Miracle-dz/UniFAR-FTUs huggingface dataset:
New Adversarial Image Detection Based on Sentiment Analysis

Deep neural networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e.g., DeepFool, are on the rise and outrunning adversarial example detection techniques. This article presents a new adversarial example detector that outperforms state-of-the-art detectors in identifying the latest adversarial attacks on image datasets. Specifically, we propose to use sentiment analysis for adversarial example detection, qualified by the progressively manifesting impact of an adversarial perturbation on the hidden-layer feature maps of a DNN under attack. Accordingly, we design a modularized embedding layer with the minimum learnable parameters to embed the hidden-layer feature maps into word vectors and assemble sentences ready for sentiment analysis. Extensive experiments demonstrate that the new detector consistently surpasses the state-of-the-art detection algorithms in detecting the latest attacks launched against ResNet and Inception neutral networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The detector only has about 2 million parameters and takes less than 4.6 ms to detect an adversarial example generated by the latest attack models using a Tesla K80 GPU card.
Rich structural polymorphism of monolayer C60 from cluster rotation

The recent experimental fabrication of monolayer and few-layer C60 polymers paves the way for synthesizing two-dimensional cluster-assembled materials. Compared to atoms with the SO(3) symmetry, clusters as superatoms (e.g., C60) have an additional rotational degree of freedom, greatly enriching the phase spaces of superatom-assembled materials. Using first-principles calculations, we find the energy barriers of cluster rotation in quasi-tetragonal monolayer C60 structures are rather low (about 10 meV/atom). The small rotational energy barriers lead to a series of tetragonal C60 polymorphs with energies that are close to the experimental quasi-tetragonal (expt-qT) phase. Similarly, several dynamically stable quasi-hexagonal monolayer C60 structures are found to have energies within 7 meV/atom above the experimental quasi-hexagonal phase. Our calculations demonstrate photo-excited electron-hole pairs and electrostatic doping of electrons can effectively modulate the relative energies of quasi-tetragonal C60 polymorphs. Particularly, the unstable monolayer expt-qT phase becomes dynamically stable when it is electrostatically doped with electrons. In contrast, the relative energies between different quasi-hexagonal polymorphs are insensitive to electrostatic doping of electrons.
Contrastive losses as generalized models of global epistasis

Fitness functions map large combinatorial spaces of biological sequences to properties of interest. Inferring these multimodal functions from experimental data is a central task in modern protein engineering. Global epistasis models are an effective and physically-grounded class of models for estimating fitness functions from observed data. These models assume that a sparse latent function is transformed by a monotonic nonlinearity to emit measurable fitness. Here we demonstrate that minimizing contrastive loss functions, such as the Bradley-Terry loss, is a simple and flexible technique for extracting the sparse latent function implied by global epistasis. We argue by way of a fitness-epistasis uncertainty principle that the nonlinearities in global epistasis models can produce observed fitness functions that do not admit sparse representations, and thus may be inefficient to learn from observations when using a Mean Squared Error (MSE) loss (a common practice). We show that contrastive losses are able to accurately estimate a ranking function from limited data even in regimes where MSE is ineffective. We validate the practical utility of this insight by showing contrastive loss functions result in consistently improved performance on benchmark tasks.
HiPool: Modeling Long Documents Using Graph Neural Networks

Encoding long sequences in Natural Language Processing (NLP) is a challenging problem. Though recent pretraining language models achieve satisfying performances in many NLP tasks, they are still restricted by a pre-defined maximum length, making them challenging to be extended to longer sequences. So some recent works utilize hierarchies to model long sequences. However, most of them apply sequential models for upper hierarchies, suffering from long dependency issues. In this paper, we alleviate these issues through a graph-based method. We first chunk the sequence with a fixed length to model the sentence-level information. We then leverage graphs to model intra- and cross-sentence correlations with a new attention mechanism. Additionally, due to limited standard benchmarks for long document classification (LDC), we propose a new challenging benchmark, totaling six datasets with up to 53k samples and 4034 average tokens’ length. Evaluation shows our model surpasses competitive baselines by 2.6% in F1 score, and 4.8% on the longest sequence dataset. Our method is shown to outperform hierarchical sequential models with better performance and scalability, especially for longer sequences.
A Sparse Johnson-Lindenstrauss Transform using Fast Hashing

The \emph{Sparse Johnson-Lindenstrauss Transform} of Kane and Nelson (SODA 2012) provides a linear dimensionality-reducing map $A \in \mathbb{R}^{m \times u}$ in $\ell_2$ that preserves distances up to distortion of $1 + \varepsilon$ with probability $1 - \delta$, where $m = O(\varepsilon^{-2} \log 1/\delta)$ and each column of $A$ has $O(\varepsilon m)$ non-zero entries. The previous analyses of the Sparse Johnson-Lindenstrauss Transform all assumed access to a $\Omega(\log 1/\delta)$-wise independent hash function. The main contribution of this paper is a more general analysis of the Sparse Johnson-Lindenstrauss Transform with less assumptions on the hash function. We also show that the \emph{Mixed Tabulation hash function} of Dahlgaard, Knudsen, Rotenberg, and Thorup (FOCS 2015) satisfies the conditions of our analysis, thus giving us the first analysis of a Sparse Johnson-Lindenstrauss Transform that works with a practical hash function.
ACCURATE MODELING OF X-RAY EXTINCTION BY INTERSTELLAR GRAINS

Interstellar abundance determinations from fits to X-ray absorption edges often rely on the incorrect assumption that scattering is insignificant and can be ignored. We show instead that scattering contributes significantly to the attenuation of X-rays for realistic dust grain size distributions and substantially modifies the spectrum near absorption edges of elements present in grains. The dust attenuation modules used in major X-ray spectral fitting programs do not take this into account. We show that the consequences of neglecting scattering on the determination of interstellar elemental abundances are modest; however, scattering (along with uncertainties in the grain size distribution) must be taken into account when near-edge extinction fine structure is used to infer dust mineralogy. We advertise the benefits and accuracy of anomalous diffraction theory for both X-ray halo analysis and near edge absorption studies. We present an open source Fortran suite, General Geometry Anomalous Diffraction Theory (GGADT), that calculates X-ray absorption, scattering, and differential scattering cross sections for grains of arbitrary geometry and composition.
PoLyScriber: Integrated Fine-Tuning of Extractor and Lyrics Transcriber for Polyphonic Music

Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility. Typically, lyrics transcription can be performed by a two-step pipeline, i.e. a singing vocal extraction front end, followed by a lyrics transcriber back end, where the front end and back end are trained separately. Such a two-step pipeline suffers from both imperfect vocal extraction and mismatch between front end and back end. In this work, we propose a novel end-to-end integrated fine-tuning framework, that we call PoLyScriber, to globally optimize the vocal extractor front end and lyrics transcriber back end for lyrics transcription in polyphonic music. The experimental results show that our proposed PoLyScriber achieves substantial improvements over the existing approaches on publicly available test datasets.
Qumode transfer between continuous- and discrete-variable devices

Transferring quantum information between different types of quantum hardware is crucial for integrated quantum technology. In particular, converting information between continuous-variable (CV) and discrete-variable (DV) devices enables many applications in quantum networking, quantum sensing, quantum machine learning, and quantum computing. This paper addresses the transfer of CV-encoded information between CV and DV devices. We present an efficient method for encoding CV states and implementing CV gates on DV devices, as well as two teleportation protocols for transferring CV states between CV and DV devices. The success probability of the teleportation protocols depends on the measurement outcome and can be increased to near-deterministic values by adding ancillary qubits to the DV devices.
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution

We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics-based simulation to a level of detail that closely approximates that of a reference-quality off-line simulator with much higher resolution (26x element count in our examples) and accurate physical modeling. Our approach is rooted in our ability to construct - via simulation - a training set of paired frames, from the low- and high-resolution simulators respectively, that are in semantic correspondence with each other. We use face animation as an exemplar of such a simulation domain, where creating this semantic congruence is achieved by simply dialing in the same muscle actuation controls and skeletal pose in the two simulators. Our proposed neural network super-resolution framework generalizes from this training set to unseen expressions, compensates for modeling discrepancies between the two simulations due to limited resolution or cost-cutting approximations in the real-time variant, and does not require any semantic descriptors or parameters to be provided as input, other than the result of the real-time simulation. We evaluate the efficacy of our pipeline on a variety of expressive performances and provide comparisons and ablation experiments for plausible variations and alternatives to our proposed scheme.
Model Rain and Clouds over Oceans: Comparison with SSM/I Observations

Abstract A comparison of global model cloud and rain parameterization output with satellite observed radiances was carried out. Hydrometeor profiles from ECMWF operational short-range forecasts were combined with a microwave radiative transfer model to generate observation-equivalent radiances simulating the Special Sensor Microwave Imager (SSM/I) measurements. These were generated for two 15-day periods in January and July 2001 to be compared to SSM/I observations from three DMSP satellites, namely F-13, F-14, and F-15. The simulations were analyzed to isolate the relative contributions of water vapor, cloud water, rain, and snow to the total signal given their frequency of occurrence in the global fields. The 19.35-GHz channel has the great advantage of being less sensitive to cloud geometry and model-generated snow, thus providing a more unique relationship between cloud–rainwater and blackbody equivalent brightness temperatures (TBs). The 37.0-GHz channel showed great skill in separating cloud and (mo.
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators

We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective training algorithms, but the ability to bootstrap real-world training and enable broad generalization. To this end, our system combines scalable deep RL from real-world data with bootstrapping from training in simulation, and incorporates auxiliary inputs from existing computer vision systems as a way to boost generalization to novel objects, while retaining the benefits of end-to-end training. We analyze the tradeoffs of different design decisions in our system, and present a large-scale empirical validation that includes training on real-world data gathered over the course of 24 months of experimentation, across a fleet of 23 robots in three office buildings, with a total training set of 9527 hours of robotic experience. Our final validation also consists of 4800 evaluation trials across 240 waste station configurations, in order to evaluate in detail the impact of the design decisions in our system, the scaling effects of including more real-world data, and the performance of the method on novel objects. The projects website and videos can be found at \href{http://rl-at-scale.github. io}{rl-at-scale.github. io}.
An Independent Determination of the Distance to Supernova SN 1987A by Means of the Light Echo AT 2019xis

Accurate distance determination to astrophysical objects is essential for the understanding of their intrinsic brightness and size. The distance to SN 1987A has been previously measured by the expanding photosphere method and by using the angular size of the circumstellar rings with absolute sizes derived from light curves of narrow UV emission lines, with reported distances ranging from 46.77 to 55 kpc. In this study, we independently determined the distance to SN 1987A using photometry and imaging polarimetry observations of AT 2019xis, a light echo of SN 1987A, by adopting a radiative transfer model of the light echo developed in Ding et al. We obtained distances to SN 1987A in the range from 49.09 ± 2.16 kpc to 59.39 ± 3.27 kpc, depending on the interstellar polarization and extinction corrections, which are consistent with the literature values. This study demonstrates the potential of using light echoes as a tool for distance determination to astrophysical objects in the Milky Way, up to kiloparsec level scales.
Microwave-heating laboratory experiments for planetary mantle convection

Thermal evolution of telluric planets is mainly controlled by secular cooling and internal heating due to the decay of radioactive isotopes, two processes that are equivalent from the standpoint of convection dynamics. In a fluid cooled from above and volumetrically heated, convection is dominated by instabilities of the top boundary layer and the interior thermal structure is non-isentropic. Here we present innovative laboratory experiments where microwave radiation is used to generate uniform internal heat in fluids at high Prandtl number ( ${>}300$ ) and high Rayleigh–Roberts number (ranging from $10^{4}$ to $10^{7}$ ), appropriate for planetary mantle convection. Non-invasive techniques are employed to determine both temperature and velocity fields. We successfully validate the experimental results by conducting numerical simulations in three-dimensional Cartesian geometry that reproduce the experimental conditions. Scaling laws relating key characteristics of the thermal boundary layer, namely its thickness and temperature drop, to the Rayleigh–Roberts number have been established for both rigid and free-slip boundary conditions. A robust conclusion is that for rigid boundary conditions the internal temperature is significantly higher than for free-slip boundary conditions. Our scaling laws, coupled with plausible physical parameters entering the Rayleigh–Roberts number, enable us to calculate the mantle potential temperature for the Earth and Venus, two telluric planets with different mechanical boundary conditions at their surface.
StarPlat: A Versatile DSL for Graph Analytics

Graphs model several real-world phenomena. With the growth of unstructured and semi-structured data, parallelization of graph algorithms is inevitable. Unfortunately, due to inherent irregularity of computation, memory access, and communication, graph algorithms are traditionally challenging to parallelize. To tame this challenge, several libraries, frameworks, and domain-specific languages (DSLs) have been proposed to reduce the parallel programming burden of the users, who are often domain experts. However, existing frameworks to model graph algorithms typically target a single architecture. In this paper, we present a graph DSL, named StarPlat, that allows programmers to specify graph algorithms in a high-level format, but generates code for three different backends from the same algorithmic specification. In particular, the DSL compiler generates OpenMP for multi-core, MPI for distributed, and CUDA for many-core GPUs. Since these three are completely different parallel programming paradigms, binding them together under the same language is challenging. We share our experience with the language design. Central to our compiler is an intermediate representation which allows a common representation of the high-level program, from which individual backend code generations begin. We demonstrate the expressiveness of StarPlat by specifying four graph algorithms: betweenness centrality computation, page rank computation, single-source shortest paths, and triangle counting. We illustrate the effectiveness of our approach by comparing the performance of the generated codes with that obtained with hand-crafted library codes. We find that the generated code is competitive to library-based codes in many cases. More importantly, we show the feasibility to generate efficient codes for different target architectures from the same algorithmic specification of graph algorithms.
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation

Clothes grasping and unfolding is a core step in robotic-assisted dressing. Most existing works leverage depth images of clothes to train a deep learning-based model to recognize suitable grasping points. These methods often utilize physics engines to synthesize depth images to reduce the cost of real labeled data collection. However, the natural domain gap between synthetic and real images often leads to poor performance of these methods on real data. Furthermore, these approaches often struggle in scenarios where grasping points are occluded by the clothing item itself. To address the above challenges, we propose a novel Bi-directional Fractal Cross Fusion Network (BiFCNet) for semantic segmentation, enabling recognition of graspable regions in order to provide more possibilities for grasping. Instead of using depth images only, we also utilize RGB images with rich color features as input to our network in which the Fractal Cross Fusion (FCF) module fuses RGB and depth data by considering global complex features based on fractal geometry. To reduce the cost of real data collection, we further propose a data augmentation method based on an adversarial strategy, in which the color and geometric transformations simultaneously process RGB and depth data while maintaining the label correspondence. Finally, we present a pipeline for clothes grasping and unfolding from the perspective of semantic segmentation, through the addition of a strategy for grasp point selection from segmentation regions based on clothing flatness measures, while taking into account the grasping direction. We evaluate our BiFCNet on the public dataset NYUDv2 and obtained comparable performance to current state-of-the-art models. We also deploy our model on a Baxter robot, running extensive grasping and unfolding experiments as part of our ablation studies, achieving an 84% success rate.
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements

Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects on the correctness of LM outputs, and introduce Vera, a general-purpose model that estimates the plausibility of declarative statements based on commonsense knowledge. Trained on ~7M commonsense statements created from 19 QA datasets and two large-scale knowledge bases, and with a combination of three training objectives, Vera is a versatile model that effectively separates correct from incorrect statements across diverse commonsense domains. When applied to solving commonsense problems in the verification format, Vera substantially outperforms existing models that can be repurposed for commonsense verification, and it further exhibits generalization capabilities to unseen tasks and provides well-calibrated outputs. We find that Vera excels at filtering LM-generated commonsense knowledge and is useful in detecting erroneous commonsense statements generated by models like ChatGPT in real-world settings.
X-Ray Studies of Blazar 1ES 1959+650 Using Swift and XMM-Newton Satellite

High synchrotron energy peaked blazar 1ES 1959+650 was studied with the Swift and XMM-Newton satellites in a total of 127 observations during the period 2018 June–2020 December. We extensively studied its flux and spectral variability on intraday and long-term timescales. Discrete correlation function analysis between soft and hard X-ray bands indicates soft as well as hard lags. The results were used to constrain the magnetic field of the emitting region, which was found to be 0.64 ± 0.05 G. On long-term timescales, the distribution of fluxes shows lognormal behavior, which could be attributed to the minijets-in-a-jet model or might be due to the propagation of relativistic shocks down the jet. The spectral energy distribution around the synchrotron peak is well described by the log-parabola model. Spectral parameters like the peak energy E p , curvature β, and peak luminosity L p were derived from spectral analysis. Their correlations were studied to constrain the acceleration processes of the emitting particles. E p shows strong correlation with L p during the high state of the source, which indicates spectral changes might be caused by the variations of the average electron energy. Low values of the curvature parameter β and a weak correlation between E p and β indicate the coexistence of stochastic/statistical acceleration of electrons in the emitting region. Implications of other results are also discussed.
Digital and Cloud Forensic Challenges

Digital forensics and cloud forensics are increasingly important fields that face a range of challenges. This study aims to assess the general challenges faced in these fields. A literature review was conducted to identify the major challenges in digital and cloud forensics, including data acquisition, data analysis, data preservation, privacy concerns, and legal issues. The challenges were analyzed in detail, considering the reasons why they are challenges, the impact they have on digital and cloud forensics, and any potential solutions. The study concludes that the challenges faced in digital and cloud forensics are significant and varied, and that addressing these challenges is critical for the effective and efficient use of digital and cloud forensics in investigations. This study provides a valuable overview of the current state of digital and cloud forensic challenges and can help guide future research in this important field.