Miracle-dz/UniFAR-FTUs huggingface dataset:
A one-armed CNN for exoplanet detection from light curves

W e propose Genesis, a one-armed simplified Convolutional Neural Network (CNN) for exoplanet detection, and compare it to the more complex, two-armed CNN called Astronet. Furthermore, we examine howMonte Carlo cross-validation affects the estimation of the exoplanet detection performance. Finally, we increase the input resolution twofold to assess its effect on performance. The experiments reveal that (i) the reduced complexity of Genesis, i.e., a more than 95% reduction in the number of free parameters, incurs a small performance cost of about 0.5% compared to Astronet, (ii) Monte Carlo crossvalidation provides a more realistic performance estimate that is almost 0.7% below the original estimate, and (iii) the twofold increase in input resolution decreases the average performance by about 0.5%. We conclude by arguing that further exploration of shallower CNN architectures may be beneficial in order to improve the generalizability of CNN-based exoplanet detection across surveys.
Abundance Patterns of α and Neutron-capture Elements in the Helmi Stream

We identified eight additional stars as members of the Helmi stream (HStr) in the combined GALAH+ DR3 and Gaia EDR3 catalog. By consistently reevaluating claimed members from the literature, we consolidate a sample of 22 HStr stars with parameters determined from high-resolution spectroscopy and spanning a considerably wider (by ∼0.5 dex) metallicity interval (− 2.5 ≲ [Fe/H] < − 1.0) than previously reported. Our study focuses on α (Mg and Ca) and neutron-capture (Ba and Eu) elements. We find that the chemistry of HStr is typical of dwarf spheroidal (dSph) galaxies, in good agreement with previous N-body simulations of this merging event. Stars of HStr constitute a clear declining sequence in [α/Fe] for increasing metallicity up to [Fe/H] ∼ −1.0. Moreover, stars of HStr show a median value of +0.5 dex for [Eu/Fe] with a small dispersion (±0.1 dex). Every star analyzed with [Fe/H] < −1.2 belongs to the r-process enhanced ([Eu/Fe] > +0.3 and [Ba/Eu] < 0.0) metal-poor category, providing remarkable evidence that, at such a low-metallicity regime, stars of HStr experienced enrichment in neutron-capture elements predominantly via r-process nucleosynthesis. Finally, the extended metallicity range also suggests an increase in [Ba/Eu] for higher [Fe/H], in conformity with other surviving dwarf satellite galaxies of the Milky Way.
A new stochastic framework for ship capsizing

We present a new stochastic framework for studying ship capsize. It is a synthesis of two strands of transition state theory. The first is an extension of deterministic transition state theory to dissipative non-autonomous systems, together with a probability distribution over the forcing functions. The second is stochastic reachability and large deviation theory for transition paths in Markovian systems. In future work we aim to bring these together to make a tool for predicting capsize rate in different stochastic sea states, suggesting control strategies and improving designs.
Towards ultra-high fidelity quantum operations: SQiSW gate as a native two-qubit gate

Cupjin Huang,1 Dawei Ding,1 Qi Ye,2, 3 Feng Wu,4 Linghang Kong,5 Fang Zhang,1 Xiaotong Ni,4 Yaoyun Shi,1 Hui-Hai Zhao,2 and Jianxin Chen1 Alibaba Quantum Laboratory, Alibaba Group USA, Bellevue, Washington 98004, USA Alibaba Quantum Laboratory, Alibaba Group, Beijing 100102, P.R. China Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People’s Republic of China Alibaba Quantum Laboratory, Alibaba Group, Hangzhou, Zhejiang 311121, P.R. China Center for Theoretical Physics, Massachusetts Institute of Technology,Cambridge, MA 02139, USA (Dated: October 26, 2021)
Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration

We introduce a simple new method for visual imitation learning, which allows a novel robot manipulation task to be learned from a single human demonstration, without requiring any prior knowledge of the object being interacted with. Our method models imitation learning as a state estimation problem, with the state defined as the end-effector’s pose at the point where object interaction begins, as observed from the demonstration. By modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-supervised manner, by automatically moving the end-effector’s camera around the object. At test time, the end-effector is moved to the estimated state through a linear path, at which point the demonstration’s end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory without actually needing to explicitly learn a policy. Real-world experiments on 8 everyday tasks show that our method can learn a diverse range of skills from just a single human demonstration, whilst also yielding a stable and interpretable controller. Videos at: www.robot-learning.uk/coarse-to-fine-imitation-learning.
On quasilinear Maxwell equations in two dimensions

New sharp Strichartz estimates for the Maxwell system in two dimensions with rough permittivity and non-trivial charges are proved. We use the FBI transform to carry out the analysis in phase space. For this purpose, the Maxwell equations are conjugated to a system of half-wave equations with rough coeﬃcients. For this system, Strichartz estimates are proved similarly as in previous work by Tataru on scalar wave equations with rough coeﬃcients. We use the estimates to improve the local well-posedness theory for quasilinear Maxwell equations in two dimensions.
Applying information theory and entropy to eliminate errors in mouse-tracking results

Mouse-tracking of computer system users represents a less expensive, but also a far more applicable alternative to eye-tracking. The main disadvantage of mouse-tracking are errors manifested as discrepancies between the actual eye-gaze position and the mouse cursor position. This paper presents a method for automated correction of errors arising in mouse-tracking. Our approach draws upon information theory and employs Shannon entropy. The method is based on calculating the entropy of a visual representation of a Web page, i.e., we quantify information potential values of various positions. Information obtained, thereby, is paired with cumulative time intervals, spent by the mouse cursor in each position. In this way, we identify cursor positions that do not match eye-gaze positions. To verify the effectiveness of our method, we compared the eye gaze and mouse cursor heat maps in the following ways: We calculated the coefficient of correlation between the two; we computed Euclidean distance between their centers of gravity; and we performed visual comparison.
Five-mode linear optical scheme for Bell state generation

The capability of linear optics to generate entangled states is widely exploited in quantum information processing, however, it is non-trivial to generate qubit states. Here we investigate the linear optical methods for generation of dual-rail-encoded Bell states using computer optimization. We have found a five-mode scheme that produces these states with probability 1/9 with heralding by one photon detector. This result is an improvement in both compactness and success probability compared to the previously known schemes requiring six-mode transformations and two photon detectors and providing a success probability of 2/27. We show that the increase in success probability is due to the elevated order of photon interference implemented by our scheme: four photons interfere in our scheme, while three photons interfere in the known scheme.
IE-GSTC Metasurface Field Solver Using Surface Susceptibility Tensors With Normal Polarizabilities

An integral equation (IE)-based electromagnetic field solver using metasurface susceptibility tensors is proposed and validated using a variety of numerical examples in 2-D. The proposed method solves for fields generated by the metasurface, which is represented as spatial discontinuities satisfying the generalized sheet transition conditions (GSTCs), and described using tensorial surface susceptibility densities, $\bar {\bar {\chi }}$ . For the first time, the complete tensorial representation of susceptibilities is incorporated in this integrated IE-GSTC framework, where the normal surface polarizabilities and their spatial derivatives along the metasurface are rigorously taken into account. The proposed field equation formulation further utilizes a local coordinate system, which enables modeling metasurfaces with arbitrary orientations and geometries. The proposed 2-D boundary element method BEM-GSTC framework is successfully tested using a variety of examples, including infinite and finite-sized metasurfaces, periodic metasurfaces, and complex shaped structures, showing comparisons with both analytical results and a commercial full-wave solver. It is shown that the zero-thickness sheet model with complete tensorial susceptibilities can very accurately reproduce the macroscopic fields, accounting for their angular field scattering response and the edge diffraction effects in finite-sized surfaces.
Near-infrared Supernova Ia Distances: Host Galaxy Extinction and Mass-step Corrections Revisited

We present optical and near-infrared (NIR, Y-, J-, H-band) observations of 42 Type Ia supernovae (SNe Ia) discovered by the untargeted intermediate Palomar Transient Factory survey. This new data set covers a broad range of redshifts and host galaxy stellar masses, compared to previous SN Ia efforts in the NIR. We construct a sample, using also literature data at optical and NIR wavelengths, to examine claimed correlations between the host stellar masses and the Hubble diagram residuals. The SN magnitudes are corrected for host galaxy extinction using either a global total-to-selective extinction ratio, R V = 2.0, for all SNe, or a best-fit R V for each SN individually. Unlike previous studies that were based on a narrower range in host stellar mass, we do not find evidence for a “mass step,” between the color- and stretch-corrected peak J and H magnitudes for galaxies below and above log(M*/M⊙)=10 . However, the mass step remains significant (3σ) at optical wavelengths (g, r, i) when using a global R V , but vanishes when each SN is corrected using their individual best-fit R V . Our study confirms the benefits of the NIR SN Ia distance estimates, as these are largely exempted from the empirical corrections dominating the systematic uncertainties in the optical.
Semantic Table Retrieval using Keyword and Table Queries

Tables on the Web contain a vast amount of knowledge in a structured form. To tap into this valuable resource, we address the problem of table retrieval: answering an information need with a ranked list of tables. We investigate this problem in two different variants, based on how the information need is expressed: as a keyword query or as an existing table ("query-by-table"). The main novel contribution of this work is a semantic table retrieval framework for matching information needs (keyword or table queries) against tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using two purpose-built test collections based on Wikipedia tables, we demonstrate significant and substantial improvements over state-of-the-art baselines.
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network

Contract element extraction (CEE) is the novel task of automatically identifying and extracting legally relevant elements such as contract dates, payments, and legislation references from contracts. Automatic methods for this task view it as a sequence labeling problem and dramatically reduce human labor. However, as contract genres and element types may vary widely, a significant challenge for this sequence labeling task is how to transfer knowledge from one domain to another, i.e., cross-domain CEE. Cross-domain CEE differs from cross-domain named entity recognition (NER) in two important ways. First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors. Second, the extraction zones for cross-domain CEE are much larger than for cross-domain NER. As a result, the contexts of elements from different domains can be more diverse. We propose a framework, the Bi-directional Feedback cLause-Element relaTion network (Bi-FLEET), for the cross-domain CEE task that addresses the above challenges. Bi-FLEET has three main components: (1) a context encoder, (2) a clause-element relation encoder, and (3) an inference layer. To incorporate invariant knowledge about element and clause types, a clause-element graph is constructed across domains and a hierarchical graph neural network is adopted in the clause-element relation encoder. To reduce the influence of context variations, a multi-task framework with a bi-directional feedback scheme is designed in the inference layer, conducting both clause classification and element extraction. The experimental results over both cross-domain NER and CEE tasks show that Bi-FLEET significantly outperforms state-of-the-art baselines.
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.
ICAM-1 depletion in the center of immunological synapses is important for calcium releasing in T-cells

T-cell activation requires the formation of the immunological synapse (IS) between a T-cell and an antigen-presenting cell (APC) to control the development of the adaptive immune response. However, calcium release, an initial signal of T-cell activation, has been found to occur before IS formation. The mechanism for triggering the calcium signaling and relationship between calcium release and IS formation remains unclear. Herein, using live-cell imaging, we found that intercellular adhesion molecule 1 (ICAM-1), an essential molecule for IS formation, accumulated and then was depleted at the center of the synapse before complete IS formation. During the process of ICAM-1 depletion, calcium was released. If ICAM-1 failed to be depleted from the center of the synapse, the sustained calcium signaling could not be induced. Moreover, depletion of ICAM-1 in ISs preferentially occurred with the contact of antigen-specific T-cells and dendritic cells (DCs). Blocking the binding of ICAM-1 and lymphocyte function-as.
Audio Captioning with Composition of Acoustic and Semantic Information

Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder–decoder-based models without considering semantic information. To fill this gap, we present a novel encoder–decoder architecture using bi-directional Gated Recurrent Units (BiGRU) with audio and semantic embeddings. We extract semantic embedding by obtaining subjects and verbs from the audio clip captions and combine these embedding with audio embedding to feed the BiGRU-based encoder–decoder model. To enable semantic embeddings for the test audios, we introduce a Multilayer Perceptron classifier to predict the semantic embeddings of those clips. We also present exhaustive experiments to show the efficiency of different features and datasets for our proposed model the audio captioning task. To extract audio features, we use the log Mel energy features, VGGish embeddings, and a pretrained audio neural network (PANN) embeddings. Extensive experiments on two audio captioning datasets Clotho and AudioCaps show that our proposed model outperforms state-of-the-art audio captioning models across different evaluation metrics and using the semantic information improves the captioning performance.
Electrocardio Panorama: Synthesizing New ECG views with Self-supervision

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening

This work presents a probabilistic channel pruning method to accelerate Convolutional Neural Networks (CNNs). Previous pruning methods often zero out unimportant channels in training in a deterministic manner, which reduces CNN's learning capacity and results in suboptimal performance. To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated. BWCP has several merits. (1) It simultaneously trains and prunes CNNs from scratch in a probabilistic way, exploring larger network space than deterministic methods. (2) BWCP is empowered by the proposed batch whitening tool, which is able to empirically and theoretically increase the activation probability of useful channels while keeping unimportant channels unchanged without adding any extra parameters and computational cost in inference. (3) Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet with various network architectures show that BWCP outperforms its counterparts by achieving better accuracy given limited computational budgets. For example, ResNet50 pruned by BWCP has only 0.70\% Top-1 accuracy drop on ImageNet, while reducing 43.1\% FLOPs of the plain ResNet50.
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition

Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition. However, they are less capable at capturing local information. In this work, we propose a 2D Conv-Attention module which combines convolution neural networks with attention for beamforming. We apply self- and cross-attention to explicitly model the correlations within and between the input channels. The end-to-end 2D Conv-Attention model is compared with a multi-head self-attention and superdirective-based neural beamformers. We train and evaluate on an in-house multi-channel dataset. The results show a relative improvement of 3.8% in WER by the proposed model over the baseline neural beamformer.
Mobility-enhanced MPC for Legged Locomotion on Rough Terrain

—Re-planning in legged locomotion is crucial to track a given set-point while adapting to the terrain and rejecting external disturbances. In this work, we propose a real-time Nonlinear Model Predictive Control (NMPC) tailored to a legged robot for achieving dynamic locomotion on a wide variety of terrains. We introduce a mobility-based criterion to deﬁne an NMPC cost that enhances the locomotion of quadruped robots while maximizing leg mobility and staying far from kinematic limits. Our NMPC is based on the real-time iteration scheme that allows us to re-plan online at 25 Hz with a time horizon of 2 seconds. We use the single rigid body dynamic model deﬁned in the center of mass frame that allows to increase the computational efﬁciency. In simulations, the NMPC is tested to traverse a set of pallets of different sizes, to walk into a V-shaped chimney, and to locomote over rough terrain. We demonstrate the effectiveness of our NMPC with the mobility feature that allowed IIT’s 87 . 4 kg quadruped robot HyQ to achieve an omni-directional walk on ﬂat terrain, to traverse a static pallet, and to adapt to a repositioned pallet during a walk in real experiments.
Fractal Quantum Phase Transitions: Critical Phenomena Beyond Renormalization

We identify a quantum critical point with fractal symmetry whose effective theory eludes the renormalization group framework. We consider the Newman-Moore model with three-body interaction subjected to an external transverse field, which exhibits a Kramers-Wannier type self-duality and a fractal $Z_2$ symmetry with Ising charge conserved on a fractal subset of sites, i.e., on Sierpinski gaskets. Using large-scale quantum Monte Carlo simulations, we identify a continuous quantum phase transition between a phase with spontaneous fractal symmetry breaking and a paramagnetic phase. This phase transition is characterized by the emergence of a fractal scaling dimension $d=\ln(3)/\ln(2)$ at the quantum critical point, where the power-law exponent of the correlation function is related to the fractal dimension of the Sierpinski triangle. We develop a field theory to elucidate such quantum criticality and denote the fractal scaling as a subsequence of UV-IR mixing, where the low energy modes at the critical point are manipulated by short-wavelength physics due to the fractal symmetry.