Miracle-dz/UniFAR-FTUs huggingface dataset:
The sharp ALMA view of infall and outflow in the massive protocluster G31.41+0.31

Context. To better understand the formation of high-mass stars, it is fundamental to investigate how matter accretes onto young mas- sive stars, how it is ejected, and how all this differs from the low-mass case. The massive protocluster G31.41+0.31 is the ideal target to study all these processes because observations at millimeter and centimeter wavelengths have resolved the emission of the Main core into at least four massive dust continuum sources, named A, B, C, and D, within 1 (cid:48)(cid:48) or 0.018pc, and have identiﬁed signatures of infall and several outﬂows associated with the core. Aims. We study the interplay between infall and outﬂow in G31.41+0.31, at a spatial resolution of a few 100au, by investigating their properties and their possible impact on the core. Methods. We carried out molecular line observations of typical high-density tracers, such as CH 3 CN or H 2 CO, and shock and outﬂow tracers, such as SiO, with ALMA at 1.4mm that achieved an angular resolution of ∼ 0 . (cid:48)(cid:48) 09 ( ∼ 340au). Results. The observations reveal inverse P Cygni proﬁles in CH 3 CN and H 2 CO toward the four sources embedded in the Main core, suggesting that all of them are undergoing collapse. The infall rates estimated from the redshifted absorption are on the order of ∼ 10 − 2 M (cid:12) yr − 1 . The individual infall rates imply that the accretion timescale of the Main core is an order of magnitude smaller than its rotation timescale. This conﬁrms that rotating toroids such as the G31 Main core are non-equilibrium, transient collaps- ing structures that need to be constantly replenished with fresh material from a large-scale reservoir. For sources B, C, and D the infall could be accelerating inside the sources, while for source A the presence of a second emission component complicates the interpretation. The SiO observations have revealed the presence of at least six outﬂows in the G31.41+0.31 star-forming region, and suggest that each of the four sources embedded in the Main core drives a molecular outﬂow. The outﬂow rates are on the order of ∼ 10 − 5 –10 − 4 M (cid:12) yr − 1 , depending on the SiO abundance. The feedback of the outﬂows appears sufﬁcient to sustain the turbulence in the core and to eventually disrupt the core or prevent its further collapse. The mass accretion rates onto the individual sources, esti- mated from the highest values of the outﬂow mass loss rates, are on the order of 10 − 4 M (cid:12) yr − 1 . The difference of about two orders of magnitude between the accretion rates and the infall rates suggests that the central stars and the jets and outﬂows might not be efﬁcient in removing disk material, which could lead to gravitational instabilities, fragmentation of the disk, and episodic accretion events. Conclusions. Infall and outﬂows are simultaneously present in all four sources embedded in the high-mass star-forming core G31.41+0.31. This indicates that these sources are still actively accreting and have not reached their ﬁnal mass yet, which conﬁrms the youth of this massive core.
Investigating Software Testability and Test cases Effectiveness

Software measurement is an essential management tool to develop robust and maintainable software systems. Software metrics can be used to control the inherent complexities in software design. To guarantee that the components of the software are inevitably testable, the testability attribute is used, which is a sub-characteristics of the software’s maintabilility as well as quality assurance. This study investigates the relationship between static code and test metrics and testability and test cases effectiveness. The study answers three formulated research questions. The results of the analysis showed that size and complexity metrics are suitable for predicting the testability of object-oriented classes.
Roadmap for Cybersecurity in Autonomous Vehicles

Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort. These vehicles will be connected to various external systems and utilize advanced embedded systems to perceive their environment and make intelligent decisions. However, this increased connectivity makes these vehicles vulnerable to various cyber-attacks that can have catastrophic effects. Attacks on automotive systems are already on the rise in today's vehicles and are expected to become more commonplace in future autonomous vehicles. Thus, there is a need to strengthen cybersecurity in future autonomous vehicles. In this article, we discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence. We propose a roadmap toward building secure autonomous vehicles and highlight key open challenges that need to be addressed.
Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Unmanned aerial vehicles (UAVs) with mounted base stations are a promising technology for monitoring smart farms. They can provide communication and computation services to extensive agricultural regions. With the assistance of a Multi-Access Edge Computing infrastructure, an aerial base station (ABS) network can provide an energy-efficient solution for smart farms that need to process deadline critical tasks fed by IoT devices deployed on the field. In this paper, we introduce a multi-objective maximization problem and a Q-Learning based method which aim to process these tasks before their deadline while considering the UAVs’ hover time. We also present three heuristic baselines to evaluate the performance of our approaches. In addition, we introduce an integer linear programming (ILP) model to define the upper bound of our objective function. The results show that Q-Learning outperforms the baselines in terms of remaining energy levels and percentage of delay violations.
Monadic Monadic Second Order Logic

One of the main reasons for the correspondence of regular languages and monadic second-order logic is that the class of regular languages is closed under images of surjective letter-to-letter homomorphisms. This closure property holds for structures such as finite words, finite trees, infinite words, infinite trees, elements of the free group, etc. Such structures can be modelled using monads. In this paper, we study which structures (understood via monads in the category of sets) are such that the class of regular languages (i.e. languages recognized by finite algebras) are closed under direct images of surjective letter-to-letter homomorphisms. We provide diverse sufficient conditions for a monad to satisfy this property. We also present numerous examples of monads, including positive examples that do not satisfy our sufficient conditions, and counterexamples where the closure property fails.
Explainability in Music Recommender Systems

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty. In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders' explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply -- or need to be adapted -- to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.
City3D: Large-scale Urban Reconstruction from Airborne Point Clouds

We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the ﬁnal model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Speciﬁcally, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne point clouds and the use of 3D city models in urban applications.
Winograd Convolution for Deep Neural Networks: Efficient Point Selection

Convolutional neural networks (CNNs) have dramatically improved the accuracy of image, video, and audio processing for tasks such as object recognition, image segmentation, and interactive speech systems. CNNs require large amounts of computing resources for both training and inference, primarily because the convolution layers are computationally intensive. Fast convolution algorithms such as Winograd convolution can greatly reduce the computational cost of these layers. However, Winograd convolution has poor numeric properties, such that greater savings in computation cause exponentially increasing floating point errors. A defining feature of each Winograd convolution algorithm is a set of real-value points where polynomials are sampled. The choice of points impacts the numeric accuracy of the algorithm, but the optimal set of points for small convolutions remains unknown. Existing work considers only small integers and simple fractions as candidate points. In this work, we propose a novel approach to point selection using points of the form \(\lbrace -\frac{1}{c},-c,c,\frac{1}{c}\rbrace\) using the full range of real-valued numbers for c. We show that groups of this form cause cancellations in the Winograd transform matrices that reduce numeric error. We find empirically that the error for different values of c forms a rough curve across the range of real-value numbers. It is therefore possible to localize the values of c that lead to lower error. We show that it is not necessary to choose integers or simple fractions as evaluation points, and that lower errors can be achieved with non-obvious real-valued points. We study a range of sizes for small convolutions and achieve reduction in error ranging from 2% to around 59% for both 1D and 2D convolution, when compared to state of the art. Furthermore, we identify patterns in cases when we select a subset of our proposed points that will always lead to a lower error. Finally, we implement a complete Winograd convolution layer and use it to run state-of-the-art deep convolution neural networks on real datasets and show that our proposed points achieve reduction in error, ranging from 22% to 63%, while also showing how an increased Winograd output size can result in execution speed-up for some cases.
Dynamical phase transitions in the collisionless pre-thermal states of isolated quantum systems: theory and experiments

We overview the concept of dynamical phase transitions (DPTs) in isolated quantum systems quenched out of equilibrium. We focus on non-equilibrium transitions characterized by an order parameter, which features qualitatively distinct temporal behavior on the two sides of a certain dynamical critical point. DPTs are currently mostly understood as long-lived prethermal phenomena in a regime where inelastic collisions are incapable to thermalize the system. The latter enables the dynamics to substain phases that explicitly break detailed balance and therefore cannot be encompassed by traditional thermodynamics. Our presentation covers both cold atoms as well as condensed matter systems. We revisit a broad plethora of platforms exhibiting pre-thermal DPTs, which become theoretically tractable in a certain limit, such as for a large number of particles, large number of order parameter components, or large spatial dimension. The systems we explore include, among others, quantum magnets with collective interactions, ϕ 4 quantum field theories, and Fermi–Hubbard models. A section dedicated to experimental explorations of DPTs in condensed matter and AMO systems connects this large variety of theoretical models.
TheHuzz: Instruction Fuzzing of Processors Using Golden-Reference Models for Finding Software-Exploitable Vulnerabilities

The increasing complexity of modern processors poses many challenges to existing hardware verification tools and methodologies for detecting security-critical bugs. Recent attacks on processors have shown the fatal consequences of uncovering and exploiting hardware vulnerabilities. Fuzzing has emerged as a promising technique for detecting software vulnerabilities. Recently, a few hardware fuzzing techniques have been proposed. However, they suffer from several limitations, including non-applicability to commonly used Hardware Description Languages (HDLs) like Verilog and VHDL, the need for significant human intervention, and inability to capture many intrinsic hardware behaviors, such as signal transitions and floating wires. In this paper, we present the design and implementation of a novel hardware fuzzer, TheHuzz, that overcomes the aforementioned limitations and significantly improves the state of the art. We analyze the intrinsic behaviors of hardware designs in HDLs and then measure the coverage metrics that model such behaviors. TheHuzz generates assembly-level instructions to increase the desired coverage values, thereby finding many hardware bugs that are exploitable from software. We evaluate TheHuzz on four popular open-source processors and achieve 1.98x and 3.33x the speed compared to the industry-standard random regression approach and the state-of-the-art hardware fuzzer, DiffuzRTL, respectively. Using TheHuzz, we detected 11 bugs in these processors, including 8 new vulnerabilities, and we demonstrate exploits using the detected bugs. We also show that TheHuzz overcomes the limitations of formal verification tools from the semiconductor industry by comparing its findings to those discovered by the Cadence JasperGold tool.
Zero-Shot Long-Form Voice Cloning with Dynamic Convolution Attention

With recent advancements in voice cloning, the performance of speech synthesis for a target speaker has been rendered similar to the human level. However, autoregressive voice cloning systems still suffer from text alignment failures, resulting in an inability to synthesize long sentences. In this work, we propose a variant of attention-based text-to-speech system that can reproduce a target voice from a few seconds of reference speech and generalize to very long utterances as well. The proposed system is based on three independently trained components: a speaker encoder, synthesizer and universal vocoder. Generalization to long utterances is realized using an energy-based attention mechanism known as Dynamic Convolution Attention, in combination with a set of modifications proposed for the synthesizer based on Tacotron 2. Moreover, effective zero-shot speaker adaptation is achieved by conditioning both the synthesizer and vocoder on a speaker encoder that has been pretrained on a large corpus of diverse data. We compare several implementations of voice cloning systems in terms of speech naturalness, speaker similarity, alignment consistency and ability to synthesize long utterances, and conclude that the proposed model can produce intelligible synthetic speech for extremely long utterances, while preserving a high extent of naturalness and similarity for short texts.
Strange metallicity of moiré twisted bilayer graphene

Recent measurements in several different laboratories report the observation of an approximately linear-in-temperature resistivity with a large twist-angle-dependent slope (or temperature coefficient) in moir\'e twisted bilayer graphene (tBLG) down to a few K and sometimes to much lower temperatures. In this note, we theoretically discuss this `strange metal' linear-in-temperature transport behavior from the perspective of resistive scattering by acoustic phonons, emphasizing the aspects of the transport data, which are and which are not consistent with the phonon scattering mechanism. Extensive theoretical comparison with a new experiment [A. Jaoui et al., Nature Physics 18, 633 (2022)] is the central new aspect of this work.
FRAMED: Data-Driven Structural Performance Analysis of Community-Designed Bicycle Frames

This paper presents a data-driven analysis of the structural performance of 4500 community-designed bicycle frames. We present FRAMED — a parametric dataset of bicycle frames based on bicycles designed by bicycle practitioners from across the world. To support our data-driven approach, we also provide a dataset of structural performance values such as weight, displacements under load, and safety factors for all the bicycle frame designs. By exploring a diverse design space of frame design parameters and a set of ten competing design objectives, we present an automated way to analyze the structural performance of bicycle frames. Our structural simulations are validated against physical experimentation on bicycle frames. Through our analysis, we highlight overall trends in bicycle frame designs created by community members, study several bicycle frames under different loading conditions, identify non-dominated design candidates that perform well on multiple objectives, and explore correlations between structural objectives. Our analysis shows that over 75% of bicycle frames created by community members are infeasible, motivating the need for AI agents to support humans in designing bicycles. This work aims to simultaneously serve researchers focusing on bicycle design as well as researchers focusing on the development of data-driven design algorithms, such as surrogate models and Deep Generative Methods. The dataset and code are provided at http://decode.mit. edu/projects/framed/.
Towards Multi-Objective Statistically Fair Federated Learning

Federated Learning (FL) has emerged as a result of data ownership and privacy concerns to prevent data from being shared between multiple parties included in a training procedure. Although issues, such as privacy, have gained significant attention in this domain, not much attention has been given to satisfying statistical fairness measures in the FL setting. With this goal in mind, we conduct studies to show that FL is able to satisfy different fairness metrics under different data regimes consisting of different types of clients. More specifically, uncooperative or adversarial clients might contaminate the global FL model by injecting biased or poisoned models due to existing biases in their training datasets. Those biases might be a result of imbalanced training set (Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b; Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is able to satisfy multiple objectives including various statistical fairness metrics. Through experimentation, we then show the effectiveness of this method comparing it with various baselines, its ability in satisfying different objectives collectively and individually, and its ability in identifying uncooperative or adversarial clients and down-weighing their effect
Sequential Parametric Optimization for Rate-Splitting Precoding in Non-Orthogonal Unicast and Multicast Transmissions

This paper investigates rate-splitting (RS) precoding for non-orthogonal unicast and multicast (NOUM) transmissions using fully-digital and hybrid precoders. We study the nonconvex weighted sum-rate (WSR) maximization problem subject to a multicast requirement. We propose FALCON, an approach based on sequential parametric optimization, to solve the aforementioned problem. We show that FALCON converges to a local optimum without requiring judicious selection of an initial feasible point. Besides, we show through simulations that by leveraging RS, hybrid precoders can attain nearly the same performance as their fully-digital counterparts under certain specific settings.
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions

Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such as Space2Vec) were proposed to directly encode any point in 2D space as a high-dimensional vector, and has been successfully applied to various (geo)spatial prediction tasks. However, a map projection distortion problem rises when applying location encoding models to large-scale real-world GPS coordinate datasets (e.g., species images taken all over the world) - all current location encoding models are designed for encoding points in a 2D (Euclidean) space but not on a spherical surface, e.g., earth surface. To solve this problem, we propose a multi-scale location encoding model called Sphere2V ec which directly encodes point coordinates on a spherical surface while avoiding the mapprojection distortion problem. We provide theoretical proof that the Sphere2Vec encoding preserves the spherical surface distance between any two points. We also developed a unified view of distance-reserving encoding on spheres based on the Double Fourier Sphere (DFS). We apply Sphere2V ec to the geo-aware image classification task. Our analysis shows that Sphere2V ec outperforms other 2D space location encoder models especially on the polar regions and data-sparse areas for image classification tasks because of its nature for spherical surface distance preservation.
Electron Cooling with Space-Charge Dominated Proton Beams at Iota

We describe a new electron cooler being developed for 2.5 MeV protons at the Integrable Optics Test Accelerator (IOTA), which is a highly re-configurable storage ring at Fermilab. This system would enable the study of magnetized electron cooling in the presence of intense space-charge with transverse tune shifts approaching -0.5 as well as highly nonlinear focusing optics in the IOTA ring. We present an overview of the design, simulations and hardware to be used for this project.