Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes

Re-offense risk is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision-makers in their assessments, institutions increasingly rely on algorithmic risk assessment instruments (RAIs). These tools assess the likelihood that an individual will be arrested for a new criminal offense within some time window following their release. However, since not all crimes result in arrest, RAIs do not directly assess the risk of re-offense. Furthermore, disparities in the likelihood of arrest can potentially lead to biases in the resulting risk scores. Several recent validations of RAIs have therefore focused on arrests for violent offenses, which are viewed as being more accurate and less biased reflections of offending behavior. In this paper, we investigate biases in violent arrest data by analysing racial disparities in the likelihood of arrest for White and Black violent offenders. We focus our study on 2007--2016 incident-level data of violent offenses from 16 US states as recorded in the National Incident Based Reporting System (NIBRS). Our analysis shows that the magnitude and direction of the racial disparities depend on various characteristics of the crimes. In addition, our investigation reveals large variations in arrest rates across geographical locations and offense types. We discuss the implications of the observed disconnect between re-arrest and re-offense in the context of RAIs and the challenges around the use of data from NIBRS to correct for the sampling bias.
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learning architecture, that often lack a clear justification or interpretation. In this paper, we introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with a simple regularization term on the variance of the embeddings along each dimension individually. VICReg combines the variance term with a decorrelation mechanism based on redundancy reduction and covariance regularization, and achieves results on par with the state of the art on several downstream tasks. In addition, we show that incorporating our new variance term into other methods helps stabilize the training and leads to performance improvements.
Adaptive Policy Transfer in Reinforcement Learning

Efficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics. Policy transfer through warm initialization, imitation, or interacting over a large set of agents with randomized instances, have been commonly applied to solve a variety of Reinforcement Learning tasks. However, this seems far from how skill transfer happens in the biological world: Humans and animals are able to quickly adapt the learned behaviors between similar tasks and learn new skills when presented with new situations. Here we seek to answer the question: Will learning to combine adaptation and exploration lead to a more efficient transfer of policies between domains? We introduce a principled mechanism that can"Adapt-to-Learn", that is adapt the source policy to learn to solve a target task with significant transition differences and uncertainties. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm with significantly reduced sample complexity in transferring skills between related tasks.
A comprehensive DFT based insights into the physical properties of tetragonal Mo5PB2

Tetragonal Mo5PB2 compound, a recently discovered superconductor, belongs to technologically important class of materials. It is quite surprising to note that a large number of physical properties of Mo5PB2, including elastic properties and their anisotropy, acoustic behavior, electronic (charge density distribution, electron density difference), thermo-physical, bonding characteristics, and optical properties have not been carried out at all. In the present work we have explored all these properties in details for the first time with density functional theory based first-principles method. Mo5PB2 is found to be a mechanically stable, elastically anisotropic compound with ductile character. Moreover, the chemical bonding is interpreted by calculating the electronic energy density of states, electron density distribution, elastic properties and Mulliken bond population analysis. Mo5PB2 has a combination of mainly ionic, metallic, and some covalent bonding characteristics. The compound possesses high level of machinability. The band structure along with a large electronic density of states at the Fermi level reveals metallic character. Calculated values of different thermal parameters of Mo5PB2 are closely related to the elastic properties. The energy dependent optical parameters show close assent to the underlying electronic band structure. The optical absorption and reflectivity spectra and the low energy index of refraction of Mo5PB2 show that the compound holds promise to be used in optoelectronic device sector. Unlike the notable anisotropy found in elastic, mechanical properties and minimum thermal conductivity, the optical parameters are found to be almost isotropic with respect to the polarization direction of the incident electric field.
Can You Traducir This? Machine Translation for Code-Switched Input

Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools. We focus here on Machine Translation (MT) of CSW texts, where we aim to simultaneously disentangle and translate the two mixed languages. Due to the lack of actual translated CSW data, we generate artificial training data from regular parallel texts. Experiments show this training strategy yields MT systems that surpass multilingual systems for code-switched texts. These results are confirmed in an alternative task aimed at providing contextual translations for a L2 writing assistant.
Strong and fast rising pressure waves emitted by plasmonic vapor nanobubbles

Julien Lombard, Julien Lam, François Detcheverry, Thierry Biben, and Samy Merabia Departamento de F́ısica y Qúımica Teórica, Facultad de Qúımica Universidad Nacional Autónoma de México, Mexico City 04510, Mexico CEMES, CNRS and Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse Cedex, France Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, F-69622, Villeurbanne, France (Dated: May 12, 2021)
Sharp phase transition for Gaussian percolation in all dimensions

— We consider the level-sets of continuous Gaussian ﬁelds on R d above a certain level − ‘ ∈ R , which deﬁnes a percolation model as ‘ varies. We assume that the covariance kernel satisﬁes certain regularity, symmetry and positivity conditions as well as a polynomial decay with exponent greater than d (in particular, this includes the Bargmann–Fock ﬁeld). Under these assumptions, we prove that the model undergoes a sharp phase transition around its critical point ‘ c . More precisely, we show that connection probabilities decay exponentially for ‘ < ‘ c and percolation occurs in suﬃciently thick 2D slabs for ‘ > ‘ c . This extends results recently obtained in dimension d = 2 to arbitrary dimensions through completely diﬀerent techniques. The result follows from a global comparison with a truncated (i.e. with ﬁnite range of dependence) and discretized (i.e. deﬁned on the lattice ε Z d ) version of the model, which may be of independent interest. The proof of this comparison relies on an interpolation scheme that integrates out the long-range and inﬁnitesimal correlations of the model while compensating them with a slight change in the parameter ‘ . Résumé. — On considère les courbes de niveau de champs gaussiens continus sur R d au-dessus d’un certain niveau − ‘ ∈ R , ce qui déﬁnit un modèle de percolation lorsque ‘ varie. Nous supposons que le noyau de covariance satisfait certaines conditions de régularité, de symétrie et de positivité ainsi qu’une décroissance polynomiale d’exposant supérieur à d (cela inclut notamment le champ de Bargmann–Fock). Sous ces hypothèses, nous prouvons que le modèle subit une transition de phase abrupte autour de son point critique ‘ c . Plus précisément, nous montrons que les probabilités de connexion décroissent exponentiellement pour ‘ < ‘ c et que la percolation se produit dans des dalles 2D suﬃsamment épaisses pour ‘ > ‘ c . Ceci étenddles résultats récemment obtenus en dimension d = 2 à des dimensions arbitraires par des techniques complètement diﬀérentes. Le résultat découle d’une comparaison globale avec une version tronquée (c’est-à-dire avec une plage de dépendance ﬁnie) et discrétisée (c’est-à-dire déﬁnie sur le réseau ε Z d ) du modèle, qui peut présenter un intérêt indépendant. La démonstration de cette comparaison repose sur un schéma d’interpolation qui intègre les corrélations à longue portée et inﬁnitésimales du modèle tout en les compensant par une légère modiﬁcation du paramètre ‘ .
A Hybrid Decomposition-based Multi-objective Evolutionary Algorithm for the Multi-Point Dynamic Aggregation Problem

An emerging optimisation problem from the real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become one of the active research topics of the multi-robot system. This paper focuses on a multi-objective MPDA problem which is to design an execution plan of the robots to minimise the number of robots and the maximal completion time of all the tasks. The strongly-coupled relationships among robots and tasks, the redundancy of the MPDA encoding, and the variable-size decision space of the MO-MPDA problem posed extra challenges for addressing the problem effectively. To address the above issues, we develop a hybrid decomposition-based multi-objective evolutionary algorithm (HDMOEA) using $ \varepsilon $-constraint method. It selects the maximal completion time of all tasks as the main objective, and converted the other objective into constraints. HDMOEA decomposes a MO-MPDA problem into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound robot number. All the subproblems are optimized simultaneously with the transferring knowledge from other subproblems. Besides, we develop a hybrid population initialisation mechanism to enhance the quality of initial solutions, and a reproduction mechanism to transmit effective information and tackle the encoding redundancy. Experimental results show that the proposed HDMOEA method significantly outperforms the state-of-the-art methods in terms of several most-used metrics.
A Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data‐driven models are increasingly utilized to perform crucial decision‐making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group‐specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across‐group parity, in comparison with the single fairness model, group‐separate model, and group‐ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real‐world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID‐19).
Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation

Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works suggest that performing category alignment can alleviate domain shift reasonably. However, they are mainly based on image-to-image adversarial training and little consideration is given to semantic variations of an object among images, failing to capture a comprehensive picture of different categories. This motivates us to explore a holistic representative, the semantic distribution from each category in source domain, to mitigate the problem above. In this paper, we present semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment under the guidance of semantic distributions. Specifically, we first design a pixel-wise contrastive loss by considering the correspondences between semantic distributions and pixel-wise representations from both domains. Essentially, clusters of pixel representations from the same category should cluster together and those from different categories should spread out. Next, an upper bound on this formulation is derived by involving the learning of an infinite number of (dis)similar pairs, making it efficient. Finally, we verify that SDCA can further improve segmentation accuracy when integrated with the self-supervised learning. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.The code is publicly available at https://github.com/BIT-DA/SDCA
Agatha: Smart Contract for DNN Computation

Smart contract is one of the core features of Ethereum and has inspired many blockchain descendants. Since its advent, the verification paradigm of smart contract has been improving toward high scalability. It shifts from the expensive on-chain verification to the orchestration of off-chain VM (virtual machine) execution and on-chain arbitration with the pinpoint protocol. The representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism. Inspired by visionaries in academia and industry, we consider the DNN computation to be promising but on the next level of complexity for the verification paradigm of smart contract. Unfortunately, even for the state-of-the-art verification paradigm, off-chain VM execution of DNN computation has an orders-of-magnitude slowdown compared to the native off-chain execution. To enable the native off-chain execution of verifiable DNN computation, we present Agatha system, which solves the significant challenges of misalignment and inconsistency: (1) Native DNN computation has a graph-based computation paradigm misaligned with previous VM-based execution and arbitration; (2) Native DNN computation may be inconsistent cross platforms which invalidates the verification paradigm. In response, we propose the graph-based pinpoint protocol (GPP) which enables the pinpoint protocol on computational graphs, and bridges the native off-chain execution and the contract arbitration. We also develop a technique named Cross-evaluator Consistent Execution (XCE), which guarantees cross-platform consistency and forms the correctness foundation of GPP. We showcase Agatha for the DNN computation of popular models (MobileNet, ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain overhead, and an off-chain execution overhead of 3.0%, which represents an off-chain latency reduction of at least 602x compared to the state-of-the-art verification paradigm.
R2D2: Relational Text Decoding with Transformers

We propose a novel framework for modeling the interaction between graphical structures and the natural language text associated with their nodes and edges. Existing approaches typically fall into two categories. On group ignores the relational structure by converting them into linear sequences and then utilize the highly successful Seq2Seq models. The other side ignores the sequential nature of the text by representing them as fixed-dimensional vectors and apply graph neural networks. Both simplifications lead to information loss. Our proposed method utilizes both the graphical structure as well as the sequential nature of the texts. The input to our model is a set of text segments associated with the nodes and edges of the graph, which are then processed with a transformer encoder-decoder model, equipped with a self-attention mechanism that is aware of the graphical relations between the nodes containing the segments. This also allows us to use BERT-like models that are already trained on large amounts of text. While the proposed model has wide applications, we demonstrate its capabilities on data-to-text generation tasks. Our approach compares favorably against state-of-the-art methods in four tasks without tailoring the model architecture. We also provide an early demonstration in a novel practical application -- generating clinical notes from the medical entities mentioned during clinical visits.
Enormous explosion energy of Type IIP SN 2017gmr with bipolar 56Ni ejecta

The unusual Type IIP SN 2017gmr is revisited in order to pinpoint the origin of its anomalous features, including the peculiar light curve after about 100 days. The hydrodynamicmodelling suggests the enormous explosion energy of≈1052 erg. We find that the light curve with the prolonged plateau/tail transition can be reproduced either in the model with a high hydrogen abundance in the inner ejecta and a large amount of radioactive 56Ni, or in the model with an additional central energy source associated with the fallback/magnetar interaction in the propeller regime. The asymmetry of the late HU emission and the reported linear polarization are reproduced by the model of the bipolar 56Ni ejecta. The similar bipolar structure of the oxygen distribution is responsible for the two-horn structure of the [O i ] 6360, 6364 Å emission. The bipolar 56Ni structure along with the high explosion energy are indicative of the magneto-rotational explosion. We identify narrow high-velocity absorption features in HU and He i 10830Å lines with their origin in the fragmented cold dense shell formed due to the outer ejecta deceleration in a confined circumstellar shell.
Leveraging Sparse Linear Layers for Debuggable Deep Networks

We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. These networks remain highly accurate while also being more amenable to human interpretation, as we demonstrate quantiatively via numerical and human experiments. We further illustrate how the resulting sparse explanations can help to identify spurious correlations, explain misclassifications, and diagnose model biases in vision and language tasks. The code for our toolkit can be found at https://github.com/madrylab/debuggabledeepnetworks.
Floyd's Manifold is a Conjugation Space

E. E. Floyd showed in 1973 that there exist only two non-trivial cobordism classes that contain manifolds with three cells, and that they lie in dimensions 10 and 5. We prove that there is an action of the cyclic group C 2 on the 10-dimensional Floyd manifold which turns it into a conjugation manifold in the sense of Hausmann, Holm, and Puppe. The submanifold of ﬁxed points is the 5-dimensional Floyd manifold, whose cohomology is isomorphic to that of the large one, scaled down by dividing the cohomological degree by a factor two.
New constraints on axion-like dark matter using a Floquet quantum detector

Dark matter is one of the greatest mysteries in physics. It interacts via gravity and composes most of our universe, but its elementary composition is unknown. We search for nongravitational interactions of axion-like dark matter with atomic spins using a precision quantum detector. The detector is composed of spin-polarized xenon gas that can coherently interact with a background dark matter field as it traverses through the galactic dark matter halo. Conducting a 5-month-long search, we report on the first results of the Noble and Alkali Spin Detectors for Ultralight Coherent darK matter (NASDUCK) collaboration. We limit ALP-neutron interactions in the mass range of 4 × 10−15 to 4 × 10−12 eV/c2 and improve upon previous terrestrial bounds by up to 1000-fold for masses above 4 × 10−13 eV/c2. We also set bounds on pseudoscalar dark matter models with quadratic coupling.
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective

Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can recover the performance of these developments not by changing the objective, but by regularising the value-function estimator. Constraining the Lipschitz constant of a single layer using spectral normalisation is sufficient to elevate the performance of a Categorical-DQN agent to that of a more elaborated \rainbow{} agent on the challenging Atari domain. We conduct ablation studies to disentangle the various effects normalisation has on the learning dynamics and show that is sufficient to modulate the parameter updates to recover most of the performance of spectral normalisation. These findings hint towards the need to also focus on the neural component and its learning dynamics to tackle the peculiarities of Deep Reinforcement Learning.
Blocking Without Breaking: Identification and Mitigation of Non-Essential IoT Traffic

Abstract Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections from IoT devices, without rendering the devices inoperable. In this paper, we address this question by developing a rigorous methodology that relies on automated IoT-device experimentation to reveal which network connections (and the information they expose) are essential, and which are not. We further develop strategies to automatically classify network traffic destinations as either required (i.e., their traffic is essential for devices to work properly) or not, hence allowing firewall rules to block traffic sent to non-required destinations without breaking the functionality of the device. We find that indeed 16 among the 31 devices we tested have at least one blockable non-required destination, with the maximum number of blockable destinations for a device being 11. We further analyze the destination of network traffic and find that all third parties observed in our experiments are blockable, while first and support parties are neither uniformly required or non-required. Finally, we demonstrate the limitations of existing blocklists on IoT traffic, propose a set of guidelines for automatically limiting non-essential IoT traffic, and we develop a prototype system that implements these guidelines.