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Nowadays superconducting radio frequency (SRF) cavities represent fundamental tools used for (Standard Model) particle acceleration, (beyond Standard Model) particle probing and long-lifetime photon preservation. We study the SRF frequency shift in the vicinity of the critical temperature $T_c$ and the quality factor mainly at low temperatures within the Dynes superconductor model. We scrutinize and use the local limit response to the external electromagnetic field. Our approach allows for a finer analysis of the peculiar behavior of the resonant frequency shift immensely close to $T_c$, observed in recent experiments. In the ideal dirty limit, we analytically elaborate on the width and depth of the resulting dip. Studying the sign of the slope of the resonant frequency shift at $T_c$ in the moderately clean regime reveals the role of the pair-breaking and pair-conserving disorder. Next, to find the relevance of our description, we compare and also fit our results with the recent experimental data from the N-doped Nb sample presented in [arXiv:2307.07905v1]. Our analysis remarkably complies with the experimental findings, especially concerning the dip width. We offer straightforward, homogeneous-disorder-based interpretation within the moderately clean regime. Assuming the same regime at low temperatures, we address details of the high-quality plateaus. Summing all up, this work presents (and studies the limits of) the simple effective description of the complex problem corresponding to the electromagnetic response in the superconductors combining homogeneous conventional pairing and two different kinds of disorder scattering., Comment: 16 pages, 13 figures, 2 appendices

Mass segregation is seen in many star clusters, but whether massive stars form in the center of a cluster or migrate there dynamically is still debated. N-body simulations have shown that early dynamical mass segregation is possible when sub-clusters merge to form a dense core with a small crossing time. However, the effect of gas dynamics on both the formation and dynamics of the stars could inhibit the formation of the dense core. We aim to study the dynamical mass segregation of star cluster models that include gas dynamics and self-consistently form stars from the dense substructure in the gas. Our models use the Torch framework, which is based on AMUSE and includes stellar and magnetized gas dynamics, as well as stellar evolution and feedback from radiation, stellar winds, and supernovae. Our models consist of three star clusters forming from initial turbulent spherical clouds of mass $10^{4,5,6}\rm~M_\odot$ and radius $11.7\rm~pc$ that have final stellar masses of $3.6\times10^3\rm~M_\odot$, $6.5\times10^4\rm~M_\odot$, and $8.9\times10^5\rm~M_\odot$, respectively. There is no primordial mass segregation in the model by construction. All three clusters become dynamically mass segregated at early times via collapse confirming that this mechanism occurs within sub-clusters forming directly out of the dense substructure in the gas. The dynamics of the embedded gas and stellar feedback do not inhibit the collapse of the cluster. We find that each model cluster becomes mass segregated within $2~$Myr of the onset of star formation, reaching the levels observed in young clusters in the Milky Way. However, we note that the exact values are highly time-variable during these early phases of evolution. Massive stars that segregate to the center during core collapse are likely to be dynamically ejected, a process that can decrease the overall level of mass segregation again., Comment: Submitted to A&A. 8 pages, 4 figures

To avoid detection, adversaries often use command-line obfuscation. There are numerous techniques of the command-line obfuscation, all designed to alter the command-line syntax without affecting its original functionality. This variability forces most security solutions to create an exhaustive enumeration of signatures for even a single pattern. In contrast to using signatures, we have implemented a scalable NLP-based detection method that leverages a custom-trained, small transformer language model that can be applied to any source of execution logs. The evaluation on top of real-world telemetry demonstrates that our approach yields high-precision detections even on high-volume telemetry from a diverse set of environments spanning from universities and businesses to healthcare or finance. The practical value is demonstrated in a case study of real-world samples detected by our model. We show the model's superiority to signatures on established malware known to employ obfuscation and showcase previously unseen obfuscated samples detected by our model.

Time sharing between activities remains an indispensable part of everyday activity pattern. However, the issue has not yet been fully acknowledged within the existing time allocation models, potentially resulting in inaccuracies in valuing travel time savings. Therefore this study is aimed at addressing this gap by investigating the potential impact of introducing time sharing within such a framework, as well as factors determining it as represented by travel activities. In doing so, time constraint in the time allocation model of Small was modified to enable sharing the same time interval between different activities. The resulting expression indicated that such an augmentation could lead to lower estimates of value of time as a resource. On the other hand, empirical research based on the data from the National Passenger Survey 2004 used for calibrating cross-nested logit model indicated a number of factors affecting the choice of travel activities. It was discovered that significant include possession of equipment allowing particular activities, e.g. newspaper, paperwork or ICT devices, companionship, gender, length of the journey, frequency of using the service, possibility of working on the train, journey planning in advance, first class travel, termination of the trip in central London, peak-time travel and availability of seating., Comment: 15 pages, 2 Figures, Presented at European Transport Conference 2010

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models perform competitively to open access models on a wide range of evaluation benchmarks, and were sized to fit on a single DGX H100 with 8 GPUs when deployed in FP8 precision. We believe that the community can benefit from these models in various research studies and commercial applications, especially for generating synthetic data to train smaller language models. Notably, over 98% of data used in our model alignment process is synthetically generated, showcasing the effectiveness of these models in generating synthetic data. To further support open research and facilitate model development, we are also open-sourcing the synthetic data generation pipeline used in our model alignment process.

Oxygen ion conductors are crucial for enhancing the efficiency of various clean energy technologies, including fuel cells, batteries, electrolyzers, membranes, sensors, and more. In this study, LaBi2O4Cl is identified as an ultra-fast oxygen conductor from the MBi2O4X (M=rare-earth element, X=halogen element) family, discovered by a structure-similarity analysis of >60k oxygen-containing compounds. Ab initio studies reveal that LaBi2O4Cl has an ultra-low migration barrier of 0.1 eV for oxygen vacancy, significantly lower than 0.6-0.8 eV for interstitial oxygen. Frenkel pairs are the dominant defects in intrinsic LaBi2O4Cl, facilitating notable oxygen diffusion primarily through vacancies at higher temperatures. LaBi2O4Cl with extrinsic oxygen vacancies (2.8%) exhibits a conductivity of 0.3 S/cm at 25{\deg}C, maintains a 0.1 eV diffusion barrier up to 1100{\deg}C, and transitions from extrinsic to mixed extrinsic and intrinsic behavior as the Frenkel pair concentration increases at higher temperatures. Experimental results on synthesized LaBi2O4Cl and Sr-doped LaBi2O4Cl demonstrate comparable or higher oxygen conductivity than YSZ and LSGM below 400 {\deg}C, with lower activation energies. Further experimental optimization of LaBi2O4Cl, including aliovalent doping and microstructure refinement, could significantly enhance its performance and efficiency, facilitating fast oxygen conduction approaching room temperature.

Human evaluation is a critical component in machine translation system development and has received much attention in text translation research. However, little prior work exists on the topic of human evaluation for speech translation, which adds additional challenges such as noisy data and segmentation mismatches. We take first steps to fill this gap by conducting a comprehensive human evaluation of the results of several shared tasks from the last International Workshop on Spoken Language Translation (IWSLT 2023). We propose an effective evaluation strategy based on automatic resegmentation and direct assessment with segment context. Our analysis revealed that: 1) the proposed evaluation strategy is robust and scores well-correlated with other types of human judgements; 2) automatic metrics are usually, but not always, well-correlated with direct assessment scores; and 3) COMET as a slightly stronger automatic metric than chrF, despite the segmentation noise introduced by the resegmentation step systems. We release the collected human-annotated data in order to encourage further investigation., Comment: LREC-COLING2024 publication (with corrections for Table 3)

Two main mechanisms have classically been proposed for the formation of runaway stars. The binary supernova scenario (BSS) suggests that a massive star in a binary explodes as a supernova, ejecting its companion. The dynamical ejection scenario suggests that a star is ejected through a strong dynamical encounter between multiple stars. We propose a third mechanism for the formation of runaway stars: the sub-cluster ejection scenario (SCES), where an infalling sub-cluster of stars is ejected out of the cluster by a slingshot interaction with the contracting gravitational potential of the assembling cluster. We demonstrate the SCES in a star-by-star simulation of a young massive cluster forming from a $10^6\rm~M_\odot$ gas cloud using the Torch framework. This star cluster forms hierarchically through a sequence of sub-cluster mergers, determined by the initial turbulent, spherical initial conditions of the gas. We find that these mergers drive the formation of runaway stars in our model. Late-forming sub-clusters fall into the central potential, where they are ejected on a slingshot trajectory, forming groups of runaway stars that are distributed highly anisotropically. Runaways formed by the same SCES share similar ages, velocities, and ejection directions. Surveying observations, we identify several SCES candidate groups with anisotropic ejection directions. The SCES is capable of producing runaway binaries: two wide dynamical binaries in infalling sub-clusters were tightened through ejection. This allows for another velocity kick via subsequent BSS, which is a promising avenue for producing hypervelocity stars unbound to the Galaxy. The SCES occurs when sub-cluster formation is resolved. We expect non-spherical initial gas distributions to increase runaway star numbers. The observation of groups of runaway stars formed via SCES thus reveals the assembly history of their natal clusters., Comment: 11 pages, 10 figures. Submitted to A&A. Comments welcome!

Nanotechnology's impact on semiconductor industry advancement, particularly through the engineering of nanostructures like nanowires, opens new possibilities for material functionality due to the tunable physical properties of nanostructures compared to bulk materials. This paper presents a comprehensive study on group IV semiconductors and their binaries across four polytypes: 2H, 3C, 4H, and 6H, focusing on their optoelectronic application potential. Deep understanding of these polytypes is particularly relevant for nanowire-based technologies. Through first principles modeling, we examine the structural and electronic properties of these materials, emphasizing their band structure, stability, and the feasibility for light-emitting applications. We use a generalized Ising model to discuss materials stability and tendency for polytypism. We also determine relative band edge positions and employ a six $k\cdot p$ model for a detailed understanding of the materials' electronic properties. Due to the comprehensive nature of this study, we provide insight on the chemical trends present in all of the studied properties. Our theoretical predictions align well with existing experimental data, suggesting new avenues for nanostructure-based device development. The discussion extends to the implications of these findings for the fabrication of optoelectronic devices with the studied IV-IV materials, highlighting the challenges and opportunities for future research in nanowire synthesis and their application.

We present a pseudopolynomial-time algorithm for the Knapsack problem that has running time $\widetilde{O}(n + t\sqrt{p_{\max}})$, where $n$ is the number of items, $t$ is the knapsack capacity, and $p_{\max}$ is the maximum item profit. This improves over the $\widetilde{O}(n + t \, p_{\max})$-time algorithm based on the convolution and prediction technique by Bateni et al.~(STOC 2018). Moreover, we give some evidence, based on a strengthening of the Min-Plus Convolution Hypothesis, that our running time might be optimal. Our algorithm uses two new technical tools, which might be of independent interest. First, we generalize the $\widetilde{O}(n^{1.5})$-time algorithm for bounded monotone min-plus convolution by Chi et al.~(STOC 2022) to the \emph{rectangular} case where the range of entries can be different from the sequence length. Second, we give a reduction from general knapsack instances to \emph{balanced} instances, where all items have nearly the same profit-to-weight ratio, up to a constant factor. Using these techniques, we can also obtain algorithms that run in time $\widetilde{O}(n + OPT\sqrt{w_{\max}})$, $\widetilde{O}(n + (nw_{\max}p_{\max})^{1/3}t^{2/3})$, and $\widetilde{O}(n + (nw_{\max}p_{\max})^{1/3} OPT^{2/3})$, where $OPT$ is the optimal total profit and $w_{\max}$ is the maximum item weight.

The Pickands estimator for the extreme value index is beneficial due to its universal consistency, location, and scale invariance, which sets it apart from other types of estimators. However, similar to many extreme value index estimators, it is marked by poor asymptotic efficiency. Chen (2021) introduces a Conditional Value-at-Risk (CVaR)-based Pickands estimator, establishes its consistency, and demonstrates through simulations that this estimator significantly reduces mean squared error while preserving its location and scale invariance. The initial focus of this paper is on demonstrating the weak convergence of the empirical CVaR in functional space. Subsequently, based on the established weak convergence, the paper presents the asymptotic normality of the CVaR-based Pickands estimator. It further supports these theoretical findings with empirical evidence obtained through simulation studies.

We introduce a novel method that combines differential geometry, kernels smoothing, and spectral analysis to quantify facial muscle activity from widely accessible video recordings, such as those captured on personal smartphones. Our approach emphasizes practicality and accessibility. It has significant potential for applications in national security and plastic surgery. Additionally, it offers remote diagnosis and monitoring for medical conditions such as stroke, Bell's palsy, and acoustic neuroma. Moreover, it is adept at detecting and classifying emotions, from the overt to the subtle. The proposed face muscle analysis technique is an explainable alternative to deep learning methods and a non-invasive substitute to facial electromyography (fEMG).

The problem of designing connectivity oracles supporting vertex failures is one of the basic data structures problems for undirected graphs. It is already well understood: previous works [Duan--Pettie STOC'10; Long--Saranurak FOCS'22] achieve query time linear in the number of failed vertices, and it is conditionally optimal as long as we require preprocessing time polynomial in the size of the graph and update time polynomial in the number of failed vertices. We revisit this problem in the paradigm of algorithms with predictions: we ask if the query time can be improved if the set of failed vertices can be predicted beforehand up to a small number of errors. More specifically, we design a data structure that, given a graph $G=(V,E)$ and a set of vertices predicted to fail $\widehat{D} \subseteq V$ of size $d=|\widehat{D}|$, preprocesses it in time $\tilde{O}(d|E|)$ and then can receive an update given as the symmetric difference between the predicted and the actual set of failed vertices $\widehat{D} \triangle D = (\widehat{D} \setminus D) \cup (D \setminus \widehat{D})$ of size $\eta = |\widehat{D} \triangle D|$, process it in time $\tilde{O}(\eta^4)$, and after that answer connectivity queries in $G \setminus D$ in time $O(\eta)$. Viewed from another perspective, our data structure provides an improvement over the state of the art for the \emph{fully dynamic subgraph connectivity problem} in the \emph{sensitivity setting} [Henzinger--Neumann ESA'16]. We argue that the preprocessing time and query time of our data structure are conditionally optimal under standard fine-grained complexity assumptions.

The mode of star formation that results in the formation of globular clusters and young massive clusters is difficult to constrain through observations. We present models of massive star cluster formation using the Torch framework, which uses AMUSE to couple distinct multi-physics codes that handle star formation, stellar evolution and dynamics, radiative transfer, and magnetohydrodynamics. We upgrade Torch by implementing the N-body code PeTar, thereby enabling Torch to handle massive clusters forming from $10^6\rm\, M_\odot$ clouds with $\ge10^5$ individual stars. We present results from Torch simulations of star clusters forming from $10^4, 10^5$, and $10^6\rm M_\odot$ turbulent, spherical gas clouds (named M4, M5, M6) of radius $R=11.7$ pc. We find that star formation is highly efficient and becomes more so at higher cloud mass and surface density. For M4, M5, and M6 with initial surface densities $2.325\times 10^{1,2,3}\rm\, M_\odot\, pc^{-2}$, after a free-fall time of $t_{ff}=6.7,2.1,0.67$ Myr, we find that $\sim30\%$, 40%, and 60% of the cloud mass has formed into stars, respectively. The final integrated star formation efficiency is $32\%,\, 65\%$, and 85\% for M4, M5, and M6. Observations of nearby clusters similar to M4 have similar integrated star formation efficiencies of $\leq30\%$. The M5 and M6 models represent a different regime of cluster formation that is more appropriate for the conditions in starburst galaxies and gas-rich galaxies at high redshift, and that leads to a significantly higher efficiency of star formation. We argue that young massive clusters build up through short efficient bursts of star formation in regions that are sufficiently dense ($\ge 10^2 \rm\,M_\odot\,pc^{-2}$) and massive ($\ge10^5\rm\, M_\odot$). In such environments, the dynamical time of the cloud becomes short enough that stellar feedback cannot act quickly enough to slow star formation., Comment: Submitted to A&A

Existing open-source helpfulness preference datasets do not specify what makes some responses more helpful and others less so. Models trained on these datasets can incidentally learn to model dataset artifacts (e.g. preferring longer but unhelpful responses only due to their length). To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful. Specifically, our 37k-sample dataset has annotations for correctness, coherence, complexity, and verbosity in addition to overall helpfulness of responses. Training Llama 2 70B using the HelpSteer dataset with SteerLM technique produces a model that scores 7.54 on MT Bench, which is currently the highest score for open models that do not require training data from more powerful models (e.g. GPT4). We release this dataset with CC-BY-4.0 license at https://huggingface.co/datasets/nvidia/HelpSteer

We study two classic variants of block-structured integer programming. Two-stage stochastic programs are integer programs of the form $\{A_i \mathbf{x} + D_i \mathbf{y}_i = \mathbf{b}_i\textrm{ for all }i=1,\ldots,n\}$, where $A_i$ and $D_i$ are bounded-size matrices. On the other hand, $n$-fold programs are integer programs of the form $\{{\sum_{i=1}^n C_i\mathbf{y}_i=\mathbf{a}} \textrm{ and } D_i\mathbf{y}_i=\mathbf{b}_i\textrm{ for all }i=1,\ldots,n\}$, where again $C_i$ and $D_i$ are bounded-size matrices. It is known that solving these kind of programs is fixed-parameter tractable when parameterized by the maximum dimension among the relevant matrices $A_i,C_i,D_i$ and the maximum absolute value of any entry appearing in the constraint matrix. We show that the parameterized tractability results for two-stage stochastic and $n$-fold programs persist even when one allows large entries in the global part of the program. More precisely, we prove that: - The feasibility problem for two-stage stochastic programs is fixed-parameter tractable when parameterized by the dimensions of matrices $A_i,D_i$ and by the maximum absolute value of the entries of matrices $D_i$. That is, we allow matrices $A_i$ to have arbitrarily large entries. - The linear optimization problem for $n$-fold integer programs that are uniform -- all matrices $C_i$ are equal -- is fixed-parameter tractable when parameterized by the dimensions of matrices $C_i$ and $D_i$ and by the maximum absolute value of the entries of matrices $D_i$. That is, we require that $C_i=C$ for all $i=1,\ldots,n$, but we allow $C$ to have arbitrarily large entries. In the second result, the uniformity assumption is necessary; otherwise the problem is $\mathsf{NP}$-hard already when the parameters take constant values. Both our algorithms are weakly polynomial: the running time is measured in the total bitsize of the input., Comment: 40 pages. Extended abstract to appear in the proceedings of SODA 2024

As one of the three main pillars of fine-grained complexity theory, the 3SUM problem explains the hardness of many diverse polynomial-time problems via fine-grained reductions. Many of these reductions are either directly based on or heavily inspired by P\u{a}tra\c{s}cu's framework involving additive hashing and are thus randomized. Some selected reductions were derandomized in previous work [Chan, He; SOSA'20], but the current techniques are limited and a major fraction of the reductions remains randomized. In this work we gather a toolkit aimed to derandomize reductions based on additive hashing. Using this toolkit, we manage to derandomize almost all known 3SUM-hardness reductions. As technical highlights we derandomize the hardness reductions to (offline) Set Disjointness, (offline) Set Intersection and Triangle Listing -- these questions were explicitly left open in previous work [Kopelowitz, Pettie, Porat; SODA'16]. The few exceptions to our work fall into a special category of recent reductions based on structure-versus-randomness dichotomies. We expect that our toolkit can be readily applied to derandomize future reductions as well. As a conceptual innovation, our work thereby promotes the theory of deterministic 3SUM-hardness. As our second contribution, we prove that there is a deterministic universe reduction for 3SUM. Specifically, using additive hashing it is a standard trick to assume that the numbers in 3SUM have size at most $n^3$. We prove that this assumption is similarly valid for deterministic algorithms.

Simultaneous speech translation (SST) aims to provide real-time translation of spoken language, even before the speaker finishes their sentence. Traditionally, SST has been addressed primarily by cascaded systems that decompose the task into subtasks, including speech recognition, segmentation, and machine translation. However, the advent of deep learning has sparked significant interest in end-to-end (E2E) systems. Nevertheless, a major limitation of most approaches to E2E SST reported in the current literature is that they assume that the source speech is pre-segmented into sentences, which is a significant obstacle for practical, real-world applications. This thesis proposal addresses end-to-end simultaneous speech translation, particularly in the long-form setting, i.e., without pre-segmentation. We present a survey of the latest advancements in E2E SST, assess the primary obstacles in SST and its relevance to long-form scenarios, and suggest approaches to tackle these challenges., Comment: IJCNLP-AACL SRW 2023 - camera-ready version

As evidenced by recent survey results, majority of asteroids are slow rotators (P>12 h), but lack spin and shape models due to selection bias. This bias is skewing our overall understanding of the spins, shapes, and sizes of asteroids, as well as of their other properties. Also, diameter determinations for large (>60km) and medium-sized asteroids (between 30 and 60 km) often vary by over 30% for multiple reasons. Our long-term project is focused on a few tens of slow rotators with periods of up to 60 hours. We aim to obtain their full light curves and reconstruct their spins and shapes. We also precisely scale the models, typically with an accuracy of a few percent. We used wide sets of dense light curves for spin and shape reconstructions via light-curve inversion. Precisely scaling them with thermal data was not possible here because of poor infrared data: large bodies are too bright for WISE mission. Therefore, we recently launched a campaign among stellar occultation observers, to scale these models and to verify the shape solutions, often allowing us to break the mirror pole ambiguity. The presented scheme resulted in shape models for 16 slow rotators, most of them for the first time. Fitting them to stellar occultations resolved previous inconsistencies in size determinations. For around half of the targets, this fitting also allowed us to identify a clearly preferred pole solution, thus removing the ambiguity inherent to light-curve inversion. We also address the influence of the uncertainty of the shape models on the derived diameters. Overall, our project has already provided reliable models for around 50 slow rotators. Such well-determined and scaled asteroid shapes will, e.g. constitute a solid basis for density determinations when coupled with mass information. Spin and shape models continue to fill the gaps caused by various biases., Comment: Accepted to Astronomy & Astrophysics. 12 pages + appendices

A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject's gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. The gait deviation index, gait profile score, and the overall abnormality measure are standard benchmarks for quantifying gait abnormality. However, these indices do not account for the intrinsic smoothness of the gait movement at each joint/plane and the potential co-variation between the joints/planes. Utilizing a multivariate functional principal components analysis we propose the functional gait deviation index (FGDI). FGDI accounts for the intrinsic smoothness of the gait movement at each joint/plane and the potential co-variation between the joints. We show that FGDI scales with overall gait function, provides a consistent measure of gait abnormality, and is implemented easily using an interactive web app., Comment: arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission

Music recommendation for videos attracts growing interest in multi-modal research. However, existing systems focus primarily on content compatibility, often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat, a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user's preference as inputs and retrieves an appropriate music matching the context. The reasoning module, equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat, we build a large-scale dataset, conversational music recommendation for videos, that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability.

Current simultaneous speech translation models can process audio only up to a few seconds long. Contemporary datasets provide an oracle segmentation into sentences based on human-annotated transcripts and translations. However, the segmentation into sentences is not available in the real world. Current speech segmentation approaches either offer poor segmentation quality or have to trade latency for quality. In this paper, we propose a novel segmentation approach for a low-latency end-to-end speech translation. We leverage the existing speech translation encoder-decoder architecture with ST CTC and show that it can perform the segmentation task without supervision or additional parameters. To the best of our knowledge, our method is the first that allows an actual end-to-end simultaneous speech translation, as the same model is used for translation and segmentation at the same time. On a diverse set of language pairs and in- and out-of-domain data, we show that the proposed approach achieves state-of-the-art quality at no additional computational cost., 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

Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search with hypothesis reliability scoring to determine when to wait for more input speech before translating further. However, this method maintains multiple hypotheses until the entire speech input is consumed -- this scheme cannot directly show a single \textit{incremental} translation to users. Further, this method lacks mechanisms for \textit{controlling} the quality vs. latency tradeoff. We propose a modified incremental blockwise beam search incorporating local agreement or hold-$n$ policies for quality-latency control. We apply our framework to models trained for online or offline translation and demonstrate that both types can be effectively used in online mode. Experimental results on MuST-C show 0.6-3.6 BLEU improvement without changing latency or 0.8-1.4 s latency improvement without changing quality., Comment: Accepted at INTERSPEECH 2023

Open many body quantum systems play a paramount role in various branches of physics, such as quantum information, nonlinear optics or condensed matter. The dissipative character of open systems has gained a lot of interest especially within the fields of quantum optics, due to unprecedented stabilization of quantum coherence, and quantum information, with its desire to control environmental degrees of freedom. We look beyond the typical mechanism of dissipation associated with an external source and show that strongly interacting many particle systems can create quantum decoherence within themselves. We study a quantum bosonic two-dimensional many body system with extended interactions between particles. Analytical calculations show that the system can be driven out of its coherent state, which is prevalent among commonly used setups. However, we also observe a revival of the superfluid phase within the same framework for sufficiently large interaction strength. The breakdown of quantum coherence is inevitable, but can be misinterpreted if one assumes improper coupling between the constituents of the many particle system. We show an adequate path to retrieve physically relevant results and consider its limitations. The system displays a natural cutoff that enforces the breakdown of superfluidity.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available at https://www.kaggle.com/datasets/ipateam/nuinsseg and https://github.com/masih4/NuInsSeg, respectively., Comment: 7 pages, 1 Figure

We study dynamic algorithms in the model of algorithms with predictions. We assume the algorithm is given imperfect predictions regarding future updates, and we ask how such predictions can be used to improve the running time. This can be seen as a model interpolating between classic online and offline dynamic algorithms. Our results give smooth tradeoffs between these two extreme settings. First, we give algorithms for incremental and decremental transitive closure and approximate APSP that take as an additional input a predicted sequence of updates (edge insertions, or edge deletions, respectively). They preprocess it in $\tilde{O}(n^{(3+\omega)/2})$ time, and then handle updates in $\tilde{O}(1)$ worst-case time and queries in $\tilde{O}(\eta^2)$ worst-case time. Here $\eta$ is an error measure that can be bounded by the maximum difference between the predicted and actual insertion (deletion) time of an edge, i.e., by the $\ell_\infty$-error of the predictions. The second group of results concerns fully dynamic problems with vertex updates, where the algorithm has access to a predicted sequence of the next $n$ updates. We show how to solve fully dynamic triangle detection, maximum matching, single-source reachability, and more, in $O(n^{\omega-1}+n\eta_i)$ worst-case update time. Here $\eta_i$ denotes how much earlier the $i$-th update occurs than predicted. Our last result is a reduction that transforms a worst-case incremental algorithm without predictions into a fully dynamic algorithm which is given a predicted deletion time for each element at the time of its insertion. As a consequence we can, e.g., maintain fully dynamic exact APSP with such predictions in $\tilde{O}(n^2)$ worst-case vertex insertion time and $\tilde{O}(n^2 (1+\eta_i))$ worst-case vertex deletion time (for the prediction error $\eta_i$ defined as above)., Comment: To appear in proceedings of SODA 2024. Abstract shortened to meet arXiv requirements

In this work, we present a ready-to-use symmetry invariant expansion form of the eight-band k.p Hamiltonian for inorganic and organic metal halide perovskites (CsPbX$_3$ and MAPbX$_3$ with $X = \{$Cl, Br, I$\}$). We use the k.p model to calculate the electronic band structures for perovskite materials of cubic and pseudo-cubic phase. In order to find respective parameters, the band structures of considered materials were obtained within state-of-the-art density functional theory and used next as targets to adjust the k.p bands and determine the values of k.p parameters. The calculated band structures were used to obtain the material gain for bulk crystals (CsPbCl$_3$, CsPbBr$_3$, CsPbI$_3$, MAPbCl$_3$, MAPbBr$_3$ and MAPbI$_3$) which is compared with the material gain in well-established III-V semiconductors. It was found that for these perovskites a positive material gain appears at lower carrier density than for the reference materials (GaAs and InP). We demonstrate that from the point of view of the electronic band structure, the studied perovskites are very promising gain medium for lasers.

The incorporation of "real-world data" to supplement the analysis of trials and improve decision-making has spurred the development of statistical techniques to account for introduced confounding. Recently, "hybrid" methods have been developed through which measured confounding is first attenuated via propensity scores and unmeasured confounding is addressed through (Bayesian) dynamic borrowing. Most efforts to date have focused on augmenting control arms with historical controls. Here we consider augmenting treatment arms through "expanded access", which is a pathway of non-trial access to investigational medicine for patients with seriously debilitating or life-threatening illnesses. Motivated by a case study on expanded access, we developed a novel method (the ProPP) that provides a conceptually simple and easy-to-use combination of propensity score weighting and the modified power prior. Our weighting scheme is based on the estimation of the average treatment effect of the patients in the trial, with the constraint that external patients cannot receive higher weights than trial patients. The causal implications of the weighting scheme and propensity-score integrated approaches in general are discussed. In a simulation study our method compares favorably with existing (hybrid) borrowing methods in terms of precision and type-I error rate. We illustrate our method by jointly analysing individual patient data from the trial and expanded access program for vemurafenib to treat metastatic melanoma. Our method provides a double safeguard against prior-data conflict and forms a straightforward addition to evidence synthesis methods of trial and real-world (expanded access) data., Comment: 26 Pages, 7 Figures, 1 Table