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This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAEs are trained on experimental data collected from two variants of a fully electric SUV, which differ in maximum torque delivery and drivetrain configuration. New meaningful jerk signals are generated within an engineering context through the interpretation of the VAE's latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs. We show that VAEs bypass the need for exhaustive manual system parametrization while maintaining physical plausibility by conditioning data generation on specific inputs., Comment: 27 pages

The near-Earth asteroid (98943) Torifune, previously designated 2001 CC$_{21}$, is the flyby target of the Hayabusa2 extended mission, nicknamed Hayabusa2$\#$ (SHARP: Small Hazardous Asteroid Reconnaissance Probe). The ground-based telescope observations offer a key science input for the mission's scientific investigation. During 2022 - 2024 this asteroid was at visible apparent magnitudes brighter than 18.5, allowing for a detailed characterization using ground-based telescope observations. We determined its rotation period $P~=~5.021516\pm0.000106$ h and its absolute magnitude H = 18.78 $\pm$ 0.14 and. The large number of lightcurves allows to estimate its axes ratio, its convex shape and its pole orientation $\lambda = 301^{\circ} \pm 35^{\circ}$, $\beta = {89^{+1}_{-6}}^{\circ}$ and $\epsilon = 5^{\circ} \pm 3^{\circ}$ which indicate a prograde rotation. We report the semi-axis of the equivalent ellipsoid, $a$ = 0.42$^{+0.08}_{0.06}$ km, $b$ = 0.16$^{+0.05}_{0.04}$ km, and $c$ = $0.17\pm0.03$ km. Consequently, the volume equivalent diameter is $D_{eq}$ = $0.44 \pm 0.06$ km . Using observations conducted simultaneously with four broadband filters, we determined $(g-r) = 0.663 \pm 0.022$ mag, $(r-i) = 0.177 \pm 0.012$ mag, and $(i-z_s) = -0.061 \pm 0.032$ mag. Additionally, we found that Torifune exhibits no detectable large-scale heterogeneity. We classified the object using a high signal-to-noise ratio spectrum (over the visible and near-infrared region) as Sq-type in the Bus-DeMeo taxonomy. We estimate a mineralogy similar to LL/L ordinary chondrites, with an ol/(ol+px) = 0.60, a Fa content of 28.5 mol$\%$, and a Fs content of 23.4 mol$\%$. The spectral data indicate a surface affected by moderate space weathering effects., Comment: Accepted for publication in PSJ, 29 page, 15 figures

MALTA2 is a depleted monolithic active pixel sensor (DMAPS) designed for tracking at high rates and typically low detection threshold of $\sim150\,\mathrm{e^-}$. A precise knowledge of the threshold is crucial to understanding the charge collection in the pixel and specifying the environment for sensor application. A simple procedure is developed to calibrate the threshold to unit electrons making use of a dedicated charge injection circuit and an Fe-55 source with dominant charge deposition of $1600\, \mathrm{e^-}$. The injection voltage is determined which corresponds to the injection under Fe-55 exposure and is the basis for charge calibration. The charge injection circuit incorporates a capacitance with design value of $\mathrm{C_{inj}}=$ 230 aF. Experimentally, the average capacitance value for non-irradiated samples is found to be $\mathrm{C_{inj,exp}}=$ 257 aF. The 12 % divergence motivates the need for the presented precise calibration procedure, which is proposed to be performed for each MALTA2 sensor.

Methods from Quantum Information Theory are used to scrutinize quantum correlations encoded in the two-quark density matrix over light-cone momentum fractions $x_1$ and $x_2$. A non-perturbative three quark model light-cone wavefunction predicts significant non-classical correlations associated with the "entanglement negativity" measure for asymmetric and small quark momentum fractions. We perform one step of QCD scale evolution of the entire density matrix, not just its diagonal (dPDF), by computing collinearly divergent corrections due to the emission of a gluon. Finally, we present first qualitative numerical results for single-step scale evolution of quantum entanglement correlations in double quark PDFs. At a higher $Q^2$ scale, the non-classical correlations manifest in the dPDF for nearly symmetric momentum fractions., Comment: 16 pages

Topologically trivial insulators are classified into two primary categories: unobstructed and obstructed atomic insulators. While both types can be described by exponentially localized Wannier orbitals, a defining feature of obstructed atomic insulators is that the centers of charge of these orbitals are positioned at empty sites within the unit cell, rather than on atoms. Despite extensive theoretical predictions, the unambiguous and quantitative experimental identification of an obstructed atomic phase has remained elusive. In this work, we present the first direct experimental evidence of such a phase in 1H-NbSe$_2$. We develop a novel method to extract the inter-orbital correlation functions from the local spectral function probed by scanning tunneling microscopy (STM), leveraging the orbital wave functions obtained from ab initio calculations. Applying this technique to STM images, we determine the inter-orbital correlation functions for the atomic band of 1H-NbSe$_2$ that crosses the Fermi level. Our results show that this band realizes an optimally compact obstructed atomic phase, providing the first unambiguous experimental identification of such a phase. Our approach of deconvolving the STM signal using ab initio orbital wave functions is broadly applicable to other material platforms, offering a powerful tool for exploring other electronic phases., Comment: 9+39 pages, 4+10 figures, 0+2 tables

Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks with commercial models leading the way. While open models usually operate at a smaller scale, they maintain competitiveness through specialization and fine-tuning. However, a significant challenge persists: open models often underperform in low-resource languages due to limited representation in the training corpus. In this paper, we present LLMic, a bilingual foundation language model designed specifically for the Romanian Language. We document the complete process of pretraining a foundation model for a low-resource language, including corpus construction, architecture selection, and hyper-parameter optimization. Our evaluation demonstrates that LLMic can be specialized for tasks in the target language, achieving results comparable to other much larger open models. We show that fine-tuning LLMic for language translation after the initial pretraining phase outperforms existing solutions in English-to-Romanian translation tasks. This opens the path for efficient large-scale processing for the Romanian language community, using the much smaller LLMic model

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications., Comment: CVPR 2024 MedSAM on Laptop Competition Summary: https://www.codabench.org/competitions/1847/

Using supervised automatic summarisation methods requires sufficient corpora that include pairs of documents and their summaries. Similarly to many tasks in natural language processing, most of the datasets available for summarization are in English, posing challenges for developing summarization models in other languages. Thus, in this work, we introduce RoLargeSum, a novel large-scale summarization dataset for the Romanian language crawled from various publicly available news websites from Romania and the Republic of Moldova that were thoroughly cleaned to ensure a high-quality standard. RoLargeSum contains more than 615K news articles, together with their summaries, as well as their headlines, keywords, dialect, and other metadata that we found on the targeted websites. We further evaluated the performance of several BART variants and open-source large language models on RoLargeSum for benchmarking purposes. We manually evaluated the results of the best-performing system to gain insight into the potential pitfalls of this data set and future development., Comment: Accepted at COLING 2024 (long papers)

Pre-trained Language Models (PLMs) have shown remarkable performances in recent years, setting a new paradigm for NLP research and industry. The legal domain has received some attention from the NLP community partly due to its textual nature. Some tasks from this domain are represented by question-answering (QA) tasks. This work explores the legal domain Multiple-Choice QA (MCQA) for a low-resource language. The contribution of this work is multi-fold. We first introduce JuRO, the first openly available Romanian legal MCQA dataset, comprising three different examinations and a number of 10,836 total questions. Along with this dataset, we introduce CROL, an organized corpus of laws that has a total of 93 distinct documents with their modifications from 763 time spans, that we leveraged in this work for Information Retrieval (IR) techniques. Moreover, we are the first to propose Law-RoG, a Knowledge Graph (KG) for the Romanian language, and this KG is derived from the aforementioned corpus. Lastly, we propose a novel approach for MCQA, Graph Retrieval Augmented by Facts (GRAF), which achieves competitive results with generally accepted SOTA methods and even exceeds them in most settings.

We introduce a new class of moir\'e systems and materials based on monolayers with triangular lattices and low-energy states at the M points of the Brillouin zone. These M-point moir\'e materials are fundamentally distinct from those derived from $\Gamma$- or K-point monolayers, featuring three time-reversal-preserving valleys related by three-fold rotational symmetry. We propose twisted bilayers of experimentally exfoliable 1T-SnSe$_2$ and 1T-ZrS$_2$ as realizations of this new class. Using extensive ab initio simulations, we develop quantitative continuum models and analytically show that the corresponding M-point moir\'e Hamiltonians exhibit emergent momentum-space non-symmorphic symmetries and a kagome plane-wave lattice in momentum space. This represents the first experimentally viable realization of a projective representation of crystalline space groups in a non-magnetic system. With interactions, these materials represent six-flavor Hubbard simulators with Mott physics, as can be seen by their flat Wilson loops. Furthermore, the presence of a non-symmorphic momentum-space in-plane mirror symmetry makes some of the M-point moir\'e Hamiltonians quasi-one-dimensional in each valley, suggesting the possibility of realizing Luttinger liquid physics. We predict the twist angles at which a series of (conduction) flat bands appear, provide a faithful continuum Hamiltonian, analyze its topology and charge density and briefly discuss several aspects of the physics of this new platform., Comment: 7+124 pages, 5+132 figures, 1+27 tables. Previously submitted. See also arXiv:2411.08950 and arXiv:2411.09741

The discovery of a charge density wave in FeGe extends the discussion of the nature of charge order in kagome metals to a magnetic compound. Motivated by this observation, we combine density functional theory (DFT) and functional-renormalization-group calculations to study interaction-induced Fermi-surface instabilities of the magnetic state of FeGe. We argue that the leading intra-band contribution to electronic correlations are approximately 2D and come from Van Hove points at the projected $M$~points. By varying parameters around DFT values, we determine a phase diagram for the quasi-2D scenario as function of on-site and nearest-neighbor interactions. We discuss universal aspects in the electronic mechanisms for the resulting phases, as well as the role of SU(2) symmetry breaking. We find FeGe to be in a regime of strong competition between $p$-wave charge density wave, $f$-wave pairing, and $d$-wave spin Pomeranchuk instabilities. This interplay can be influenced in favor of superconducting pairing for slightly increased nearest-neighbor interaction, suggesting a potential to induce superconductivity in FeGe.

We produce families of two-dimensional gap solitons (GSs) maintained by moir\'{e} lattices (MLs) composed of linear and nonlinear sublattices, with the defocusing sign of the nonlinearity. Depending on the angle between the sublattices, the ML may be quasiperiodic or periodic, composed of mutually incommensurate or commensurate sublattices, respectively (in the latter case, the inter-lattice angle corresponds to Pythagorean triples). The GSs include fundamental, quadrupole, and octupole solitons, as well as quadrupoles and octupoles carrying unitary vorticity. Stability segments of the GS families are identified by means of the linearized equation for small perturbations, and confirmed by direct simulations of perturbed evolution., Comment: 4 figures, to be published in Optics Letters (2024)

The study of twisted two-dimensional (2D) materials, where twisting layers create moir\'e superlattices, has opened new opportunities for investigating topological phases and strongly correlated physics. While systems such as twisted bilayer graphene (TBG) and twisted transition metal dichalcogenides (TMDs) have been extensively studied, the broader potential of a seemingly infinite set of other twistable 2D materials remains largely unexplored. In this paper, we define "theoretically twistable materials" as single- or multi-layer structures that allow for the construction of simple continuum models of their moir\'e structures. This excludes, for example, materials with a "spaghetti" of bands or those with numerous crossing points at the Fermi level, for which theoretical moir\'e modeling is unfeasible. We present a high-throughput algorithm that systematically searches for theoretically twistable semimetals and insulators based on the Topological 2D Materials Database. By analyzing key electronic properties, we identify thousands of new candidate materials that could host rich topological and strongly correlated phenomena when twisted. We propose representative twistable materials for realizing different types of moir\'e systems, including materials with different Bravais lattices, valleys, and strength of spin-orbital coupling. We provide examples of crystal growth for several of these materials and showcase twisted bilayer band structures along with simplified twisted continuum models. Our results significantly broaden the scope of moir\'e heterostructures and provide a valuable resource for future experimental and theoretical studies on novel moir\'e systems., Comment: 15+81 pages, 5+187 figures, 4+104 tables. The Topological 2D Materials Database is available at https://topologicalquantumchemistry.com/topo2d/index.html . See also the accompanying paper "Two-dimensional Topological Quantum Chemistry and Catalog of Topological Materials"

In this paper, we analyze the scalar field (quasi-)normal modes of recently derived rotating black holes within the framework of Einstein-Gauss-Bonnet theory at the Chern-Simons point in five dimensions. We also examine the mode spectrum of these probes on new static gravitational solitons. These solitons, featuring a regular center, are constructed from static black holes with gravitational hair via a double analytic continuation. By imposing ingoing boundary conditions at the horizons of rotating black holes, ensuring regularity at the soliton centers, and imposing Dirichlet boundary conditions at infinity, we obtain numerical spectra for the rotating black holes and solitons. For static black holes, we demonstrate analytically that the imaginary part of the mode frequencies is negative. Our analysis of the massless Klein-Gordon equation on five-dimensional geometries reveals an infinite family of gapped, massive three-dimensional Klein-Gordon fields, despite the presence of a non-compact extended direction. For the static solitons, the frequencies are real and non-equispaced, whereas in the rotating black holes, counter-rotating modes are absorbed more quickly, and the imaginary part of the co-rotating modes approaches zero as extremality is approached. Additionally, we show that both the rotating black holes and solitons can be equipped with non-trivial torsion, leading to a novel branch of solutions., Comment: 23 pages, 4 figures

This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established by SliceGPT. By transitioning from constant to dynamic slicing, our method leverages the newly proposed Layer Redundancy (LR) score, which assesses how much change each layer changes its input by measuring the cosine similarity of the input to the output of the layer. We use this score to prune parts of individual layers based on redundancy in such a way that the average pruned percentage for all layers is a fixed value. We conducted extensive experiments using models like Llama3-8B and Mistral-7B on multiple datasets, evaluating different slicing bases and percentages to determine optimal configurations that balance efficiency and performance. Our findings show that our dynamic slicing approach not only maintains but, in many cases, enhances model performance compared to the baseline established by constant slicing methods. For instance, in several settings, we see performance improvements of up to 5% over the SliceGPT baseline. Additionally, a perplexity decrease by as much as 7% was observed across multiple benchmarks, validating the effectiveness of our method. The code, model weights, and datasets are open-sourced at https://github.com/RazvanDu/DynamicSlicing., Comment: Accepted at EMNLP Findings 2024

Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5-turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller., Comment: 37 pages, 16 figures, Accepted by EMNLP 2024

Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is even more prevalent in speech emotion recognition. Our work explores the model's performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. This result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning., Comment: 16 pages, 1 figure, Accepted by WISE 2024

The remarkable achievements obtained by open-source large language models (LLMs) in recent years have predominantly been concentrated on tasks involving the English language. In this paper, we aim to advance the performance of Llama2 models on Romanian tasks. We tackle the problem of reduced computing resources by using QLoRA for training. We release RoQLlama-7b, a quantized LLM, which shows equal or improved results compared to its full-sized counterpart when tested on seven Romanian downstream tasks in the zero-shot setup. Also, it consistently achieves higher average scores across all few-shot prompts. Additionally, we introduce a novel Romanian dataset, namely RoMedQA, which contains single-choice medical questions in Romanian., Comment: Accepted at EMNLP Findings 2024 (short papers)

This paper highlights the significance of natural language processing (NLP) within artificial intelligence, underscoring its pivotal role in comprehending and modeling human language. Recent advancements in NLP, particularly in conversational bots, have garnered substantial attention and adoption among developers. This paper explores advanced methodologies for attaining smaller and more efficient NLP models. Specifically, we employ three key approaches: (1) training a Transformer-based neural network to detect offensive language, (2) employing data augmentation and knowledge distillation techniques to increase performance, and (3) incorporating multi-task learning with knowledge distillation and teacher annealing using diverse datasets to enhance efficiency. The culmination of these methods has yielded demonstrably improved outcomes., Comment: Accepted by NLDB2024

We investigate spontaneous symmetry- and antisymmetry-breaking bifurcations of solitons in a nonlinear dual-core waveguide with the pure-quartic dispersion and Kerr nonlinearity. Symmetric, antisymmetric, and asymmetric pure-quartic solitons (PQSs) are found, and their stability domains are identified. The bifurcations for both the symmetric and antisymmetric PQSs are of the supercritical type (alias phase transitions of the second kind). Direct simulations of the perturbed evolution of PQSs corroborate their stability boundaries predicted by the analysis of small perturbations., Comment: 4 pages, 5 figures, to be published in Optics Letters

The intrinsic frustrated nature of a kagome lattice is amenable to the realization of exotic phases of matter, such as quantum spin liquids or spin ices, and more recently the multiple-$\mathrm{\textbf{q}}$ charge density waves (CDW) in the kagome metals. Despite intense efforts to understand the mechanism driving the electronic modulations, its origin is still unknown and hindered by competing interactions and intertwined orders. Here, we identify a dimerization-driven 2D hexagonal charge-diffuse precursor in the antiferromagnetic kagome metal FeGe and demonstrate that the fraction of dimerized/undimerized states is the relevant order parameter of the multiple-$\mathrm{\textbf{q}}$ CDW of a continuous phase transition. The pretransitional charge fluctuations with propagation vector $\mathrm{\textbf{q}=\textbf{q}_M}$ at T$_{\mathrm{CDW}}$$<$T$<$T$^*$(125 K) are anisotropic, hence holding a quasi-long-range bond-orientational order. The broken translational symmetry emerges from the anisotropic diffuse precursor, akin to the Ising scenario of antiferromagnetic triangular lattices. The temperature and momentum dependence of the critical scattering show parallels to the stacked hexatic $\mathrm{B}$-phases reported in liquid crystals and transient states of CDWs and highlight the key role of the topological defect-mediated melting of the CDW in FeGe.

The study explores the conformable electromagnetic field theory. The concept of the conformable delta function is introduced. Subsequently, the conformable Maxwell's equations are derived.

Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others., Comment: Accepted at KES 2024

Glioblastoma multiforme (GBM), the most aggressive primary brain tumour, exhibits low survival rates due to its rapid growth, infiltrates surrounding brain tissue, and is highly resistant to treatment. One major challenge is oedema infiltration, a fluid build$-$up that provides a path for cancer cells to invade other areas. MRI resolution is insufficient to detect these infiltrating cells, leading to relapses despite chemotherapy and radiotherapy. In this work, we propose a new multiscale mathematical modelling method, to explore the oedema infiltration and predict tumour relapses. To address tumour relapses, we investigated several possible scenarios for the distribution of remaining GBM cells within the oedema after surgery. Furthermore, in this computational modelling investigation on tumour relapse scenarios were investigated assuming the presence of clinically relevant chemo$-$radio therapy numerical results suggest that a higher concentration of GBM cells near the surgical cavity edge led to limited spread and slower progression of tumour relapse. Finally, we explore mathematical and computational avenues for reconstructing relevant shapes for the initial distributions of GBM cells within the oedema from available MRI scans. The results obtained show good overlap between our simulation and the patient serial MRI scans taken 881 days into the treatment. While still under analytical investigation, this work paves the way for robust reconstruction of tumour relapses from available clinical data.

Research in the field of language models is rapidly evolving, with many open models being released to the public. Openly available pretraining corpora usually focus on only a handful of languages, with many others either missing completely or extremely underrepresented. In this report, we introduce FuLG, a hundred-fifty-billion-token Romanian corpus extracted from CommonCrawl. We present our methodology for filtering FuLG and compare it via ablation studies against existing Romanian corpora.

The recently revised small-$x$ helicity evolution, resumming the double-logarithmic factor, $\alpha_s\ln^2(1/x)$, allows for the study of helicity distributions of quarks and gluons at small Bjorken $x$, corresponding to high center-of-mass energy. In this work, we calculate the moderate-$x$ initial conditions in the regime, $\alpha_s \ln^2(1/x)\sim 1$, for the small-$x$ helicity evolution using a light-front valence quark model of the proton, which provides additional physical information about the target. The perturbative emission and absorption of a gluon by the valence quarks are also included. The results, given in Eqs. (35), provide a new set of initial conditions with a significantly reduced number of free parameters than conventional models. Consequently, the predictive power of small-$x$ helicity evolution is expected to improve once the initial conditions from this work are incorporated., Comment: 31 pages, 7 figures

Exclusive production of a $\chi_{c1}$ axial vector quarkonia in photon-proton scattering at high energies requires a $C$-odd $t$-channel exchange. In the limit of vanishing momentum transfer this occurs either via the exchange of a photon, the Primakoff process, where the spin of the proton does not change. For axial-vector meson production, as a consequence of the Landau-Yang theorem, the Primakoff cross section is finite as $t \to 0$. Alternatively, a $C$-odd spin dependent Odderon can be exchanged, which involves a spin flip of the proton. The resulting cross section is related to the square of the gluon Sivers function or its $\boldsymbol{k}_\perp^2$-moment. Using two models for the gluon Sivers function from the literature we compute the ratio of Sivers to Primakoff cross sections and the angular coefficient $\lambda_\theta$ governing the angular distribution of the $\chi_{c1} \to J/\psi + \gamma$ decay as functions of $x$. We point out that these observables constrain the magnitude of the gluon Sivers function at small $x$ which could be accessed in electron-proton scattering and ultraperipheral proton-proton and nucleus-proton collisions., Comment: 6 pages (with references), 3 figures

ULTRASAT (ULtra-violet TRansient Astronomy SATellite) is a wide-angle space telescope that will perform a deep time-resolved all-sky survey in the near-ultraviolet (NUV) spectrum. The science objectives are the detection of counterparts to short-lived transient astronomical events such as gravitational wave sources and supernovae. The mission is led by the Weizmann Institute of Science and is planned for launch in 2026 in collaboration with the Israeli Space Agency and NASA. DESY will provide the UV camera, composed by the detector assembly located in the telescope focal plane and the remote electronics unit. The camera is composed out of four back-metallized CMOS Image Sensors (CIS) manufactured in the 4T, dual gain Tower process. As part of the radiation qualification of the camera, Single Event Effect (SEE) testing has been performed by irradiating the sensor with heavy ions at the RADEF, Jyvaskyla facility. Preliminary results of both Single Event Upset (SEU) and Single Event Latch-up (SEL) occurrence rate in the sensor are presented. Additionally, an in-orbit SEE rate simulation has been performed in order to gain preliminary knowledge about the expected effect of SEE on the mission.

Hyperscalers dominate the landscape of large network deployments, yet they rarely share data or insights about the challenges they face. In light of this supremacy, what problems can we find to solve in this space? We take an unconventional approach to find relevant research directions, starting from public plans to build a $100 billion datacenter for machine learning applications. Leveraging the language models scaling laws, we discover what workloads such a datacenter might carry and explore the challenges one may encounter in doing so, with a focus on networking research. We conclude that building the datacenter and training such models is technically possible, but this requires novel wide-area transports for inter-DC communication, a multipath transport and novel datacenter topologies for intra-datacenter communication, high speed scale-up networks and transports, outlining a rich research agenda for the networking community.

We present a simple meta quantization approach that quantizes different layers of a large language model (LLM) at different bit levels, and is independent of the underlying quantization technique. Specifically, we quantize the most important layers to higher bit precision and less important layers to lower bits. We propose two effective strategies to measure the importance of layers within LLMs: the first measures the importance of a layer based on how different its output embeddings are from the input embeddings (higher is better); the second estimates the importance of a layer using the number of layer weights that are much larger than average (smaller is better). We show that quantizing different layers at varying bits according to our importance scores results in minimal performance drop with a far more compressed model size. Finally, we present several practical key takeaways from our variable layer-wise quantization experiments: (a) LLM performance under variable quantization remains close to the original model until 25-50% of layers are moved in lower quantization using our proposed ordering but only until 5-10% if moved using no specific ordering; (b) Adding layer importance to inherently dynamic quantization techniques can further improve their performance, showing that our approach is complementary to other dynamic quantization methods; (c) Quantizing LLMs to lower bits performs substantially better than pruning unless extreme quantization (2-bit) is used; and (d) Layer-wise quantization to lower bits works better in the case of larger LLMs with more layers compared to smaller LLMs with fewer layers. Our code is publicly available at https://github.com/RazvanDu/LayerwiseQuant/.

Languages such as P4 and NPL have enabled a wide and diverse range of networking applications that take advantage of programmable dataplanes. However, software development in these languages is difficult. To address this issue, high-level languages have been designed to offer programmers powerful abstractions that reduce the time, effort and domain-knowledge required for developing networking applications. These languages are then translated by a compiler into P4/NPL code. Inspired by the recent success of Large Language Models (LLMs) in the task of code generation, we propose to raise the level of abstraction even higher, employing LLMs to translate prose into high-level networking code. We analyze the problem, focusing on the motivation and opportunities, as well as the challenges involved and sketch out a roadmap for the development of a system that can generate high-level dataplane code from natural language instructions. We present some promising preliminary results on generating Lucid code from natural language.

In this study we investigate novel hybrid structure of silicon nanowires (SiNWs) coated with PANI:metaloxide(MO$_x$) nanoparticles i.e., WO$_3$ and TiO$_2$. The SiNWs were fabricated using MACE, whereas PANI:MO$_x$ were deposited using chemical oxidative polymerization method on SiNWs. To this date little attempts has been done to utilize such hybrid structures for respiratory sensing. The structures were characterized using RAMAN spectroscopy, X-ray diffraction, Electron disperssive spectroscopy, and Scanning electron microscopy. The electrical characterization to obtain respiratory sensing reveals excellent response compared to those obtained for SiNWs:MO$_x$ and SiNWs:PANI. Such enhancement in sensitivity is attributed to formation p-n heterojunction along side with wider conduction channel provided of PANI, increased porosity in SiNWs/PANI:WO$_3$ hybrid structures, providing active sites, increased oxygen vacancies and large surface area compared to that of pure MO$_x$ nanoparticles. Further, an improved drift in base line and sensor stability was established for the structure with PANI:WO$_3$ as compared to the PANI:TiO$_2$., Comment: 12 figures, 16 pages, 44 references

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.