Miracle-dz/UniFAR-FTUs huggingface dataset:
Faintest of Them All: ZTF 21aaoryiz/SN 2021fcg—Discovery of an Extremely Low Luminosity Type Iax Supernova

We present the discovery of ZTF 21aaoryiz/SN 2021fcg—an extremely low luminosity Type Iax supernova. SN 2021fcg was discovered by the Zwicky Transient Facility in the star-forming galaxy IC0512 at a distance of ≈27 Mpc. It reached a peak absolute magnitude of M r = −12.66 ± 0.20 mag, making it the least luminous thermonuclear supernova discovered to date. The E(B − V) contribution from the underlying host galaxy is unconstrained. However, even if it were as large as 0.5 mag, the peak absolute magnitude would be M r = −13.78 ± 0.20 mag—still consistent with being the lowest-luminosity SN. Optical spectra of SN 2021fcg taken at 37 and 65 days post-maximum show strong [Ca ii], Ca ii, and Na i D emission and several weak [Fe ii] emission lines. The [Ca ii] emission in the two spectra has extremely low velocities of ≈1300 and 1000 km s−1, respectively. The spectra very closely resemble those of the very low luminosity Type Iax supernovae SN 2008 ha, SN 2010ae, and SN 2019gsc taken at similar phases. The peak bolometric luminosity of SN 2021fcg is ≈ 2.5−0.3+1.5×1040 erg s−1, which is a factor of 3 lower than that for SN 2008 ha. The bolometric lightcurve of SN 2021fcg is consistent with a very low ejected nickel mass (M Ni≈0.8−0.5+0.4×10−3 M ⊙). The low luminosity and nickel mass of SN 2021fcg pose a challenge to the picture that low-luminosity SNe Iax originate from deflagrations of near-M ch hybrid carbon–oxygen–neon white dwarfs. Instead, the merger of a carbon–oxygen and oxygen–neon white dwarf is a promising model to explain SN 2021fcg.
Efﬁcient Training of High-Resolution Representation Seismic Image Fault Segmentation Network by Weakening Anomaly Labels

—Seismic data fault detection has recently been regarded as a 3D image segmentation task. The nature of fault structures in seismic image makes it difﬁcult to manually label faults. Manual labeling often has many false negative labels (abnormal labels), which will seriously harm the training process. In this work, we ﬁnd that region-based loss signiﬁcantly outperforms distribution-based loss when dealing with false negative labels, therefore we propose Mask Dice loss (MD loss), which is the ﬁrst reported region-based loss function for training 3D image segmentation models using sparse 2D slice labels. In addition, fault is an edge feature, and the current network widely used for fault segmentation downsamples the features multiple times, which is not conducive to edge representation and thus requires many parameters and computational effort to preserve the features. We propose Fault-Net, which always maintains the high-resolution features of seismic images, and the inference process preserves the edge information of faults and performs effective feature fusion to achieve high-quality fault segmentation with only a few parameters and computational effort. Experimental results show that MD loss can clearly weaken the effect of anomalous labels. The Fault-Net parameter is only 0.42MB, support up to 528 3 (1.5 × 10 8 , Float32) size cuboid inference on 16GB video ram, its inference speed on CPU and GPU is signiﬁcantly faster than other networks. It works well on most of the open data seismic images, and the result of our method is the state-of-the-art in the FORCE fault identiﬁcation competition.
Using Document Similarity Methods to create Parallel Datasets for Code Translation

Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code translation task. However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages. To bypass this limitation, unsupervised neural machine translation techniques have been proposed to learn code translation using only monolingual corpora. In this work, we propose to use document similarity methods to create noisy parallel datasets of code, thus enabling supervised techniques to be applied for automated code translation without having to rely on the availability or expensive curation of parallel code datasets. We explore the noise tolerance of models trained on such automatically-created datasets and show that these models perform comparably to models trained on ground truth for reasonable levels of noise. Finally, we exhibit the practical utility of the proposed method by creating parallel datasets for languages beyond the ones explored in prior work, thus expanding the set of programming languages for automated code translation.
Adaptively Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation

—In practical application, 3D Human Pose Estimation (HPE) is facing with several variable elements, involving the number of views, the length of the video sequence, and whether using camera calibration. To this end, we propose a uniﬁed framework named Multi-view and Temporal Fusing Transformer (MTF-Transformer) to adaptively handle varying view numbers and video length without calibration. MTF-Transformer consists of Feature Extractor, Multi-view Fusing Transformer (MFT), and Temporal Fusing Transformer (TFT). Feature Extractor estimates the 2D pose from each image and encodes the predicted coordinates and conﬁdence into feature embedding for further 3D pose inference. It discards the image features and focuses on lifting the 2D pose into the 3D pose, making the subsequent modules computationally lightweight enough to handle videos. MFT fuses the features of a varying number of views with a relative-attention block. It adaptively measures the implicit relationship between each pair of views and reconstructs the features. TFT aggregates the features of the whole sequence and predicts 3D pose via a transformer, which is adaptive to the length of the video and takes full advantage of the temporal information. With these modules, MTF-Transformer handles different application scenes, varying from a monocular-single-image to multi-view-video, and the camera calibration is avoidable. We demonstrate quantitative and qualitative results on the Human3.6M, TotalCapture, and KTH Multiview Football II. Compared with state-of-the-art methods with camera parameters, experiments show that MTF-Transformer not only obtains comparable results but also generalizes well to dynamic capture with an arbitrary number of unseen views. Code is available in https://github.com/lelexx/MTF-Transformer.
Self-explaining Neural Network with Plausible Explanations

Explaining the predictions of complex deep learning models, often referred to as ”black boxes” is critical in high-stakes domains like healthcare. However, post-hoc model explanations often are not understandable by clinicians and are diﬃcult to integrate into clinical work-ﬂow. Further, while most explainable models use individual clinical variables as “units of explanation”, human understanding often rely on higher-level concepts or feature representations. In this paper, we propose a novel, self-explaining neural network for longitudinal in-hospital mortality prediction using domain-knowledge driven Sequential Organ Failure Assessment (SOFA) organ-speciﬁc scores as the atomic units of explanation. We also design a novel procedure to quantitatively validate the model explanations against gold standard discharge diagnosis information of patients. Our results provide interesting insights into how each of the SOFA organ scores contribute to mortality at diﬀerent timesteps within longitudinal patient trajectory.
Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets

Time Series Classification (TSC) has drawn a lot of attention in literature because of its broad range of applications for different domains, such as medical data mining, weather forecasting. Although TSC algorithms are designed for balanced datasets, most real-life time series datasets are imbalanced. The Skewed distribution is a problem for time series classification both in distance-based and feature-based algorithms under the condition of poor class separability. To address the imbalance problem, both sampling-based and algorithmic approaches are used in this paper. Different methods significantly improve time series classification's performance on imbalanced datasets. Despite having a high imbalance ratio, the result showed that F score could be as high as 97.6% for the simulated TwoPatterns Dataset.
Modality-Guided Subnetwork for Salient Object Detection

Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior designs: 1) Our model works for both RGB and RGBD data, and dynamically estimates depth if not available. Taking the inner workings of depth-prediction networks into account, we propose to estimate the pseudo-geometry maps from RGB input — essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD results in real-time inference but achieves state-of-the-art performance compared to other RGB models. 3) The flexible and lightweight design of MGS facilitates the integration into RGBD two-streaming models. The introduced fusion design enables a cross-modality interaction to enable further progress but with a minimal cost.
DPUV3INT8: A Compiler View to programmable FPGA Inference Engines

We have a FPGA design, we make it fast, efficient, and tested for a few important examples. Now we must infer a general solution to deploy in the data center. Here, we describe the FPGA DPUV3INT8 design and our compiler effort. The hand-tuned SW-HW solution for Resnet50\_v1 has (close to) 2 times better images per second (throughput) than our best FPGA implementation; the compiler generalizes the hand written techniques achieving about 1.5 times better performance for the same example, the compiler generalizes the optimizations to a model zoo of networks, and it achieves 80+\% HW efficiency.
Document-Level Text Simplification: Dataset, Criteria and Baseline

Text simplification is a valuable technique. However, current research is limited to sentence simplification. In this paper, we define and investigate a new task of document-level text simplification, which aims to simplify a document consisting of multiple sentences. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable. Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task. Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation. We analyze the results and point out the shortcomings of the baseline models.
Using Trust for Heterogeneous Human-Robot Team Task Allocation

Human-robot teams have the ability to perform better across various tasks than human-only and robot-only teams. However, such improvements cannot be realized without proper task allocation. Trust is an important factor in teaming relationships, and can be used in the task allocation strategy. Despite the importance, most existing task allocation strategies do not incorporate trust. This paper reviews select studies on trust and task allocation. We also summarize and discuss how a bi-directional trust model can be used for a task allocation strategy. The bi-directional trust model represents task requirements and agents by their capabilities, and can be used to predict trust for both existing and new tasks. Our task allocation approach uses predicted trust in the agent and expected total reward for task assignment. Finally, we present some directions for future work, including the incorporation of trust from the human and human capacity for task allocation, and a negotiation phase for resolving task disagreements.
ZSpeedL - Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices

The recognition of unseen objects from a semantic representation or textual description, usually denoted as zero-shot learning, is more prone to be used in real-world scenarios when compared to traditional object recognition. Nevertheless, no work has evaluated the feasibility of deploying zero-shot learning approaches in these scenarios, particularly when using low-power devices. In this paper, we provide the first benchmark on the inference time of zero-shot learning, comprising an evaluation of state-of-the-art approaches regarding their speed/accuracy trade-off. An analysis to the processing time of the different phases of the ZSL inference stage reveals that visual feature extraction is the major bottleneck in this paradigm, but, we show that lightweight networks can dramatically reduce the overall inference time without reducing the accuracy obtained by the de facto ResNet101 architecture. Also, this benchmark evaluates how different ZSL approaches perform in low-power devices, and how the visual feature extraction phase could be optimized in this hardware. To foster the research and deployment of ZSL systems capable of operating in real-world scenarios, we release the evaluation framework used in this benchmark(https://github.com/CristianoPatricio/zsl-methods).
Piecewise Interpretable Hilbert Spaces

We define and study piecewise interpretable Hilbert spaces in continuous logic. These are Hilbert spaces which arise as direct limits of imaginary sorts of a model M of a theory T . We introduce natural examples of piecewise interpretable Hilbert spaces in a wide variety of contexts. We show that piecewise interpretable Hilbert spaces can be seen as encoding interesting model theoretic information about M or T , such as properties of definable measures or Galois-theoretic information. We also show that piecewise interpretable Hilbert spaces encode unitary group representations in various ways. We carry out a systematic structural analysis of piecewise interpretable Hilbert spaces with scattered subsets. As an application of this work, we recover the classification of the unitary representations of oligomorphic groups first discovered by Tsankov (2012). Our main tool is local stability theory in continuous logic.
Revisit dictionary learning for video compressive sensing under the Plug-and-Play framework

Aiming at high-dimensional (HD) data acquisition and analysis, snapshot compressive imaging (SCI) obtains the 2D compressed measurement of HD data with optical imaging systems and reconstructs HD data using compressive sensing algorithms. While the Plug-and-Play (PnP) framework offers an emerging solution to SCI reconstruction, its intrinsic denoising process is still a challenging problem. Unfortunately, existing denoisers in the PnP framework either suffer limited performance or require extensive training data. In this paper, we propose an efficient and effective shallow-learning-based algorithm for video SCI reconstruction. Revisiting dictionary learning methods, we empower the PnP framework with a new denoiser, the kernel singular value decomposition (KSVD). Benefited from the advent of KSVD, our algorithm retains a good trade-off among quality, speed, and training difficulty. On a variety of datasets, both quantitative and qualitative evaluations of our simulation results demonstrate the effectiveness of our proposed method. In comparison to a typical baseline using total variation, our method achieves around 2 dB improvement in PSNR and 0.02 in SSIM. We expect that our proposed PnP-KSVD algorithm can serve as a new baseline for video SCI reconstruction.
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model

To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g., cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-efficient methods in deep learning and found that they either encourage invariance to data stochasticity (e.g., consistency regularization under different augmentations) or model stochasticity (e.g., difference penalty for predictions of models with different dropout). To take the power of both worlds, we propose a novel X-model by simultaneously encouraging the invariance to {data stochasticity} and {model stochasticity}. Further, the X-model plays a minimax game between the feature extractor and task-specific heads to further enhance the invariance to model stochasticity. Extensive experiments verify the superiority of the X-model among various tasks, from a single-value prediction task of age estimation to a dense-value prediction task of keypoint localization, a 2D synthetic, and a 3D realistic dataset, as well as a multi-category object recognition task.
Near-Field Wireless Power Transfer with Dynamic Metasurface Antennas

Radio frequency wireless power transfer (WPT) enables charging low-power mobile devices without relying on wired infrastructure. Current existing WPT systems are typically designed assuming far-field propagation, where the radiated energy is steered in given angles, resulting in limited efficiency and possible radiation in undesired locations. When large arrays at high frequencies, such as dynamic metasurface antenna (DMA), are employed, WPT might take place in the radiating nearfield (Fresnel) region where spherical wave propagation holds, rather than plane wave propagation as in the far-field. In this paper, we study WPT systems charging multiple devices in the Fresnel region, where the energy transmitter is equipped with an emerging DMA, exploring how the antenna configuration can exploit the spherical wavefront to generate focused energy beams. In particular, after presenting a mathematical model for DMA-based radiating near-field WPT systems, we characterize the weighted sum-harvested energy maximization problem of the considered system, and we propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused energy beams that are capable of improving energy transfer efficiency in the radiating near-field with minimal energy pollution.
Hybrid Random Features

We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest. Special instantiations of HRFs lead to well-known methods such as trigonometric (Rahimi and Recht, 2007) or (recently introduced in the context of linear-attention Transformers) positive random features (Choromanski et al., 2021). By generalizing Bochner's Theorem for softmax/Gaussian kernels and leveraging random features for compositional kernels, the HRF-mechanism provides strong theoretical guarantees - unbiased approximation and strictly smaller worst-case relative errors than its counterparts. We conduct exhaustive empirical evaluation of HRF ranging from pointwise kernel estimation experiments, through tests on data admitting clustering structure to benchmarking implicit-attention Transformers (also for downstream Robotics applications), demonstrating its quality in a wide spectrum of machine learning problems.
A Decomposition Property for an $M^{X}/G/1$ Queue with Vacations

We introduce a queueing system that alternates between two modes, socalled working mode and vacation mode. During the working mode the system runs as an M/G/1 queue. Once the number of customers in the working mode drops to zero the vacation mode begins. During the vacation mode the system runs as a general queueing system (a service might be included) which is different from the one in the working mode. The vacation period ends in accordance with a given stopping rule, and then a random number of customers are transferred to the working mode. For this model we show that the conditional probability generating function of the number of customers given that the system is in the working mode is a product of three terms. This decomposition result puts under the same umbrella some models that have already been introduced in the past as well as some new models. Key-words: M/G/1 queue, busy period, vacation mode, working mode, disasters, steadystate
Phase Collapse in Neural Networks

Deep convolutional classifiers linearly separate image classes and improve accuracy as depth increases. They progressively reduce the spatial dimension whereas the number of channels grows with depth. Spatial variability is therefore transformed into variability along channels. A fundamental challenge is to understand the role of non-linearities together with convolutional filters in this transformation. ReLUs with biases are often interpreted as thresholding operators that improve discrimination through sparsity. This paper demonstrates that it is a different mechanism called phase collapse which eliminates spatial variability while linearly separating classes. We show that collapsing the phases of complex wavelet coefficients is sufficient to reach the classification accuracy of ResNets of similar depths. However, replacing the phase collapses with thresholding operators that enforce sparsity considerably degrades the performance. We explain these numerical results by showing that the iteration of phase collapses progressively improves separation of classes, as opposed to thresholding non-linearities.
Brownian magneto-gyrator as a tunable microengine

A Brownian particle performs gyrating motion around a potential energy minimum when subjected to thermal noises from two different heat baths. Here, we propose a magneto-gyrator made of a single charged Brownian particle that is steered by an external magnetic field. Key properties, such as the direction of gyration, the torque exerted by the engine on the confining potential and the maximum power delivered by the microengine can be tuned by varying the strength and direction of the applied magnetic field. Further tunability is obtained by rotating the potential in the plane perpendicular to the direction of the magnetic filed. We show that in this generic scenario, the microengine can be stalled and even reversed by the magnetic field. Finally, we highlight a property of the magneto-gyrator that has no counterpart in the overdamped approximation--the heat loss from the hot to cold bath requires explicit knowledge of the mass of the particle. Consequently, the efficiency of the microengine is mass-dependent even in the overdamped limit.