Miracle-dz/UniFAR-FTUs huggingface dataset:
Anisotropic field ionization in nanoclusters mediated by a Brunel-electron-driven plasma wave

Ionization is one of the most fundamental processes in intense laser-matter interaction. It is extremely efficient for clusters in laser fields and often leads to surprisingly high charge states at moderate laser intensities. Here we reveal a novel ionization mechanism in laser-cluster interaction through particle-in-cell simulations. As the laser field ionizes a cluster, Brunel electrons pushed back into the clustered plasma form an attosecond bunches, impulsively exciting plasma oscillation. The resulting localized wake field further ionizes the cluster, causing a highly ionized rod-like core along the polarization axis. This anisotropic ionization is prominent using few-cycle pulses and may be washed out using longer pulses due to collisional ionization. This newly identified ionization channel can potentially provide complicated site-specific control of ionization in nanometer-scale targets.
Synthesizing Anyone, Anywhere, in Any Pose

We address the task of in-the-wild human figure synthesis, where the primary goal is to synthesize a full body given any region in any image. In-the-wild human figure synthesis has long been a challenging and under-explored task, where current methods struggle to handle extreme poses, occluding objects, and complex backgrounds. Our main contribution is TriA-GAN, a keypoint-guided GAN that can synthesize Anyone, Anywhere, in Any given pose. Key to our method is projected GANs combined with a well-crafted training strategy, where our simple generator architecture can successfully handle the challenges of in-the-wild full-body synthesis. We show that TriA-GAN significantly improves over previous in-the-wild full-body synthesis methods, all while requiring less conditional information for synthesis (keypoints vs. DensePose). Finally, we show that the latent space of \methodName is compatible with standard unconditional editing techniques, enabling text-guided editing of generated human figures.
Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery

Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional samples to orient edges in the network. However, these models have not been shown to scale up the size of the genome, which is on the order of 103-104 genes. We introduce a new learner, SP-GIES, that jointly learns from interventional and observational datasets and achieves almost 4x speedup against an existing learner for 1,000 node networks. SP-GIES achieves an AUC-PR score of 0.91 on 1,000 node networks, and scales up to 2,000 node networks - this is 4x larger than existing works. We also show how SP-GIES improves downstream optimal experimental design strategies for selecting interventional experiments to perform on the system. This is an important step forward in realizing causal discovery at scale via autonomous experimental design.
Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease

Genome-Wide Association Studies (GWAS) help identify genetic variations in people with diseases such as Parkinson's disease (PD), which are less common in those without the disease. Thus, GWAS data can be used to identify genetic variations associated with the disease. Feature selection and machine learning approaches can be used to analyze GWAS data and identify potential disease biomarkers. However, GWAS studies have technical variations that affect the reproducibility of identified biomarkers, such as differences in genotyping platforms and selection criteria for individuals to be genotyped. To address this issue, we collected five GWAS datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. We evaluated the agreement among different strategies in terms of the Single Nucleotide Polymorphisms (SNPs) that were identified as potential PD biomarkers. Our results showed a low concordance of biomarkers discovered using different datasets or integration strategies. However, we identified fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In real world, often the state representation used may lack sufficient fidelity to specify such safety constraints. Operating based on an incomplete model can often produce unintended negative side effects (NSEs). To address these challenges, first, we associate safety signals with state-action trajectories (rather than just immediate state-action). This makes our safety model highly general. We also assume categorical safety labels are given for different trajectories, rather than a numerical cost function, which is harder to specify by the problem designer. We then employ a supervised learning model to learn such non-Markovian safety patterns. Second, we develop a Lagrange multiplier method, which incorporates the safety model and the underlying MDP model in a single computation graph to facilitate agent learning of safe behaviors. Finally, our empirical results on a variety of discrete and continuous domains show that this approach can satisfy complex non-Markovian safety constraints while optimizing agent's total returns, is highly scalable, and is also better than previous best approach for Markovian NSEs.
HGCC: Enhancing Hyperbolic Graph Convolution Networks on Heterogeneous Collaborative Graph for Recommendation

Due to the naturally power-law distributed nature of user-item interaction data in recommendation tasks, hyperbolic space modeling has recently been introduced into collaborative filtering methods. Among them, hyperbolic GCN combines the advantages of GCN and hyperbolic space and achieves a surprising performance. However, these methods only partially exploit the nature of hyperbolic space in their designs due to completely random embedding initialization and an inaccurate tangent space aggregation. In addition, the data used in these works mainly focus on user-item interaction data only, which further limits the performance of the models. In this paper, we propose a hyperbolic GCN collaborative filtering model, HGCC, which improves the existing hyperbolic GCN structure for collaborative filtering and incorporates side information. It keeps the long-tailed nature of the collaborative graph by adding power law prior to node embedding initialization; then, it aggregates neighbors directly in multiple hyperbolic spaces through the gyromidpoint method to obtain more accurate computation results; finally, the gate fusion with prior is used to fuse multiple embeddings of one node from different hyperbolic space automatically. Experimental results on four real datasets show that our model is highly competitive and outperforms leading baselines, including hyperbolic GCNs. Further experiments validate the efficacy of our proposed approach and give a further explanation by the learned embedding.
Pragmatically Appropriate Diversity for Dialogue Evaluation

Linguistic pragmatics state that a conversation's underlying speech acts can constrain the type of response which is appropriate at each turn in the conversation. When generating dialogue responses, neural dialogue agents struggle to produce diverse responses. Currently, dialogue diversity is assessed using automatic metrics, but the underlying speech acts do not inform these metrics. To remedy this, we propose the notion of Pragmatically Appropriate Diversity, defined as the extent to which a conversation creates and constrains the creation of multiple diverse responses. Using a human-created multi-response dataset, we find significant support for the hypothesis that speech acts provide a signal for the diversity of the set of next responses. Building on this result, we propose a new human evaluation task where creative writers predict the extent to which conversations inspire the creation of multiple diverse responses. Our studies find that writers' judgments align with the Pragmatically Appropriate Diversity of conversations. Our work suggests that expectations for diversity metric scores should vary depending on the speech act.
Dr. KID: Direct Remeshing and K-Set Isometric Decomposition for Scalable Physicalization of Organic Shapes

Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative <italic>K-means</italic> clustering and remeshing. For clustering, we need similarity between triangles (segments) which is defined as a distance function. The distance function maps each triangle's shape to a single point in the virtual 3D space. Thus, the distance between the triangles indicates their degree of dissimilarity. <italic>K-means</italic> clustering uses this distance and sorts segments into <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="tvcg-khan-3326595-eqinline-1-small.tif"/></alternatives></inline-formula> classes. After this, remeshing is applied to minimize the distance between triangles within the same cluster by making their shapes identical. Clustering and remeshing are repeated until the distance between triangles in the same cluster reaches an acceptable threshold. We adopt a curvature-aware strategy to determine the surface thickness and finalize puzzle pieces for 3D printing. Identical hinges and holes are created for assembling the puzzle components. For smoother outcomes, we use triangle subdivision along with curvature-aware clustering, generating curved triangular patches for 3D printing. Our algorithm was evaluated using various models, and the 3D-printed results were analyzed. Findings indicate that our algorithm performs reliably on target organic shapes with minimal loss of input geometry.
Orbital determination and dynamics of resonant extrasolar planetary systems

Abstract In this communication we review some properties and applications of mean-motion resonances in extrasolar planetary systems, with particular emphasis on the 2/1 commensurability. A first part is devoted to the dynamical structure of the 2/1 resonance, including (but not restricted to) the so-called apsidal corotations. In a second part we discuss the orbital evolution of resonant systems under the effects of non-conservative forces. Special attention is given to the use of apsidal corotations as markers of largescale orbital decay, possibly due to disk-planet interactions in primordial times. Finally, we analyze the interplay between dynamical analysis and orbital fitting. Using the HD82943 planetary system as an example, we discuss: (i) up to what point present orbital fits allow us to distinguish between different resonant configurations, and (ii) in what ways may the dynamical structure of resonances be used as a complementary part of the orbital fitting process.
Multi-band circular polarizer based on a twisted triple split-ring resonator

A multi-band circular polarizer using a twisted triple split-ring resonator (TSRR) is presented and studied numerically and experimentally. At four distinct resonant frequencies, the incident linearly polarized wave can be transformed into left/right-handed circularly polarized waves. Numerical simulation results show that a y-polarized wave can be converted into a right-handed circularly polarized wave at 5.738 GHz and 9.218 GHz, while a left-handed circularly polarized wave is produced at 7.292 GHz and 10.118 GHz. The experimental results are in agreement with the numerical results. The surface current distributions are investigated to illustrate the polarization transformation mechanism. Furthermore, the influences of the structure parameters of the circular polarizer on transmission spectra are discussed as well.
Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign problem is crucial, as the signal-to-noise ratio decreases exponentially with Euclidean time, requiring significant computing resources. Inspired by similar scenarios in lattice quantum field theory and spin systems, in this work, we employ integration contour deformations to improve the GFMC signal-to-noise ratio. Machine learning techniques are used to select optimal contours with minimal variance from parameterized families of deformations. As a proof of principle, we consider the deuteron binding energies and Euclidean density response functions. We only observe mild signal-to-noise improvement for the binding energy case. On the other hand, we achieve an order of magnitude reduction of the variance for Euclidean density response functions, paving the way for computing electron- and neutrino-nucleus cross-sections of larger nuclei.
Two-dimensional MA 2 Z 4 Heterostructures: An Emerging Designer Material Platform for Electronics, Optoelectronics and Energy Conversion Applications

Recent experimental synthesis of ambient-stable MoSi 2 N 4 monolayer via chemical vapor deposition and the experimental demonstration of exceptional mechanical and electrical properties have garnered enormous research interests. The intercalation morphology of MoSi 2 N 4 – composed of a transition metal nitride (Mo-N) inner sub-monolayer sandwiched by two silicon nitride (Si-N) outer sub-monolayers – have motivated the computational discovery of an expansive family of synthetic MA 2 Z 4 monolayers with no bulk materials counterparts, where M = transition metals or alkaline earth metals; A = Si, Ge, As; and N = N, P, As. MA 2 Z 4 monolayers exhibit highly unusual electronic, magnetic, optical, excitonic, valleytronic, piezoelectronic and topological properties, making them a compelling material platform for realizing next-generation solid-state and energy conversion device technologies. Recent advancement in heterostructure engineering further expands the opportunities of MA 2 Z 4 beyond their monolayer form, while also also introduces enormous challenges due to the huge design space for combining two or more monolayers to form a heterostructure. In this review, we summarize the recent rapid progress on the computational design of MA 2 Z 4 -based heterostructures based on ﬁrst-principle density functional theory (DFT) simulations - a central work horse widely use to understand the physics, chemistry and general design rules for speciﬁc targeted functions. Various types of vertically-stacked van der Waals heterostructures and laterally-stitched covalent-bonded heterostructures have been computationally discovered via DFT simulations, covering a large variety of applications, such as transistor channel, electrical contacts, spintronic devices, photodetectors, solar cells, photocatalytic water splitters and so on. We systematically clas-sify the MA 2 Z 4 -based heterostructures based on their contact types, and review their physical properties, with particular focuses on highlighting their performances in electronics, optoelectronics and energy conversion applications. This review unveils the vast device application potentials of MA 2 Z 4 -based heterostructures, and shall motivate the future experimental and theoretical efforts on the design and development of MA 2 Z 4 -based functional heterostructure devices.
Latent Position Network Models

In this chapter, we present a review of latent position models for networks. We review the recent literature in this area and illustrate the basic aspects and properties of this modeling framework. Through several illustrative examples we highlight how the latent position model is able to capture important features of observed networks. We emphasize how the canonical design of this model has made it popular thanks to its ability to provide interpretable visualizations of complex network interactions. We outline the main extensions that have been introduced to this model, illustrating its flexibility and applicability.
A Context-Switching/Dual-Context ROM Augmented RAM using Standard 8T SRAM

The landscape of emerging applications has been continually widening, encompassing various data-intensive applications like artificial intelligence, machine learning, secure encryption, Internet-of-Things, etc. A sustainable approach toward creating dedicated hardware platforms that can cater to multiple applications often requires the underlying hardware to context-switch or support more than one context simultaneously. This paper presents a context-switching and dual-context memory based on the standard 8T SRAM bit-cell. Specifically, we exploit the availability of multi-VT transistors by selectively choosing the read-port transistors of the 8T SRAM cell to be either high-VT or low-VT. The 8T SRAM cell is thus augmented to store ROM data (represented as the VT of the transistors constituting the read-port) while simultaneously storing RAM data. Further, we propose specific sensing methodologies such that the memory array can support RAM-only or ROM-only mode (context-switching (CS) mode) or RAM and ROM mode simultaneously (dual-context (DC) mode). Extensive Monte-Carlo simulations have verified the robustness of our proposed ROM-augmented CS/DC memory on the Globalfoundries 22nm-FDX technology node.
Spectrally Consistent Scattering, Absorption, and Polarization Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 um

AbstractA data library is developed containing the scattering, absorption, and polarization properties of ice particles in the spectral range from 0.2 to 100 μm. The properties are computed based on a combination of the Amsterdam discrete dipole approximation (ADDA), the T-matrix method, and the improved geometric optics method (IGOM). The electromagnetic edge effect is incorporated into the extinction and absorption efficiencies computed from the IGOM. A full set of single-scattering properties is provided by considering three-dimensional random orientations for 11 ice crystal habits: droxtals, prolate spheroids, oblate spheroids, solid and hollow columns, compact aggregates composed of eight solid columns, hexagonal plates, small spatial aggregates composed of 5 plates, large spatial aggregates composed of 10 plates, and solid and hollow bullet rosettes. The maximum dimension of each habit ranges from 2 to 10 000 μm in 189 discrete sizes. For each ice crystal habit, three surface roughness conditions (i.
Approche multimodale par plongement de texte et de graphes pour la détection de messages abusifs [Multimodal approach using text and graph embeddings for abusive message detection]

RÉSUMÉ. Les comportements abusifs sont de plus en plus courants sur les plateformes d’échange en ligne, ce qui oblige leurs propriétaires à trouver de nouvelles solutions de modération. Des méthodes automatiques utilisant le contenu textuel ou la structure de la conversation ont alors été proposées. Par ailleurs, de nouvelles méthodes génériques de représentation de texte et de graphes sont apparues, fondées sur la notion de plongement (ou embedding), tirant notamment proﬁt de l’augmentation drastique des données et de la puissance de calcul disponibles. Dans ce travail, nous évaluons cinq méthodes de plongement lexical et quatre méthodes de plongement de graphes sur une tâche de détection de messages abusifs. Ces deux types d’approches ne s’appuyant pas sur les mêmes informations, nous étudions également différentes combinaisons de ces plongements. Nous obtenons des résultats comparables, voire supérieurs, pour le texte, à des approches classiques avec sélection manuelle de caractéristiques. La combinaison des plongements de texte et de graphes apporte enﬁn une nette amélioration des performances. ABSTRACT. Abusive behaviors are common on online social networks, forcing hosts of such plat-forms to ﬁnd new moderation tools. Various methods based on the textual content or the structure of the conversation have thus emerged. Furthermore, new generic embedding methods have been proposed to represent text and graphs. Those approaches beneﬁt the current exponential growth in available data and computing power. In this work, we evaluate ﬁve text embedding methods and four graph embedding methods on an abusive message detection task. These two types of embedding are not based on the same information, therefore we also study various combinations of these methods. Our results are comparable, and even better for text, to standard approaches based on feature engineering. The combination of text and graph embeddings ﬁnally brings a clear improvement in performance.
Construction d’un corpus parallèle à partir de corpus comparables pour la simplification de textes médicaux en français [Building a parallel corpus from comparable corpora for French biomedical text simplification]

RÉSUMÉ. La simpliﬁcation automatique a pour objectif de produire une version de textes plus facile à comprendre à destination d’un public identiﬁé. Nous nous intéressons à la simpliﬁcation de textes médicaux. Le plus souvent, le lexique et les règles de simpliﬁcation sont acquis à partir de corpus parallèles. Comme de tels corpus n’existent pas en français, nous proposons des méthodes pour les construire à partir de corpus comparables. Notre méthode repose sur une étape de ﬁltrage, destinée à ne garder que les meilleures phrases candidates à l’alignement, et une étape d’alignement considérée comme un problème de catégorisation. Il s’agit de décider si une paire de phrases est alignable ou non. Nous exploitons différents types de descripteurs (essentiellement basés sur le lexique et les corpus) et obtenons jusqu’à 0,97 de F-mesure avec les données équilibrées. ABSTRACT. The purpose of automatic simpliﬁcation is to create version of texts which is easier to understand for a given targeted population. We aim at simplifying medical texts. Usually, lexicon and rules required for the simpliﬁcation are acquired from parallel corpora. Since such corpora are not available for French, we propose methods for their creation from comparable corpora. Our method relies on ﬁltering step, which purpose is to keep the best sentence candidates for alignment, and alignment step considered as categorization problem. The aim is to decide whether a pair of sentences is alignable or not. We exploit different types of features (mainly issued from lexicon and corpora) and get up to 0.97 F-measure with balanced data. MOTS-CLÉS : simpliﬁcation automatique