Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding Structure Matters: Comparing Methods to Adapt Multilingual Vocabularies to New Languages

Pre-trained multilingual language models underpin a large portion of modern NLP tools outside of English. A strong baseline for specializing these models for specific languages is Language-Adaptive Pre-Training (LAPT). However, retaining a large cross-lingual vocabulary and embedding matrix comes at considerable excess computational cost during adaptation. In this study, we propose several simple techniques to replace a cross-lingual vocabulary with a compact, language-specific one. Namely, we address strategies for re-initializing the token embedding matrix after vocabulary specialization. We then provide a systematic experimental comparison of our techniques, in addition to the recently-proposed Focus method. We demonstrate that: 1) Embedding-replacement techniques in the monolingual transfer literature are inadequate for adapting multilingual models. 2) Replacing cross-lingual vocabularies with smaller specialized ones provides an efficient method to improve performance in low-resource languages. 3) Simple embedding re-initialization techniques based on script-wise sub-distributions rival techniques such as Focus, which rely on similarity scores obtained from an auxiliary model.
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges

Unstructured data in Electronic Health Records (EHRs) often contains critical information -- complementary to imaging -- that could inform radiologists' diagnoses. But the large volume of notes often associated with patients together with time constraints renders manually identifying relevant evidence practically infeasible. In this work we propose and evaluate a zero-shot strategy for using LLMs as a mechanism to efficiently retrieve and summarize unstructured evidence in patient EHR relevant to a given query. Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence. Under expert evaluation, we find that this LLM-based approach provides outputs consistently preferred to a pre-LLM information retrieval baseline. Manual evaluation is expensive, so we also propose and validate a method using an LLM to evaluate (other) LLM outputs for this task, allowing us to scale up evaluation. Our findings indicate the promise of LLMs as interfaces to EHR, but also highlight the outstanding challenge posed by"hallucinations". In this setting, however, we show that model confidence in outputs strongly correlates with faithful summaries, offering a practical means to limit confabulations.
Magnetic field generation in laser-solid interactions at strong-field QED relevant intensities

Magnetic field generation in ultra-intense laser-solid interactions is studied over a range of laser intensities relevant to next-generation laser facilities ($a_0 = 50-500$) using 2D particle-in-cell simulations. It is found that fields on the order of 0.1 MT (1 GigaGauss) may be generated by relativistic electrons traveling along the surface of the target. However a significant fraction of the energy budget is converted to high-energy photons, ~38% at $a_0=500$, greatly reducing the available energy for field generation. A model for the evolution of the target-surface fields and their scaling with $a_0$ is developed using laser parameters and assumed values for the average radial electron velocity and reflectivity. The model and empirical scaling allow for the estimation of field strengths on the next generation of laser facilities, a necessary component to the proposal of any future magnetized experiment.
A Distributed Algebra System for Time Integration on Parallel Computers

We present a distributed algebra system for efficient and compact implementation of numerical time integration schemes on parallel computers and graphics processing units (GPU). The software implementation combines the time integration library Odeint from Boost with the OpenFPM framework for scalable scientific computing. Implementing multi-stage, multi-step, or adaptive time integration methods in distributed-memory parallel codes or on GPUs is challenging. The present algebra system addresses this by making the time integration methods from Odeint available in a concise template-expression language for numerical simulations distributed and parallelized using OpenFPM. This allows using state-of-the-art time integration schemes, or switching between schemes, by changing one line of code, while maintaining parallel scalability. This enables scalable time integration with compact code and facilitates rapid rewriting and deployment of simulation algorithms. We benchmark the present software for exponential and sigmoidal dynamics and present an application example to the 3D Gray-Scott reaction-diffusion problem on both CPUs and GPUs in only 60 lines of code.
Toward Reproducing Network Research Results Using Large Language Models

Reproducing research results is important for the networking community. The current best practice typically resorts to: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; or (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private ones are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). We first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report our observations and lessons and discuss future open research questions of this proposal.
DynamicScore: a Novel Metric for Quantifying Graph Dynamics

This study introduces a new metric called ''DynamicScore'' to evaluate the dynamics of graphs. It can be applied to both vertices and edges. Unlike traditional metrics, DynamicScore not only measures changes in the number of vertices or edges between consecutive time steps, but also takes into account the composition of these sets. To illustrate the possible contributions of this metric, we calculate it for increasing networks of preferential attachment (Barab{\'a}si-Albert model) and Edge-Markovian graphs. The results improve our understanding of the dynamics inherent in these generated evolving graphs.
Trade-Off Between Beamforming and Macro-Diversity Gains in Distributed mMIMO

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a ``sweet spot"on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area.
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes.
Anisotropic neutron star crust, solar system mountains, and gravitational waves

Mountains or non-axisymmetric deformations of rotating neutron stars (NS) efficiently radiate gravitational waves (GW). We consider analogies between NS mountains and surface features of solar system bodies. Both NS and moons such as Europa or Enceladus have thin crusts over deep oceans while Mercury has a thin crust over a large metallic core. Thin sheets may wrinkle in universal ways. Europa has linear features, Enceladus has ``Tiger"stripes, and Mercury has lobate scarps. NS may have analogous features. The innermost inner core of the Earth is anisotropic with a shear modulus that depends on direction. If NS crust material is also anisotropic this will produce an ellipticity, when the crust is stressed, that grows with spin frequency. This yields a breaking index (log derivative of spin down rate) very different from $n=5$ and could explain the maximum spin observed for neutron stars and a possible minimum ellipticity of millisecond pulsars.
Self-normalized Cram\'{e}r type moderate deviations for martingales and applications

Cram\'er's moderate deviations give a quantitative estimate for the relative error of the normal approximation and provide theoretical justifications for many estimator used in statistics. In this paper, we establish self-normalized Cram\'{e}r type moderate deviations for martingales under some mile conditions. The result extends an earlier work of Fan, Grama, Liu and Shao [Bernoulli, 2019]. Moreover, applications of our result to Student's statistic, stationary martingale difference sequences and branching processes in a random environment are also discussed. In particular, we establish Cram\'{e}r type moderate deviations for Student's $t$-statistic for branching processes in a random environment.
Boreas Rss-20 Polder C-130 Measurements of Surface Brdf

This Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-20 data set contains measurements of surface bidirectional reflectance distribution function (BRDF) made by the polarization and Directionality of Earth reflectances (POLDER) instrument over several surface types (pine, spruce, fen) of the BOREAS southern study area (SSA) during the 1994 intensive field campaigns (IFCs). Single-point BRDF values were acquired either from the NASA Ames Research Center (ARC) C-130 aircraft or from a NASA Wallops Flight Facility (WFF) helicopter. A related data set collected from the helicopter platform is available as is POLDER imagery acquired from the C-130. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems

Fusing Radar and Lidar sensor data can fully utilize their complementary advantages and provide more accurate reconstruction of the surrounding for autonomous driving systems. Surround Radar/Lidar can provide 360-degree view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems. However, due to the intrinsic physical constraints, the rotating speed of surround Radar, and thus the frequency to generate Radar data frames, is much lower than surround Lidar. Existing Radar/Lidar fusion methods have to work at the low frequency of surround Radar, which cannot meet the high responsiveness requirement of autonomous driving systems.This paper develops techniques to fuse surround Radar/Lidar with working frequency only limited by the faster surround Lidar instead of the slower surround Radar, based on the state-of-the-art object detection model MVDNet. The basic idea of our approach is simple: we let MVDNet work with temporally unaligned data from Radar/Lidar, so that fusion can take place at any time when a new Lidar data frame arrives, instead of waiting for the slow Radar data frame. However, directly applying MVDNet to temporally unaligned Radar/Lidar data greatly degrades its object detection accuracy. The key information revealed in this paper is that we can achieve high output frequency with little accuracy loss by enhancing the training procedure to explore the temporal redundancy in MVDNet so that it can tolerate the temporal unalignment of input data. We explore several different ways of training enhancement and compare them quantitatively with experiments.
How to assign volunteers to tasks compatibly ? A graph theoretic and parameterized approach

In this paper we study a resource allocation problem that encodes correlation between items in terms of \conflict and maximizes the minimum utility of the agents under a conflict free allocation. Admittedly, the problem is computationally hard even under stringent restrictions because it encodes a variant of the {\sc Maximum Weight Independent Set} problem which is one of the canonical hard problems in both classical and parameterized complexity. Recently, this subject was explored by Chiarelli et al.~[Algorithmica'22] from the classical complexity perspective to draw the boundary between {\sf NP}-hardness and tractability for a constant number of agents. The problem was shown to be hard even for small constant number of agents and various other restrictions on the underlying graph. Notwithstanding this computational barrier, we notice that there are several parameters that are worth studying: number of agents, number of items, combinatorial structure that defines the conflict among the items, all of which could well be small under specific circumstancs. Our search rules out several parameters (even when taken together) and takes us towards a characterization of families of input instances that are amenable to polynomial time algorithms when the parameters are constant. In addition to this we give a superior $2^{m}|I|^{\Co{O}(1)}$ algorithm for our problem where $m$ denotes the number of items that significantly beats the exhaustive $\Oh(m^{m})$ algorithm by cleverly using ideas from FFT based fast polynomial multiplication; and we identify simple graph classes relevant to our problem's motivation that admit efficient algorithms.
Minuteman: Machine and Human Joining Forces in Meeting Summarization

Many meetings require creating a meeting summary to keep everyone up to date. Creating minutes of sufficient quality is however very cognitively demanding. Although we currently possess capable models for both audio speech recognition (ASR) and summarization, their fully automatic use is still problematic. ASR models frequently commit errors when transcribing named entities while the summarization models tend to hallucinate and misinterpret the transcript. We propose a novel tool -- Minuteman -- to enable efficient semi-automatic meeting minuting. The tool provides a live transcript and a live meeting summary to the users, who can edit them in a collaborative manner, enabling correction of ASR errors and imperfect summary points in real time. The resulting application eases the cognitive load of the notetakers and allows them to easily catch up if they missed a part of the meeting due to absence or a lack of focus. We conduct several tests of the application in varied settings, exploring the worthiness of the concept and the possible user strategies.
Learning Personalized User Preference from Cold Start in Multi-turn Conversations

This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences. In particular, the TAI system is able to automatically identify and label user preference in live interactions, manage dialogue flows for interactive teaching sessions, and reuse learned preference for preference elicitation. We develop the TAI system by leveraging BERT encoder models to encode both dialogue and relevant context information, and build action prediction (AP), argument filling (AF) and named entity recognition (NER) models to understand the teaching session. We adopt a seeker-provider interaction loop mechanism to generate diverse dialogues from cold-start. TAI is capable of learning user preference, which achieves 0.9122 turn level accuracy on out-of-sample dataset, and has been successfully adopted in production.
Simba: A Scalable Bilevel Preconditioned Gradient Method for Fast Evasion of Flat Areas and Saddle Points

The convergence behaviour of first-order methods can be severely slowed down when applied to high-dimensional non-convex functions due to the presence of saddle points. If, additionally, the saddles are surrounded by large plateaus, it is highly likely that the first-order methods will converge to sub-optimal solutions. In machine learning applications, sub-optimal solutions mean poor generalization performance. They are also related to the issue of hyper-parameter tuning, since, in the pursuit of solutions that yield lower errors, a tremendous amount of time is required on selecting the hyper-parameters appropriately. A natural way to tackle the limitations of first-order methods is to employ the Hessian information. However, methods that incorporate the Hessian do not scale or, if they do, they are very slow for modern applications. Here, we propose Simba, a scalable preconditioned gradient method, to address the main limitations of the first-order methods. The method is very simple to implement. It maintains a single precondition matrix that it is constructed as the outer product of the moving average of the gradients. To significantly reduce the computational cost of forming and inverting the preconditioner, we draw links with the multilevel optimization methods. These links enables us to construct preconditioners in a randomized manner. Our numerical experiments verify the scalability of Simba as well as its efficacy near saddles and flat areas. Further, we demonstrate that Simba offers a satisfactory generalization performance on standard benchmark residual networks. We also analyze Simba and show its linear convergence rate for strongly convex functions.
Revisiting 3D Flat Holography: Causality Structure and Modular flow

Flat space holography is an open and hard problem existing several different approaches, which may finally turn out to be consistent with each other, in the literature to tackle it. Focusing on how bulk emergent spacetime is encoded in quantum information of null boundaries, we choose a specific toy model called the flat$_3$/BMSFT model, which conjectures the duality between boundary BMS$_3$ invariant field theory and bulk quantum gravity in 3D asymptotic flat spacetimes (AFS), to explore. Aiming to find an entanglement wedge like quantity for single interval and a connected entanglement wedge for multi-intervals in flat$_3$/BMSFT model, we explore the bulk causality structures related to the holographic swing surface proposal through both boundary and bulk local modular flow, make a corresponding decomposition of the global Minkowski spacetime and look at the entanglement phase transition. As a byproduct, we solve the problem about the existence of partial entanglement entropy (PEE) correspondence in this model which is a bit nontrivial due to the unusual behavior of boundary modular flow in BMS$_3$ field theory. Among the literature considering quantum information aspects of flat$_3$/BMSFT model, there are several substantial, unusual but overlooked phenomena which need to be emphasized and revisited to gain more deserved attention. Thus another motivation of this paper is to find where these unusual phenomena come from, and physically show in a manifest way what they may imply. After reading we hope readers can feel sincerely what we present about the above mentioned second aim is more valuable than the mathematical results in the present paper.
Major factors that structure the Benthic fauna of a shallow, tropical lake, Lake Kuriftu, Ethiopia

The aim of this study was to determine the community structure of the benthos in Lake Kuriftu in relation to some environmental variables. Benthic samples were collected with an Ekman grab (0.0225 m2) from three sites (littoral, sub-littoral and profundal zones) on a monthly basis from September 2009 to February 2010. Dissolved oxygen and temperature were measured with a probe at different depths of each site. Sediment texture was determined by the Bouyoucos hydrometer and organic matter content by loss on ignition method. Chironomids, EPT (Ephemeroptera, Plecoptera, Trichoptera), oligochaetes and coleopterans were abundant in littoral and sub-littoral zones, with EPT much higher in the vegetated littoral area. The mean density of benthic macro-invertebrates in littoral, sub-littoral and profundal zones was 20,443, 18,251, and 4,388 ind/m2, and that of Chironomids was 12,545, 14,003 and 3,215 ind/m2, respectively. The profundal zone had the lowest diversity with abundant oligochaetes but no EPT samples, despite the high oxygen content of the profundal zone (6.33 mg/l). Chironomids and oligochaetes were present in all zones which had negligible difference in dissolved oxygen and organic matter content. The littoral zone had highest diversity (H’=1.26) and richness (d=2.72). Our results indicate that abundance of benthic macro-invertebrates of Lake Kuriftu was best correlated with the presence of vegetation in the littoral zone and sediment texture (high clay) in the littoral and profundal zones. We conclude that benthic macro-invertebrate community structure in Lake Kuriftu is mainly influenced by spatial variation in vegetation, sediment texture and temperature, but not dissolved oxygen and organic matter content. Keywords: Benthic macro-invertebrates, Lake Kuriftu, Littoral, Profundal, Sub-littoral.