Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptographic Hardness of Learning Halfspaces with Massart Noise

We study the complexity of PAC learning halfspaces in the presence of Massart noise. In this problem, we are given i.i. d. labeled examples $(\mathbf{x}, y) \in \mathbb{R}^N \times \{ \pm 1\}$, where the distribution of $\mathbf{x}$ is arbitrary and the label $y$ is a Massart corruption of $f(\mathbf{x})$, for an unknown halfspace $f: \mathbb{R}^N \to \{ \pm 1\}$, with flipping probability $\eta(\mathbf{x}) \leq \eta<1/2$. The goal of the learner is to compute a hypothesis with small 0-1 error. Our main result is the first computational hardness result for this learning problem. Specifically, assuming the (widely believed) subexponential-time hardness of the Learning with Errors (LWE) problem, we show that no polynomial-time Massart halfspace learner can achieve error better than $\Omega(\eta)$, even if the optimal 0-1 error is small, namely $\mathrm{OPT} = 2^{-\log^{c} (N)}$ for any universal constant $c \in (0, 1)$. Prior work had provided qualitatively similar evidence of hardness in the Statistical Query model. Our computational hardness result essentially resolves the polynomial PAC learnability of Massart halfspaces, by showing that known efficient learning algorithms for the problem are nearly best possible.
Wasserstein interpolation with constraints and application to a parking problem

We consider optimal transport problems where the cost for transporting a given probability measure $\mu_0$ to another one $\mu_1$ consists of two parts: the first one measures the transportation from $\mu_0$ to an intermediate (pivot) measure $\mu$ to be determined (and subject to various constraints), and the second one measures the transportation from $\mu$ to $\mu_1$. This leads to Wasserstein interpolation problems under constraints for which we establish various properties of the optimal pivot measures $\mu$. Considering the more general situation where only some part of the mass uses the intermediate stop leads to a mathematical model for the optimal location of a parking region around a city. Numerical simulations, based on entropic regularization, are presented both for the optimal parking regions and for Wasserstein constrained interpolation problems.
Construction of Multi-period TSO-DSO Flexibility Regions

Active distribution networks (ADN) have grown considerably in recent years. Distributed energy resources present in ADNs can provide flexibility to the power system through TSO/DSO coordination, i.e., at the interface node (feeder) between the transmission and distribution network. This paper addresses the issue of calculating multi-period flexibility regions of the ADNs. Flexibility regions are tightly dependent between periods and conditioned on the actual deployment of such flexibilities in real-time. The existing state-of-the-art has not provided a robust methodology for building multi-period flexible regions. We present a new mathematical framework based on a non-iterative formulation that considers the multi-period flexibility boundary points in a single optimization problem. The proposed methodology is evaluated on IEEE standard test networks and compared with the most widely used methods in the literature.
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning

Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet the low latency and high reliability of network applications. To this end, we model the real-time NSP as an Online Network Slice Provisioning (ONSP) problem. Specifically, we formulate the ONSP problem as an online Multi-Objective Integer Programming Optimization (MOIPO) problem. Then, we approximate the solution of the MOIPO problem by applying the Proximal Policy Optimization (PPO) method to the traffic demand prediction. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art MOIPO solvers with a lower SLA violation rate and network operation cost.
Break and Make: Interactive Structural Understanding Using LEGO Bricks

Visual understanding of geometric structures with complex spatial relationships is a fundamental component of human intelligence. As children, we learn how to reason about structure not only from observation, but also by interacting with the world around us -- by taking things apart and putting them back together again. The ability to reason about structure and compositionality allows us to not only build things, but also understand and reverse-engineer complex systems. In order to advance research in interactive reasoning for part-based geometric understanding, we propose a challenging new assembly problem using LEGO bricks that we call Break and Make. In this problem an agent is given a LEGO model and attempts to understand its structure by interactively inspecting and disassembling it. After this inspection period, the agent must then prove its understanding by rebuilding the model from scratch using low-level action primitives. In order to facilitate research on this problem we have built LTRON, a fully interactive 3D simulator that allows learning agents to assemble, disassemble and manipulate LEGO models. We pair this simulator with a new dataset of fan-made LEGO creations that have been uploaded to the internet in order to provide complex scenes containing over a thousand unique brick shapes. We take a first step towards solving this problem using sequence-to-sequence models that provide guidance for how to make progress on this challenging problem. Our simulator and data are available at github.com/aaronwalsman/ltron. Additional training code and PyTorch examples are available at github.com/aaronwalsman/ltron-torch-eccv22.
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer

Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of which would result in vulnerability to rare but complex traffic situations, such as the sudden emergence of unknown objects. However, reasoning from a global context requires access to sensors of multiple types and adequate fusion of multi-modal sensor signals, which is difficult to achieve. On the other hand, the lack of interpretability in learning models also hampers the safety with unverifiable failure causes. In this paper, we propose a safety-enhanced autonomous driving framework, named Interpretable Sensor Fusion Transformer(InterFuser), to fully process and fuse information from multi-modal multi-view sensors for achieving comprehensive scene understanding and adversarial event detection. Besides, intermediate interpretable features are generated from our framework, which provide more semantics and are exploited to better constrain actions to be within the safe sets. We conducted extensive experiments on CARLA benchmarks, where our model outperforms prior methods, ranking the first on the public CARLA Leaderboard. Our code will be made available at https://github.com/opendilab/InterFuser
Phonon Interference at the Atomic Scale

Phonons diffraction and interference patterns are observed at the atomic scale, using molecular dynamics simulations in systems containing crystalline silicon and nanometric obstacles as voids or amorphous-inclusions. The diffraction patterns caused by these nano-architectured systems of the same order as the phonon wavelengths are similar to the ones predicted by a simple Fresnel-Kirchhoff integral, with a few differences due to the nature of the obstacle and the anisotropy of crystalline silicon. These findings give evidence of the wave nature of phonons, can help to a better comprehension of the interaction of phonons with nanoobjects and at long term can be useful for intelligent thermal management and phonon frequency filtering at the nanoscale.
Quantum complexity of permutations

Let $S_n$ be the symmetric group of all permutations of $\{1, \cdots, n\}$ with two generators: the transposition switching $1$ with $2$ and the cyclic permutation sending $k$ to $k+1$ for $1\leq k\leq n-1$ and $n$ to $1$ (denoted by $\sigma$ and $\tau$). In this article, we study quantum complexity of permutations in $S_n$ using $\{\sigma, \tau, \tau^{-1}\}$ as logic gates. We give an explicit construction of permutations in $S_n$ with quadratic quantum complexity lower bound $\frac{n^2-2n-7}{4}$. We also prove that all permutations in $S_n$ have quadratic quantum complexity upper bound $3(n-1)^2$. Finally, we show that almost all permutations in $S_n$ have quadratic quantum complexity lower bound when $n\rightarrow \infty$.
Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation

Combining deep learning with symbolic logic reasoning aims to capitalize on the success of both fields and is drawing increasing attention. Inspired by DeepLogic, an end-to-end model trained to perform inference on logic programs, we introduce IMA-GloVe-GA, an iterative neural inference network for multi-step reasoning expressed in natural language. In our model, reasoning is performed using an iterative memory neural network based on RNN with a gate attention mechanism. We evaluate IMA-GloVe-GA on three datasets: PARARULES, CONCEPTRULES V1 and CONCEPTRULES V2. Experimental results show DeepLogic with gate attention can achieve higher test accuracy than DeepLogic and other RNN baseline models. Our model achieves better out-of-distribution generalisation than RoBERTa-Large when the rules have been shuffled. Furthermore, to address the issue of unbalanced distribution of reasoning depths in the current multi-step reasoning datasets, we develop PARARULE-Plus, a large dataset with more examples that require deeper reasoning steps. Experimental results show that the addition of PARARULE-Plus can increase the model's performance on examples requiring deeper reasoning depths. The source code and data are available at https://github.com/Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language.
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation

This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation. Then, we demonstrate that user attributes can negatively impact diversity and coverage. Finally, we investigate the amount of information about users that ``survives'' from the training data into the recommendation lists produced by the recommender. This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak.
Unmatched Control Barrier Functions: Certainty Equivalence Adaptive Safety

This work applies universal adaptive control to control barrier functions to achieve safe control of dynamical systems with parametric model uncertainties. The proposed approach utilizes the certainty equivalence principle to methodically select a model-parameterized control barrier function and corresponding safety controller from an allowable set with instantaneous parameter estimates. While such a combination does not necessarily yield forward invariance without additional requirements on the barrier function, we show that safety can indeed be established by simply adjusting the adaptation gain online. Simulation results demonstrate the approach.
ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems

This paper proposes an algorithmic framework for various reconfiguration problems using zero-suppressed binary decision diagrams (ZDDs), a data structure for families of sets. In general, a reconfiguration problem checks if there is a step-by-step transformation between two given feasible solutions (e.g., independent sets of an input graph) of a fixed search problem such that all intermediate results are also feasible and each step obeys a fixed reconfiguration rule (e.g., adding/removing a single vertex to/from an independent set). The solution space formed by all feasible solutions can be exponential in the input size, and indeed many reconfiguration problems are known to be PSPACE-complete. This paper shows that an algorithm in the proposed framework efficiently conducts the breadth-first search by compressing the solution space using ZDDs, and finds a shortest transformation between two given feasible solutions if exists. Moreover, the proposed framework provides rich information on the solution space, such as the connectivity of the solution space and all feasible solutions reachable from a specified one. We demonstrate that the proposed framework can be applied to various reconfiguration problems, and experimentally evaluate their performances.
Supersymmetry and R-symmetries in Wess-Zumino models: properties and model dataset construction

The Nelson-Seiberg theorem and its extensions relate supersymmetry breaking and R-symmetries in Wess-Zumino models. But their applicability may be limited by previously found non-generic counterexamples. Constructing a dataset of R-symmetric Wess-Zumino models is useful for studying the occurrence of such counterexamples as well as other purposes. This work gives a pedagogical review on the basics of supersymmetry in (3+1)-dimensions, Wess-Zumino models and their supergravity extensions, the Nelson-Seiberg theorem and its extensions. We present a preliminary construction of the dataset of R-symmetric Wess-Zumino models with up to 5 chiral ﬁelds. Among 925 models in total, 20 of them with non-generic R-charges are counterexamples to both the Nelson-Seiberg theorem and its extensions. Thus the dataset gives an estimation of the accuracy of the ﬁeld counting method based on these theorems. More constructions and applications of the dataset are expected in future work.
Integrated Human Activity Sensing and Communications

Advances in wireless communication and signal processing facilitate integrated sensing and communication (ISAC) – a compelling technology that intrinsically combines sensing and communication functionalities for the dual-purpose exploitation of wireless/hardware resources and pursues mutual benefits. Consequently, the next-generation communications network will be perceptive. In this article, we provide a review of human-related sensing in the context of ISAC. We first present a general ISAC receiver signal processing framework, with a focus on human activity recognition (HAR). Based on its specific spatial deployments, we then categorize ISAC HAR into monostatic, bi-static, and distributed deployments, and discuss their properties, critical research problems and solutions. To facilitate the system's realization and improve its recognition performance, we then explore the inherent connections between the physical-layer system parameters and HAR performance metrics. Experimental results are presented for characterizing the sensing potentials of different ISAC systems. Finally, we review the technical challenges and identify the open research problems.
Hydrogen Molecules and the Radiative Cooling of Pregalactic Shocks II: Low Velocity Shocks at High Redshift

Detailed results for the hydrodynamical, thermal, ionization, and molecular formation history of postshock cooling flows behind steady state shocks in a primordial gas at redshifts z = 5, 10, and 20 are presented and analyzed for a wide range of shock velocities from 50 to 400 km/s. The nonequilibrium results indicate that, for a significant range of shock velocities, if the shock-heated gas can cool to 10,000 K within the age of the universe, then it quite commonly forms an H2 fraction in excess of 0.001 and cools at nearly constant pressure to less than 100 K. The presence of an external flux of ionizing and dissociating radiation can, for a range of fluxes similar to that expected from a background of quasars, actually increase the peak H2 concentration to values of order 10 to the -1.5 or higher; it also increases the cooling time to 100 K. 78 references.
Editorial

Multiple imputation (MI) is an increasingly popular way to handle missing data. Its appeal lies mainly in its enormous flexibility – if carefully handled, it can cope with missing data in variables of all types (quantitative, ordered categorical, nominal, etc.), and with all roles (outcome, predictor/exposure, confounder, etc.) – but also in the relative simplicity achieved by separating the analysis task into two steps: imputation and then analysis of completed data. The literature on MI has expanded greatly since it was previously reviewed in this journal in 19991,2 and 20013. However, a number of practical issues remain. Greater experience and some further methodological developments may be needed before practitioners can confidently and routinely apply MI. The papers in this issue of Statistical Methods in Medical Research tackle a variety of topics. All the papers arise from a workshop on MI held at the MRC Biostatistics Unit, Cambridge, UK on 12–13th April 2005: the speakers and their topics are listed at the end of this editorial.
Revisiting the combinatorics of the 2D Ising model

We provide a concise exposition with original proofs of combinatorial formulas for the 2D Ising model partition function, multi-point fermionic observables, spin and energy density correlations, for general graphs and interaction constants, using the language of Kac-Ward matrices. We also give a brief account of the relations between various alternative formalisms which have been used in the combinatorial study of the planar Ising model: dimers and Grassmann variables, spin and disorder operators, and, more recently, s-holomorphic observables. In addition, we point out that these formulas can be extended to the double-Ising model, defined as a pointwise product of two Ising spin configurations on the same discrete domain, coupled along the boundary.
Analytic extension from non-pseudoconvex boundaries and $A(D)$-convexity

Soit D ⊂⊂ C n , n ≥ 2, un domaine a bord C 2 et K ⊂ ∂D un compact tel que ∂D\K soit connexe. On etudie l'extension holomorphe des fonctions CR definies sur ∂D\K a des sous-ensembles de D. On dit que K est CR-convexe si son enveloppe A(D)-convexe, A(D) - hull(K), verifie K = ∂D∩A(D) - hull(K) (A(D) designe l'espace des fonctions holomorphes sur D et continues sur D). Le theoreme principal de cet article prouve l'extension holomorphe a ∂D\A(D) - hull(K), si K est CR-convexe.
Mitigating health disparities in EHR via deconfounder

Health disparities, or inequalities between different patient demographics, are becoming a crucial issue in medical decision-making, especially in Electronic Health Record (EHR) predictive modeling. In order to ensure the fairness of sensitive attributes, conventional studies mainly adopt calibration or re-weighting methods to balance the performance on among different demographic groups. However, we argue that these methods have some limitations. First, these methods usually mean making a trade-off between the model's performance and fairness. Second, many methods attribute the existence of unfairness completely to the data collection process, which lacks substantial evidence. In this paper, we provide an empirical study to discover the possibility of using deconfounder to address the disparity issue in healthcare. Our study can be summarized in two parts. The first part is a pilot study demonstrating the exacerbation of disparity when unobserved confounders exist. The second part proposed a novel framework, Parity Medical Deconfounder (PriMeD), to deal with the disparity issue in healthcare datasets. Inspired by the deconfounder theory, PriMeD adopts a Conditional Variational Autoencoder (CVAE) to learn latent factors (substitute confounders) for observational data, and extensive experiments are provided to show its effectiveness.
An Italian lexicon-based sentiment analysis approach for medical applications

Sentiment analysis aims at extracting opinions and or emotions mainly from written text. The most popular problem in sentiment analysis certainly is polarity detection, which falls into the broader class of Natural Language Processing (NLP) problems of text classification. To date, state-of-the-art approaches to text classification use neural language models built on popular architectures such as Transformers. However, these approaches are difficult to apply in low-resource languages and domains, as for instance the Italian language or small clinical trials. Motivated by this, this paper presents VADER-IT, a lexicon-based algorithm for polarity prediction in written text, that is an adaptation to the Italian language of the popular VADER. Unlike VADER, our system also predicts a polarity class (i.e. positive, negative or neutral). The system was tested on a dataset of 5495 healthcare related reviews from QSalute https://www.qsalute. it/, reaching a micro averaged F1--score = 81% and a micro averaged Jaccard - score = 73%.
A comparison of dimensionality reduction methods for large biological data

Large-scale data often suffer from the curse of dimensionality and the constraints associated with it; therefore, dimensionality reduction methods are often performed prior to most machine learning pipelines. In this paper, we directly compare autoencoders performance as a dimensionality reduction technique (via the latent space) to other established methods: PCA, LASSO, and t-SNE. To do so, we use four distinct datasets that vary in the types of features, metadata, labels, and size to robustly compare different methods. We test prediction capability using both Support Vector Machines (SVM) and Random Forests (RF). Significantly, we conclude that autoencoders are an equivalent dimensionality reduction architecture to the previously established methods, and often outperform them in both prediction accuracy and time performance when condensing large, sparse datasets.
Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds

In this paper, we propose an efficient point cloud classification method via manifold learning based feature representation. Different from conventional methods, we use manifold learning algorithms to embed point cloud features for better considering the geometric continuity on the surface. Then, the nature of point cloud can be acquired in low dimensional space, and after being concatenated with features in the original three-dimensional (3D) space, both the capability of feature representation and the classification network performance can be improved. We explore three traditional manifold algorithms (i.e., Isomap, Locally-Linear Embedding, and Laplacian eigenmaps) in detail, and finally, we select the Locally-Linear Embedding (LLE) algorithm due to its low complexity and locality consistency preservation. Furthermore, we propose a neural network based manifold learning (NNML) method to implement manifold learning based non-linear projection. Experiments demonstrate that the proposed two manifold learning methods can obtain better performances than the state-of-the-art methods, and the obtained mean class accuracy (mA) and overall accuracy (oA) can reach 91.4% and 94.4%, respectively. Moreover, because of the improved feature learning capability, the proposed NNML method can also have better classification accuracy on models with prominent geometric shapes. To further demonstrate the advantages of PointManifold, we extend it as a plug and play method for point cloud classification task, which can be directly used with existing methods and gain a significant improvement.