direction, recently $f(\phi, T)$ gravity has been proposed in literature where the non-minimal coupling of the scalar field $\phi$ with the trace of energy-momentum tensor $T$ has been introduced in the Einstein-Hilbert action. Considering the Hilltop potential, we have studied the slow-roll inflation in the framework of $f(\phi, T)$ gravity. It is found that Hilltop inflationary models in $f(\phi, T)$ gravity are viable when seen in the light of latest Planck data.

We study warm inflation in the framework of $f(\phi)T$ gravity, where $\phi$ is the inflaton and $T$ is the trace of the energy-momentum tensor. The inflaton field is assumed to roll on the natural potential and the result is analyzed in light of Planck 2018 and BICEP/Keck 2021 data. We start our work by obtaining the field equations under slow-roll approximations. We then evaluate the scalar and tensor power spectra and their corresponding spectral index and tensor-to-scalar ratio with a temperature-dependent form of the dissipation coefficient during the inflationary era. We find that the warm inflation model in $f(\phi)T$ gravity is compatible with observational bands.

Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. To formalize this, we show that the hardness of information recall reduces to the hardness of a problem called set disjointness (SD), a quintessential problem in communication complexity that requires a streaming algorithm (e.g., recurrent model) to decide whether inputted sets are disjoint. We empirically and theoretically show that the recurrent memory required to solve SD changes with set order, i.e., whether the smaller set appears first in-context. Our analysis suggests, to mitigate the reliance on data order, we can put information in the right order in-context or process prompts non-causally. Towards that end, we propose: (1) JRT-Prompt, where context gets repeated multiple times in the prompt, effectively showing the model all data orders. This gives $11.0 \pm 1.3$ points of improvement, averaged across $16$ recurrent LMs and the $6$ ICL tasks, with $11.9\times$ higher throughput than FlashAttention-2 for generation prefill (length $32$k, batch size $16$, NVidia H100). We then propose (2) JRT-RNN, which uses non-causal prefix-linear-attention to process prompts and provides $99\%$ of Transformer quality at $360$M params., $30$B tokens and $96\%$ at $1.3$B params., $50$B tokens on average across the tasks, with $19.2\times$ higher throughput for prefill than FA2.

We report improved wave function for mesons in QCD potential model approach using multiplicative method for solution of Schr\"{o}dinger equation for the extreme cases of inter-quark separations ($r\rightarrow0$ and $r\rightarrow\infty$). Using the wave function we find the range of the scale factor c of the Cornell potential with reference to the mass scale of B and D sectors heavy-flavoured mesons. With our computed range of c, we also explore the critical inter-quark separation which should govern the choice of parent-child terms in perturbation method out of the linear and Coulombic terms of the Cornell potential.

Multi-wavelength polarimetry and radio observations of Swift J1727.8-1613 at the beginning of its recent 2023 outburst suggested the presence of a bright compact jet aligned in the north-south direction, which could not be confirmed without high angular resolution images. Using the Very Long Baseline Array and the Long Baseline Array, we imaged Swift J1727.8-1613, during the hard/hard-intermediate state, revealing a bright core and a large, two-sided, asymmetrical, resolved jet. The jet extends in the north-south direction, at a position angle of $-0.60\pm0.07\deg$ East of North. At 8.4 GHz, the entire resolved jet structure is $\sim110 (d/2.7\,\text{kpc})/\sin i$ AU long, with the southern approaching jet extending $\sim80 (d/2.7\,\text{kpc})/\sin i$ AU from the core, where $d$ is the distance to the source and $i$ is the inclination of the jet axis to the line of sight. These images reveal the most resolved continuous X-ray binary jet, and possibly the most physically extended continuous X-ray binary jet ever observed. Based on the brightness ratio of the approaching and receding jets, we put a lower limit on the intrinsic jet speed of $\beta\geq0.27$ and an upper limit on the jet inclination of $i\leq74\deg$. In our first observation we also detected a rapidly fading discrete jet knot $66.89\pm0.04$ mas south of the core, with a proper motion of $0.66\pm0.05$ mas hour$^{-1}$, which we interpret as the result of a downstream internal shock or a jet-ISM interaction, as opposed to a transient relativistic jet launched at the beginning of the outburst., Comment: Accepted in ApJL

Deep learning models have achieved remarkable success across diverse domains. However, the intricate nature of these models often impedes a clear understanding of their decision-making processes. This is where Explainable AI (XAI) becomes indispensable, offering intuitive explanations for model decisions. In this work, we propose a simple yet highly effective approach, ScoreCAM++, which introduces modifications to enhance the promising ScoreCAM method for visual explainability. Our proposed approach involves altering the normalization function within the activation layer utilized in ScoreCAM, resulting in significantly improved results compared to previous efforts. Additionally, we apply an activation function to the upsampled activation layers to enhance interpretability. This improvement is achieved by selectively gating lower-priority values within the activation layer. Through extensive experiments and qualitative comparisons, we demonstrate that ScoreCAM++ consistently achieves notably superior performance and fairness in interpreting the decision-making process compared to both ScoreCAM and previous methods.

Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of attention-based models is bottle-necked during inference by the KV-cache's aggressive memory consumption. In this work, we explore whether we can improve language model efficiency (e.g. by reducing memory consumption) without compromising on recall. By applying experiments and theory to a broad set of architectures, we identify a key tradeoff between a model's state size and recall ability. We show that efficient alternatives to attention (e.g. H3, Mamba, RWKV) maintain a fixed-size recurrent state, but struggle at recall. We propose BASED a simple architecture combining linear and sliding window attention. By varying BASED window size and linear attention feature dimension, we can dial the state size and traverse the pareto frontier of the recall-memory tradeoff curve, recovering the full quality of attention on one end and the small state size of attention-alternatives on the other. We train language models up to 1.3b parameters and show that BASED matches the strongest sub-quadratic models (e.g. Mamba) in perplexity and outperforms them on real-world recall-intensive tasks by 6.22 accuracy points. Implementations of linear attention are often less efficient than optimized standard attention implementations. To make BASED competitive, we develop IO-aware algorithms that enable 24x higher throughput on language generation than FlashAttention-2, when generating 1024 tokens using 1.3b parameter models. Code for this work is provided at: https://github.com/HazyResearch/based.

Totally asymmetric simple exclusion process (TASEP) sets the paradigm for one-dimensional driven single file motion. We study a periodic TASEP with two ``road blocks'' or defects of different kinds, one point and another extended, across which particle flows are inhibited. We show how the interplay between particle number conservation and competition between the defects lead to inhomogeneous steady states with localised domain walls (LDW). The LDW locations jump discontinuously, indicating a discontinuous transition between these LDW states, as the system passes from being controlled by one defect to the other. When the defects are ``competing'', instead of an LDW a pair of delocalised domain walls appear, none of which can penetrate the extended defect. A minimum current principle can be used to identify the dominant defect that controls the domain wall formations. Our results should be important in diverse systems, ranging from protein synthesis by ribosomes in biological cells to urban traffic networks., Comment: 11 pages, 10 figures, supplementary movies available on request

Abstractive text summarization is surging with the number of training samples to cater to the needs of the deep learning models. These models tend to exploit the training data representations to attain superior performance by improving the quantitative element of the resultant summary. However, increasing the size of the training set may not always be the ideal solution to maximize the performance, and therefore, a need to revisit the quality of training samples and the learning protocol of deep learning models is a must. In this paper, we aim to discretize the vector space of the abstractive text summarization models to understand the characteristics learned between the input embedding space and the models' encoder space. We show that deep models fail to capture the diversity of the input space. Further, the distribution of data points on the encoder space indicates that an unchecked increase in the training samples does not add value; rather, a tear-down of data samples is highly needed to make the models focus on variability and faithfulness. We employ clustering techniques to learn the diversity of a model's sample space and how data points are mapped from the embedding space to the encoder space and vice versa. Further, we devise a metric to filter out redundant data points to make the model more robust and less data hungry. We benchmark our proposed method using quantitative metrics, such as Rouge, and qualitative metrics, such as BERTScore, FEQA and Pyramid score. We also quantify the reasons that inhibit the models from learning the diversity from the varied input samples.

Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand these architectures, we pretrain a suite of 17 attention and "gated-convolution" language models, finding that SoTA gated-convolution architectures still underperform attention by up to 2.1 perplexity points on the Pile. In fine-grained analysis, we find 82% of the gap is explained by each model's ability to recall information that is previously mentioned in-context, e.g. "Hakuna Matata means no worries Hakuna Matata it means no" $\rightarrow$ "??". On this task, termed "associative recall", we find that attention outperforms gated-convolutions by a large margin: a 70M parameter attention model outperforms a 1.4 billion parameter gated-convolution model on associative recall. This is surprising because prior work shows gated convolutions can perfectly solve synthetic tests for AR capability. To close the gap between synthetics and real language, we develop a new formalization of the task called multi-query associative recall (MQAR) that better reflects actual language. We perform an empirical and theoretical study of MQAR that elucidates differences in the parameter-efficiency of attention and gated-convolution recall. Informed by our analysis, we evaluate simple convolution-attention hybrids and show that hybrids with input-dependent sparse attention patterns can close 97.4% of the gap to attention, while maintaining sub-quadratic scaling. Our code is accessible at: https://github.com/HazyResearch/zoology.