Miracle-dz/UniFAR-FTUs huggingface dataset:
Na-intercalation compounds and Na-ion batteries

The widely use of lithium-ion (Li-ion) batteries in various fields, from portable products to large-scale energy storage systems, has revolutionized our daily life. The 2019 Nobel Prize in Chemistry has been awarded to John B. Goodenough, M. Stanley Whittingham, and Akira Yoshino for their contributions in developing Li-ion batteries. Although Li-ion batteries are currently on-growing research topics, lithium availability is still a problem for mass production. In contrast to lithium, sodium resources are almost unlimited on Earth, and sodium is one of the most abundant elements in the Earth's crust. Hence, sodium-ion (Na-ion) batteries as a counterpart of Li-ion batteries have the potential to serve as the next-generation batteries. In this work, a brief history and recent development of Na-ion batteries are described. The fundamental physical and electronic properties, such as geometric structures, band structure, density of states, and spatial charge distributions, of Na-intercalation compounds are discussed. The outlook of Na-ion batteries is given at the last.
Zero-Shot AutoML with Pretrained Models

Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, particularly if D is small? Here, we extend automated machine learning (AutoML) to best make these choices. Our domain-independent meta-learning approach learns a zero-shot surrogate model which, at test time, allows to select the right deep learning (DL) pipeline (including the pre-trained model and fine-tuning hyperparameters) for a new dataset D given only trivial meta-features describing D such as image resolution or the number of classes. To train this zero-shot model, we collect performance data for many DL pipelines on a large collection of datasets and meta-train on this data to minimize a pairwise ranking objective. We evaluate our approach under the strict time limit of the vision track of the ChaLearn AutoDL challenge benchmark, clearly outperforming all challenge contenders.
Boosting Graph Structure Learning with Dummy Nodes

With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning. However, most of those strategies are designed based on practical experience rather than theoretical analysis. In this paper, we use a particular dummy node connecting to all existing vertices without affecting original vertex and edge properties. We further prove that such the dummy node can help build an efficient monomorphic edge-to-vertex transform and an epimorphic inverse to recover the original graph back. It also indicates that adding dummy nodes can preserve local and global structures for better graph representation learning. We extend graph kernels and graph neural networks with dummy nodes and conduct experiments on graph classification and subgraph isomorphism matching tasks. Empirical results demonstrate that taking graphs with dummy nodes as input significantly boosts graph structure learning, and using their edge-to-vertex graphs can also achieve similar results. We also discuss the gain of expressive power from the dummy in neural networks.
Spherical Sliced-Wasserstein

Many variants of the Wasserstein distance have been introduced to reduce its original computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages one-dimensional projections for which a closed-form solution of the Wasserstein distance is available, has received a lot of interest. Yet, it is restricted to data living in Euclidean spaces, while the Wasserstein distance has been studied and used recently on manifolds. We focus more specifically on the sphere, for which we define a novel SW discrepancy, which we call spherical Sliced-Wasserstein, making a first step towards defining SW discrepancies on manifolds. Our construction is notably based on closed-form solutions of the Wasserstein distance on the circle, together with a new spherical Radon transform. Along with efficient algorithms and the corresponding implementations, we illustrate its properties in several machine learning use cases where spherical representations of data are at stake: sampling on the sphere, density estimation on real earth data or hyperspherical auto-encoders.
Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map

Hybrid analog/digital beamforming is a promising technique to realize millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems cost-effectively. However, existing hybrid beamforming designs mainly rely on real-time channel training or beam sweeping to find the desired beams, which incurs prohibitive overhead due to a large number of antennas at both the transmitter and receiver with only limited radio frequency (RF) chains. To resolve this challenging issue, in this paper, we propose a new environment-aware hybrid beamforming technique that requires only light real-time training, by leveraging the useful tool of channel knowledge map (CKM) with the user's location information. CKM is a site-specific database, which offers location-specific channel-relevant information to facilitate or even obviate the acquisition of real-time channel state information (CSI). Two specific types of CKM are proposed in this paper for hybrid beamforming design in mmWave massive MIMO systems, namely channel angle map (CAM) and beam index map (BIM). It is shown that compared with existing environment-unaware schemes, the proposed environment-aware hybrid beamforming scheme based on CKM can drastically improve the effective communication rate, even under moderate user location errors, thanks to its great saving of the prohibitive real-time training overhead.
A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning

For preventing youth suicide, social media platforms have received much attention from researchers. A few researches apply machine learning, or deep learning-based text classification approaches to classify social media posts containing suicidality risk. This paper replicated competitive social media-based suicidality detection/prediction models. We evaluated the feasibility of detecting suicidal ideation using multiple datasets and different state-of-the-art deep learning models, RNN-, CNN-, and Attention-based models. Using two suicidality evaluation datasets, we evaluated 28 combinations of 7 input embeddings with 4 commonly used deep learning models and 5 pretrained language models in quantitative and qualitative ways. Our replication study confirms that deep learning works well for social media-based suicidality detection in general, but it highly depends on the dataset's quality.
C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments

Due to the vast number of students enrolled in Massive Open Online Courses (MOOCs), there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Such techniques take advantage of previous correct student implementations in order to provide automated, comprehensive, and personalized feedback to students. This paper presents C-Pack-IPAs, a publicly available benchmark of students' programs submitted for 25 different IPAs. C-Pack-IPAs contains semantically correct, semantically incorrect, and syntactically incorrect programs plus a test suite for each IPA. Hence, C-Pack-IPAs can be used to help evaluate the development of novel semantic, as well as syntactic, automated program repair frameworks, focused on providing feedback to novice programmers.
GDsmith: Detecting Bugs in Graph Database Engines

Graph database engines stand out in the era of big data for their efficiency of modeling and processing linked data. There is a strong need of testing graph database engines. However, random testing, the most practical way of automated test generation, faces the challenges of semantic validity, non-empty result, and behavior diversity to detect bugs in graph database engines. To address these challenges, in this paper, we propose GDsmith, the first black-box approach for testing graph database engines. It ensures that each randomly generated Cypher query satisfies the semantic requirements via skeleton generation and completion. GDsmith includes our technique to increase the probability of producing Cypher queries that return non-empty results by leveraging three types of structural mutation strategies. GDsmith also includes our technique to improve the behavior diversity of the generated Cypher queries by selecting property keys according to their previous frequencies when generating new queries. Our evaluation results demonstrate that GDsmith is effective and efficient for automated query generation and substantially outperforms the baseline. GDsmith successfully detects 27 previously unknown bugs on the released versions of three popular open-source graph database engines and receive positive feedback from their developers.
On the Bug-proneness of Structures Inspired by Functional Programming in JavaScript Projects

—Language constructs inspired by functional pro- gramming have made their way into most mainstream programming languages. Many researchers and developers consider that these constructs lead to programs that are more concise, reusable, and easier to understand. Notwithstanding, few studies investigate the prevalence of these structures and the impli-cations of using them in mainstream programming languages. This paper quantiﬁes the prevalence of four concepts typically associated with functional programming in JavaScript: recursion, immutability, lazy evaluation, and functions as values. We divide the latter into two groups, higher-order functions and callbacks & promises. We focus on JavaScript programs due to the availability of some of these concepts in the language since its inception, its inspiration from functional programming languages, and its popularity. We mine 91 GitHub repositories (more than 22 million LOC) written mostly in JavaScript (over 50 % of the code), measuring the usage of these concepts from both static and temporal perspectives. We also measure the likelihood of bug-ﬁxing commits removing uses of these concepts (which would hint at bug-proneness) and their association with the presence of code comments (which would hint at code that is hard to understand). We ﬁnd that these concepts are in widespread use ( 478 , 605 occurrences, 1 for every 46 . 65 lines of code, 43 . 59 % of LOC). In addition, the usage of higher-order functions, immutability, and lazy evaluation-related structures has been growing throughout the years for the analyzed projects, while the usage of recursion and callbacks & promises has decreased. We also ﬁnd statistical evidence that removing these structures, with the exception of the ones associated to immutability, is less common in bug-ﬁxing commits than in other In their presence with comment size. Our ﬁndings suggest to
Satellites in the Ti~1$s$ core level spectra of SrTiO$_3$ and TiO$_2$

Satellites in core level spectra of photoelectron spectroscopy (PES) can provide crucial information on the electronic structure and chemical bonding in materials, particular in transition metal oxides. This paper explores satellites of the Ti 1$s$ and 2$p$ core level spectra of SrTiO$_3$ and TiO$_2$. Conventionally, soft x-ray PES (SXPS) probes the Ti 2$p$ core level; however, it is not ideal to fully capture satellite features due to its inherent spin-orbit-splitting (SOS). Here, hard x-ray PES(HAXPES) provides access to the Ti 1$s$ spectrum instead, which allows us to study intrinsic charge responses upon core-hole creation without the complication from SOS and with favorable intrinsic linewidths. The experimental spectra are theoretically analyzed by two impurity models, including an Anderson impurity model (AIM) built on local density approximation (LDA) and dynamical mean-field theory (DMFT), and a conventional TiO$_6$ cluster model. The theoretical results emphasize the importance of explicit inclusion of higher-order Ti-O charge-transfer processes beyond the nearest-neighboring Ti-O bond to simulate the core level spectra of SrTiO$_3$ and TiO$_2$. The AIM approach with continuous bath orbitals provided by LDA+DMFT represents the experimental spectra well. Crucially, with the aid of the LDA+DMFT method, this paper provides a robust prescription of how to use the computationally cheap cluster model in fitting analyses of core level spectra.
Improving Diversity of Multiple Trajectory Prediction based on Map-adaptive Lane Loss

—Prior arts in the ﬁeld of motion predictions for autonomous driving tend to focus on ﬁnding a trajectory that is close to the ground truth trajectory. Such problem formulations and approaches, however, frequently lead to loss of diversity and biased trajectory predictions. Therefore, they are unsuitable for real-world autonomous driving where diverse and road-dependent multimodal trajectory predictions are critical for safety. To this end, this study proposes a novel loss function, Lane Loss , that ensures map-adaptive diversity and accommodates geometric constraints. A two-stage trajectory prediction architecture with a novel trajectory candidate proposal module, Trajectory Prediction Attention (TPA) , is trained with Lane Loss encourages multiple trajectories to be diversely distributed, covering feasible maneuvers in a map-aware manner. Furthermore, considering that the existing trajectory performance metrics are focusing on evaluating the accuracy based on the ground truth future trajectory, a quantitative evaluation metric is also suggested to evaluate the diversity of predicted multiple trajectories. The experiments performed on the Argoverse dataset show that the proposed method signiﬁcantly improves the diversity of the predicted trajectories without sacriﬁcing the prediction accuracy.
Controllable Image Enhancement

Editing flat-looking images into stunning photographs requires skill and time. Automated image enhancement algorithms have attracted increased interest by generating high-quality images without user interaction. However, the quality assessment of a photograph is subjective. Even in tone and color adjustments, a single photograph of auto-enhancement is challenging to fit user preferences which are sub-tle and even changeable. To address this problem, we present a semi-automatic image enhancement algorithm that can generate high-quality images with multiple styles by controlling a few parameters. We first disentangle photo retouching skills from high-quality images and build an efficient enhancement system for each skill. Specifically, an encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing (ISP) functions. The ISP functions are computationally efficient and consist of only 19 parameters. Despite our approach requiring multiple inferences to ob-tain the desired result, experimental results present that the proposed method achieves state-of-the-art performances on the benchmark dataset for image quality and model efficiency.
PRANC: Pseudo RAndom Networks for Compacting deep models

Communication becomes a bottleneck in various distributed Machine Learning settings. Here, we propose a novel training framework that leads to highly efﬁcient communication of models between agents. In short, we train our network to be a linear combination of many pseudo-randomly generated frozen models. For communication, the source agent transmits only the ‘seed’ scalar used to generate the pseudo-random ‘basis’ networks along with the learned linear mixture coefﬁcients. Our method, denoted as PRANC, learns almost 100 × fewer parameters than a deep model and still performs well on several datasets and architectures. PRANC enables 1) efﬁcient communication of models between agents, 2) efﬁcient model storage, and 3) accelerated inference by generating layer-wise weights on the ﬂy. We test PRANC on CIFAR-10, CIFAR-100, tinyImageNet, and ImageNet-100 with various architectures like AlexNet, LeNet, ResNet18, ResNet20, and ResNet56 and demonstrate a massive reduction in the number of parameters while provid-ing satisfactory performance on these benchmark datasets. The code is available https://github.com/UCDvision/PRANC
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our system consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.
Improved Coherent One-Way Quantum key Distribution for High-Loss Channels

The coherent one-way (COW) quantum key distribution (QKD) is a highly practical quantum communication protocol that is currently deployed in oﬀ-the-shelves products. However, despite its simplicity and widespread use, the security of COW-QKD is still an open problem. This is largely due to its unique security feature based on inter-signal phase distribution, which makes it very diﬃcult to analyze using standard security proof techniques. Here, to overcome this problem, we present a simple variant of COW-QKD and prove its security in the inﬁnite-key limit. The proposed modiﬁcations only involve an additional vacuum tail signal following every encoded signal and a balanced beam-splitter for passive measurement basis choice. Remarkably, the resulting key rate of our protocol is comparable with both the existing upper-bound on COW-QKD key rate and the secure key rate of the coherent-state BB84 protocol. Our ﬁndings therefore suggest that the secured deployment of COW-QKD systems in high loss optical networks is indeed feasible with minimal adaptations applied to its hardware and software.
Logic-based Reward Shaping for Multi-Agent Reinforcement Learning

Reinforcement learning (RL) relies heavily on exploration to learn from its environment and maximize observed rewards. Therefore, it is essential to design a reward function that guarantees optimal learning from the received experience. Previous work has combined automata and logic based reward shaping with environment assumptions to provide an automatic mechanism to synthesize the reward function based on the task. However, there is limited work on how to expand logic-based reward shaping to Multi-Agent Reinforcement Learning (MARL). The environment will need to consider the joint state in order to keep track of other agents if the task requires cooperation, thus suffering from the curse of dimensionality with respect to the number of agents. This project explores how logic-based reward shaping for MARL can be designed for different scenarios and tasks. We present a novel method for semi-centralized logic-based MARL reward shaping that is scalable in the number of agents and evaluate it in multiple scenarios.
Enriching Abusive Language Detection with Community Context

Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.
Generalised Bayesian Inference for Discrete Intractable Likelihood

Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical. This paper addresses this computational challenge through the development of a novel generalised Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to update beliefs about model parameters using a discrete Fisher divergence, in lieu of the problematic intractable likelihood. The result is a generalised posterior that can be sampled from using standard computational tools, such as Markov chain Monte Carlo, circumventing the intractable normalising constant. The statistical properties of the generalised posterior are analysed, with sufficient conditions for posterior consistency and asymptotic normality established. In addition, a novel and general approach to calibration of generalised posteriors is proposed. Applications are presented on lattice models for discrete spatial data and on multivariate models for count data, where in each case the methodology facilitates generalised Bayesian inference at low computational cost.
WaTZ: A Trusted WebAssembly Runtime Environment with Remote Attestation for TrustZone

WebAssembly (Wasm) is a novel low-level bytecode format that swiftly gained popularity for its efficiency, versatility and security, with near-native performance. Besides, trusted execution environments (TEEs) shield critical software assets against compromised infrastructures. However, TEEs do not guarantee the code to be trustworthy or that it was not tampered with. Instead, one relies on remote attestation to assess the code before execution. This paper describes WaTZ, which is (i) an efficient and secure runtime for trusted execution of Wasm code for Arm’s TrustZone TEE, and (ii) a lightweight remote attestation system optimised for Wasm applications running in TrustZone, as it lacks built-in mechanisms for attestation. The remote attestation protocol is formally verified using a state-of-the-art analyser and model checker. Our extensive evaluation of Arm-based hardware uses synthetic and real-world benchmarks, illustrating typical tasks IoT devices achieve. WaTZ’s execution speed is on par with Wasm runtimes in the normal world and reaches roughly half the speed of native execution, which is compensated by the additional security guarantees and the inter-operability offered by Wasm. WaTZ is open-source and available on GitHub along with instructions to reproduce our experiments.