Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation of ultra-high-pressure shocks by collision of a fast plasma projectile driven in the laser-induced cavity pressure acceleration scheme with a solid target

A novel, efficient method of generating ultra-high-pressure shocks is proposed and investigated. In this method, the shock is generated by collision of a fast plasma projectile (a macro-particle) driven by laser-induced cavity pressure acceleration (LICPA) with a solid target placed at the LICPA accelerator channel exit. Using the measurements performed at the kilojoule PALS laser facility and two-dimensional hydrodynamic simulations, it is shown that the shock pressure ∼ Gbar can be produced with this method at the laser driver energy of only a few hundred joules, by an order of magnitude lower than the energy needed for production of such pressure with other laser-based methods known so far.
Point2Vec for Self-Supervised Representation Learning on Point Clouds

Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. In an in-depth analysis, we discover that the leakage of positional information reveals the overall object shape to the student even under heavy masking and thus hampers data2vec to learn strong representations for point clouds. We address this 3D-specific shortcoming by proposing point2vec, which unleashes the full potential of data2vec-like pre-training on point clouds. Our experiments show that point2vec outperforms other self-supervised methods on shape classification and few-shot learning on ModelNet40 and ScanObjectNN, while achieving competitive results on part segmentation on ShapeNetParts. These results suggest that the learned representations are strong and transferable, highlighting point2vec as a promising direction for self-supervised learning of point cloud representations.
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

The development of language models have moved from encoder-decoder to decoder-only designs. In addition, we observe that the two most popular multimodal tasks, the generative and contrastive tasks, are nontrivial to accommodate in one architecture, and further need adaptations for downstream tasks. We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. This is done with a simple model, called MaMMUT. It consists of a single vision encoder and a text decoder, and is able to accommodate contrastive and generative learning by a novel two-pass approach on the text decoder. We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Furthermore, the same architecture enables straightforward extensions to open-vocabulary object detection and video-language tasks. The model tackles a diverse range of tasks, while being modest in capacity. Our model achieves the state of the art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models. It shows very competitive results on VQA and Video Captioning, especially considering its capacity. Ablations confirm the flexibility and advantages of our approach.
One class of linear Fredholm integral equations with functionals and parameters

The theory of linear Fredholm integral-functional equations of the second kind with linear functionals and with a parameter is considered. The necessary and sufficient conditions are obtained for the coefficients of the equation and those parameter values, in the nighbohood of which the equation has solutions. The leading terms of the asymptotics of the solutions are constructed. The constructive method is proposed for constructing a solution both in the regular case and in the irregular one. In the regular case, the solution is constructed as a Taylor series in powers of the parameter. In the irregular case, the solution is constructed as a Laurent series in powers of the parameter. Constructive theory and method is demonstrated on the model example.
Targeted Adversarial Attacks on Wind Power Forecasts

In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a long short-term memory (LSTM) network for predicting the power generation of individual wind farms and a convolutional neural network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model’s performance, as well as the extent to which the attacker’s goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
Effects of finite volume and magnetic fields on thermodynamic properties of quark matter and fluctuations of conserved charges

In the current work, we present the influence of finite volume and magnetic field on the thermodynamic properties of isospin asymmetric quark matter using the Polyakov loop extended chiral SU(3) quark mean field (PCQMF) model at finite chemical potential and temperature. Within the PCQMF model, we use the scalar and vector field values in mean-field approximation to obtain the thermodynamic properties: pressure density, entropy density and energy density. The susceptibilities of conserved charges for strongly interacting matter for different system sizes as well as for different values of the magnetic field have been studied. A sizable shift in phase boundary towards the higher values of quark chemical potential ($\mu_q$) and temperature (T) has been observed for decreasing values of system volume as well as an opposite shift towards lower temperature and quark chemical potential for increasing magnetic field. We observe an enhancement in fluctuations of conserved charges in the regime of the transition temperature. These studies may have a significant role in understanding the thermodynamic observables extracted from heavy-ion collisions data.
Implications of GWTC-3 on primordial black holes from vacuum bubbles

The population of black holes inferred from the detection of gravitational waves by the LIGO-Virgo-KAGRA Collaboration has revealed interesting features in the properties of black holes in the Universe. We analyze the GWTC-3 dataset assuming the detected black holes in each event had an either astrophysical or primordial origin. In particular, we consider astrophysical black holes described by the fiducial \textsc{Power Law + Peak} distribution and primordial black holes whose mass function obeys a broken power law. These primordial black holes can be generated by vacuum bubbles that nucleate during inflation. We find that astrophysical black holes dominate the events with mass less than $\sim 30M_\odot$, whereas primordial black holes are responsible for the massive end, and also for the peak at $\sim 30M_\odot$ in the mass distribution. More than half of the observed events could come from primordial black hole mergers. We also discuss the implications on the primordial black hole formation mechanism and the underlying inflationary model.
Coarse correlated equilibria for continuous time mean field games in open loop strategies

In the framework of continuous time symmetric stochastic differential games in open loop strategies, we introduce a generalization of mean field game solution, called coarse correlated solution. This can be seen as the analogue of a coarse correlated equilibrium in the $N$-player game, where a moderator randomly generates a strategy profile and asks the players to pre-commit to such strategies before disclosing them privately to each one of them; such a profile is a coarse correlated equilibrium if no player has an incentive to unilaterally deviate. We justify our definition by showing that a coarse correlated solution for the mean field game induces a sequence of approximate coarse correlated equilibria with vanishing error for the underlying $N$-player games. Existence of coarse correlated solutions for the mean field game is proved by means of a minimax theorem. An example with explicit solutions is discussed as well.
A bright megaelectronvolt emission line in $\gamma$-ray burst GRB 221009A

The highly variable and energetic pulsed emission of a long gamma-ray burst (GRB) is thought to originate from local, rapid dissipation of kinetic or magnetic energy within an ultra-relativistic jet launched by a newborn compact object, formed during the collapse of a massive star. The spectra of GRB pulses are best modelled by power-law segments, indicating the dominance of non-thermal radiation processes. Spectral lines in the X-ray and soft $\gamma$-ray regime for the afterglow have been searched for intensively, but never confirmed. No line features ever been identified in the high energy prompt emission. Here we report the discovery of a highly significant ($>6 \sigma$) narrow emission feature at around $10$ MeV in the brightest ever GRB 221009A. By modelling its profile with a Gaussian, we find a roughly constant width $\sigma \sim 1$ MeV and temporal evolution both in energy ($\sim 12$ MeV to $\sim 6$ MeV) and luminosity ($\sim 10^{50}$ erg/s to $\sim 2 \times 10^{49}$ erg/s) over 80 seconds. We interpret this feature as a blue-shifted annihilation line of relatively cold ($k_\mathrm{B}T\ll m_\mathrm{e}c^2$) electron-positron pairs, which could have formed within the jet region where the brightest pulses of the GRB were produced. A detailed understanding of the conditions that can give rise to such a feature could shed light on the so far poorly understood GRB jet properties and energy dissipation mechanism.
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution

This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. To construct a dynamic player interaction graph, we leverage player statistics and their interactions during gameplay. We use a graph attention network to capture the attention that each player pays to each other, allowing for more accurate modeling of the dynamic player interactions. To handle the multivariate player statistics time series, we incorporate a temporal convolution layer, which provides the model with temporal predictive power. We evaluate the performance of our model using real-world sports data, demonstrating its effectiveness in predicting player performance. Furthermore, we explore the potential use of our model in a sports betting context, providing insights into profitable strategies that leverage our predictive power. The proposed method has the potential to advance the state-of-the-art in player performance prediction and to provide valuable insights for sports analytics and betting industries.
Fair Federated Medical Image Segmentation via Client Contribution Estimation

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies. Code is available at https://nvidia.github. io/NVFlare/research/fed-ce
Detecting Extreme Ideologies in Shifting Landscapes: an Automatic&Context-Agnostic Approach

In democratic countries, the ideology landscape is foundational to individual and collective political action; conversely, fringe ideology drives Ideologically Motivated Violent Extremism (IMVE). Therefore, quantifying ideology is a crucial first step to an ocean of downstream problems, such as; understanding and countering IMVE, detecting and intervening in disinformation campaigns, and broader empirical opinion dynamics modeling. However, online ideology detection faces two significant hindrances. Firstly, the ground truth that forms the basis for ideology detection is often prohibitively labor-intensive for practitioners to collect, requires access to domain experts and is specific to the context of its collection (i.e., time, location, and platform). Secondly, to circumvent this expense, researchers generate ground truth via other ideological signals (like hashtags used or politicians followed). However, the bias this introduces has not been quantified and often still requires expert intervention. This work presents an end-to-end ideology detection pipeline applicable to large-scale datasets. We construct context-agnostic and automatic ideological signals from widely available media slant data; show the derived pipeline is performant, compared to pipelines of common ideology signals and state-of-the-art baselines; employ the pipeline for left-right ideology, and (the more concerning) detection of extreme ideologies; generate psychosocial profiles of the inferred ideological groups; and, generate insights into their morality and preoccupations.
An in silico toxicogenomic approach in constructing the aflatoxin B1-mediated regulatory network of hub genes in hepatocellular carcinoma

Abstract Aflatoxin B1 (AFB1) can cause hepatocellular carcinoma (HCC) through a mutagenic mode of action but can also lead to global changes in gene expression; however, the AFB1 network of molecular pathways involved in HCC is not known. Here, we used toxicogenomic data from human liver cells exposed to AFB1 to infer the network of AFB1-responsive molecular pathways involved in HCC. The following computational tools: STRING, MCODE, cytoHubba, iRegulon, kinase enrichment tool KEA3, and DAVID were used to identify protein-protein interaction network, hub genes, transcription factors (TFs), upstream kinases, and biological processes (BPs). Predicted molecular events were validated with an external dataset, whereas the hub genes in HCC were validated using the UALCAN database. The results revealed an association between AFB1 and the hub genes involved in the cell cycle. We identified TFs that regulate the hub genes and linked them with upstream kinases including cyclin-dependent kinases, mitogen-activated protein kinase 1, and AKT. This approach enabled the construction of the AFB1-mediated regulatory network consisting of upstream kinases, TFs, hub genes, and BPs, thus revealing the signaling hierarchy and information flow that may contribute to AFB1-induced HCC. This could be a useful tool in predicting the molecular mechanisms involved in chemical-induced diseases when available toxicogenomic data exist.
Intolerance of uncertainty, cognitive fusion, coping self‐efficacy and self‐perceived burden in patients diagnosed with cancer

As the proportion of older people worldwide increases, cancer cases and deaths from cancer are rising. Suffering from cancer has a profound impact on the daily lives of patients and their families. Cancer patients often perceive themselves as a burden, which affects their quality of life; therefore, this study investigated the relationship between intolerance of uncertainty, cognitive fusion, and self‐perception as burdens and the mediating role of coping self‐efficacy in patients diagnosed with cancer.
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph

Existing graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss, which is effective for capturing the low-frequency signals of node features. Such a dual-pass design has shown empirical success on homophilic graphs, but its effectiveness on heterophilic graphs, where directly connected nodes typically have different labels, is unknown. In addition, existing GCL approaches fail to provide strong performance guarantees. Coupled with the unpredictability of GCL approaches on heterophilic graphs, their applicability in real-world contexts is limited. Then, a natural question arises: Can we design a GCL method that works for both homophilic and heterophilic graphs with a performance guarantee? To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass augmentation-free graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks. As a direct consequence of our analysis, we implement the Single-Pass Graph Contrastive Learning method (SP-GCL). Empirically, on 14 benchmark datasets with varying degrees of homophily, the features learned by the SP-GCL can match or outperform existing strong baselines with significantly less computational overhead, which demonstrates the usefulness of our findings in real-world cases.
DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis to reveal that ignoring the motion of moving objects can result in serious localization bias. Therefore, we propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. In contrast to previous global Bird-Eye-View (BEV) methods, DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. By iteratively refining the estimated object motion and location, the preceding features can be precisely aggregated to the current frame to mitigate the aforementioned adverse effects. The simple framework has two significant appealing properties. It is flexible and practical that can be plugged into most camera-based 3D object detectors. As there are predictions of object motion in the loop, it can easily track objects across frames according to their nearest center distances. Without bells and whistles, DORT outperforms all the previous methods on the nuScenes detection and tracking benchmarks with 62.5\% NDS and 57.6\% AMOTA, respectively. The source code will be released.
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding

Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. A powerful model is expected to be capable of capturing region-object correspondences and recognizing scene changes in a video clip, reflecting spatial and temporal granularity, respectively. To strengthen model's understanding into such fine-grained details, we propose a simple yet effective video-language modeling framework, S-ViLM, by exploiting the intrinsic structures of these two modalities. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features, simultaneously. Comprehensive evaluations demonstrate that S-ViLM performs favorably against existing approaches in learning more expressive representations. Specifically, S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition, and temporal action localization.
Prospects for localizing Planet 9 with a future Uranus mission

Past years have seen various publications attempting to explain the apparent clustering features of trans-Neptunian objects, the most popular explanation being an unconfirmed ‘Planet 9’. The recently proposed Uranus Orbiter and Probe mission by NASA’s Planetary Science and Astrobiology Decadal Survey could offer the opportunity to precisely determine Planet 9’s sky location and mass by carefully monitoring ranging data during the interplanetary cruise. We use Monte Carlo Markov chain methods to reconstruct simulated spacecraft trajectories in a simplified Solar system model containing Planet 9, providing an estimate of the mission’s localization capacity depending on sky location, Earth-spacecraft Doppler link noise level and data collection rate. We characterize the noise via the Allan deviation σA, scaled to the Cassini-era value $\sigma _{\rm A}^{\rm \scriptscriptstyle Cass} = 3 \times 10^{-15}$, finding that daily measurements of the spacecraft position can lead to ∼0.2 deg2 localization of Planet 9 (assuming M9 = 6.3 M⊕, d9 = 460 au). As little as a three-fold improvement in σA drastically decreases the sky localization area size to ∼0.01 deg2. Thus, we showcase that a future Uranus mission carries a significant potential also for non-Uranian science.
Fuzzy spacetime: fundamental limits of quantum-optical holographic bulk reconstruction

In this Essay we construct a concrete, non-perturbative realization of metric reconstruction using quantum-optical model of particle detectors in relativistic quantum information. The non-perturbative approach allows us to realize a version of"short-distance physics corresponds to poor statistics"idea by Kempf which occurs way above the Planck scale before one reaches the quantum-gravitational regime. In particular, the"fuzziness"of spacetime that arise from operational measurement protocols can be given a holographic dual interpretation using bulk-to-boundary correspondence between scalar correlators in asymptotically flat spacetimes. The holographic interpretation necessitates imperfect metric reconstruction even in principle due to the universality of future null infinity.