Miracle-dz/UniFAR-FTUs huggingface dataset:
TESS Timings of 31 Hot Jupiters with Ephemeris Uncertainties

A precise transit ephemeris serves as the premise for follow-up exoplanet observations. We compare TESS Object of Interest (TOI) transit timings of 262 hot Jupiters with the archival ephemeris and find 31 of them having TOI timing offsets, among which WASP-161b shows the most significant offset of −203.7 ± 4.1 minutes. The median value of these offsets is 17.8 minutes, equivalent to 3. 6σ. We generate TESS timings in each sector for these 31 hot Jupiters, using a self-generated pipeline. The pipeline performs photometric measurements to TESS images and produces transit timings by fitting the light curves. We refine and update the previous ephemeris, based on these TESS timings (uncertainty ∼1 minute) and a long timing baseline (∼10 yr). Our refined ephemeris gives the transit timing at a median precision of 0.82 minutes until 2025 and 1.21 minutes until 2030. We regard the timing offsets to mainly originate from the underestimated ephemeris uncertainty. All the targets with timing offset larger than 10σ present earlier timings than the prediction, which cannot be due to underestimated ephemeris uncertainty, apsidal precision, or Rømer effect as those effects should be unsigned. For some particular targets, timing offsets are likely due to tidal dissipation. Our sample leads to the detection of period-decaying candidates of WASP-161b and XO-3b reported previously.
A Comparative Study of Adversarial Attacks against Point Cloud Semantic Segmentation

—Recent research efforts on 3D point cloud semantic segmentation (PCSS) have achieved outstanding performance by adopting deep CNN (convolutional neural networks) and GCN (graph convolutional networks). However, the robustness of these complex models has not been systematically analyzed. Given that PCSS has been applied in many safety-critical applications (e.g., autonomous driving, geological sensing), it is important to ﬁll this knowledge gap, in particular, how these models are affected under adversarial samples. While adversarial attacks against point clouds have been studied, we found many questions remain about the robustness of PCSS. For instance, all the prior attacks perturb the point coordinates of a point cloud, but the features associated with a point are also leveraged by some PCSS models, and whether they are good targets to attack is unknown yet. We present a comparative study of PCSS robustness in this work. In particular, we formally deﬁne the attacker’s objective under targeted attack and non-targeted attack and develop new attacks considering a variety of options, including feature-based and coordinate-based, norm-bounded and norm-unbounded, etc. We conduct evaluations with different combinations of attack options on two datasets (S3DIS and Semantic3D) and three PCSS models (PointNet++, DeepGCNs, and RandLA-Net). We found all of the PCSS models are vulnerable under both targeted and non-targeted attacks, and attacks against point features like color are more effective. With this study, we call the attention of the research community to develop new approaches to harden PCSS models against adversarial attacks.
Study of internal structures of 9,10Be and 10B in scattering of 4He from 9Be

A study of inelastic scattering and single-particle transfer reactions was performed by an alpha beam at 63 MeV on a 9$Be target. Angular distributions of the differential cross sections for the 9Be(4He,4He')9Be*, 9Be(4He,3He)10Be and 9Be(4He,t)10B reactions were measured. Experimental angular distributions of the differential cross sections for the ground state and a few low-lying states were analyzed in the framework of the optical model, coupled channels and distorted-wave Born approximation. An analysis of the obtained spectroscopic factors was performed.
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H\&N tumors from medical scans.
Wave function matching for the quantum many-body problem

We introduce a new approach for solving quantum many-body systems called wave function matching. Wave function matching transforms the interaction between particles so that the wave functions at short distances match that of an easily computable interaction. This allows for calculations of systems that would otherwise be impossible due to problems such as Monte Carlo sign cancellations. We apply the method to lattice Monte Carlo simulations of light nuclei, medium-mass nuclei, neutron matter, and nuclear matter. We use interactions at next-to-next-to-next-to-leading order in the framework of chiral effective field theory and find good agreement with empirical data. These results are accompanied by new insights on the nuclear interactions that may help to resolve long-standing challenges in accurately reproducing nuclear binding energies, charge radii, and nuclear matter saturation in ab initio calculations.
Teacher-Student Architecture for Knowledge Learning: A Survey

Although Deep Neural Networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs with voluminous parameters are hard to be deployed in a real-time system. To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance to deep teacher networks. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge learning objectives, including knowledge distillation, knowledge expansion, knowledge adaption, and multi-task learning. With the help of Teacher-Student architectures, current studies are able to achieve multiple knowledge-learning objectives through lightweight and effective student networks. Different from the existing knowledge distillation surveys, this survey detailedly discusses Teacher-Student architectures with multiple knowledge learning objectives. In addition, we systematically introduce the knowledge construction and optimization process during the knowledge learning and then analyze various Teacher-Student architectures and effective learning schemes that have been leveraged to learn representative and robust knowledge. This paper also summarizes the latest applications of Teacher-Student architectures based on different purposes (i.e., classification, recognition, and generation). Finally, the potential research directions of knowledge learning are investigated on the Teacher-Student architecture design, the quality of knowledge, and the theoretical studies of regression-based learning, respectively. With this comprehensive survey, both industry practitioners and the academic community can learn insightful guidelines about Teacher-Student architectures on multiple knowledge learning objectives.
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the substitution between two close subtokens has a similar effect with the character modification. Our method mainly contains three steps. First, a gradient-based method is adopted to find the most vulnerable words in the sentence. Then we split the selected words into subtokens to replace the origin tokenization result from the transformer tokenizer. Finally, we utilize an adversarial loss to guide the substitution of attachable subtokens in which the Gumbel-softmax trick is introduced to ensure gradient propagation.Meanwhile, we introduce the visual and length constraint in the optimization process to achieve minimum character modifications.Extensive experiments on both sentence-level and token-level tasks demonstrate that our method could outperform the previous attack methods in terms of success rate and edit distance. Furthermore, human evaluation verifies our adversarial examples could preserve their origin labels.
A Faster Approach to Spiking Deep Convolutional Neural Networks

Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interesting. Recently, several deep convolutional spiking neural networks have been proposed. These networks aim to increase biological plausibility while creating powerful tools to be applied to machine learning tasks. Here, we suggest a network structure based on previous work to improve network runtime and accuracy. Improvements to the network include reducing training iterations to only once, effectively using principal component analysis (PCA) dimension reduction, weight quantization, timed outputs for classification, and better hyperparameter tuning. Furthermore, the preprocessing step is changed to allow the processing of colored images instead of only black and white to improve accuracy. The proposed structure fractionalizes runtime and introduces an efficient approach to deep convolutional SNNs.
Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology help us to reduce costs and increase the speed and accuracy of regions of interest detection and cancer diagnosis. In this work, we propose a patch-based region of interest detection method for melanocytic skin tumor whole-slide images. We work with a dataset that contains 165 primary melanomas and nevi Hematoxylin and Eosin whole-slide images and build a deep-learning method. The proposed method performs well on a hold-out test data set including ﬁve TCGA-SKCM slides (accuracy of 93.94% in slide classiﬁcation task and intersection over union rate of 41.27% in the region of interest detection task), showing the outstanding performance of our model on melanocytic skin tumor. Even though we test the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems, such as various tumors’ classiﬁcation and prediction, to help and beneﬁt the clinical evaluation and diagnosis of different tumors.
Using Locality-Sensitive Hashing for Rendezvous Search

The multichannel rendezvous problem is a fundamental problem for neighbor discovery in many IoT applications. The existing works in the literature focus mostly on improving the worst-case performance, and the average-case performance is often not as good as that of the random algorithm. As IoT devices (users) are close to each other, their available channel sets, though they might be different, are similar. Using the locality-sensitive hashing (LSH) technique in data mining, we propose channel hopping algorithms that exploit the similarity between the two available channel sets to increase the rendezvous probability. For the synchronous setting, our algorithms have the expected time-to-rendezvous (ETTR) inversely proportional to a well-known similarity measure called the Jaccard index. For the asynchronous setting, we use dimensionality reduction to speed up the rendezvous process. Our numerical results show that our algorithms can outperform the random algorithm in terms of ETTR.
Optimistic and Validity Rollups: Analysis and Comparison between Optimism and StarkNet

The thesis addresses the problem of scalability in decentralized blockchains in the context of the trade-off between transaction throughput and hardware requirements to participate in the network. Rollups are presented, that is technologies to verify on-chain blocks executed off-chain by minimizing the assumptions of trust. The variant of the Optimistic Rollups, in particular of Optimism and the use of invalidity proofs through interactive binary search and of the Validity Rollups, in particular of StarkNet, and the use of validity proofs through STARKs are discussed. Finally, the two solutions are compared on withdrawal time, on the cost of transactions and techniques to minimize it, on the possibility of applying the technology recursively, on compatibility with Ethereum and on the licenses used.
Entanglement used to identify critical systems

Entanglement are the non-local correlations permitted by quantum theory, believed to play a fundamental role in a quantum computer. We have investigated these correlations in a number of theoretical models for condensed matter systems. Such systems are likely candidates for quantum computing, and experimentally feasible for instance as superconducting qubits. At quantum critical points the ground state of these systems is very complicated, and the entanglement is usually larger than at non-critical points. This entanglement can be used to identify the critical points through what we denote the entanglement signature, even for very small systems. From another perspective, it seems that the entanglement is an essential tool to find an unknown ground state, since this gives rise to a simple decomposition of the state.
A Real-Space Perspective on Dephasing in Solid-State High Harmonic Generation

We develop and demonstrate a fully real-space perspective on HHG in crystals. Due to Wannier-Stark localization induced on sub-cycle timescales in the presence of a strong field, real-space descriptions are natural for strongly driven solids. Our approach allows us to address the origin of the extremely short dephasing times, which appear necessary for agreement between experimental HHG measurements and theoretical calculations generally performed in reciprocal space. We develop a physically transparent model of real-space dephasing which relates its rate to the distance between different sites in a laser-driven lattice. Our approach leads to well-structured high harmonic spectra at the microscopic level, reproduces results of macroscopic propagation, and demonstrates that the requirement for ultrafast dephasing times stems from the need for suppressing recombination events with large electron-hole separations during radiative recombination.
IITD at WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection

We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP’2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modelled as a sequence tagging problem. We use a multi-granularity network with mBERT encoder for subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3 participants, respectively). Our experimental results and analysis show that it does not help to use a much larger English corpus annotated with propaganda techniques, regardless of whether used in English or after translation to Arabic.
Searching for Deviations in Trading Systems: Combining Control-Flow and Data Perspectives

Trading systems are software platforms that support the exchange of securities (e.g., company shares) between participants. In this paper, we present a method to search for deviations in trading systems by checking conformance between colored Petri nets and event logs. Colored Petri nets (CPNs) are an extension of Petri nets, a formalism for modeling of distributed systems. CPNs allow us to describe an expected causal ordering between system activities and how data attributes of domain-related objects (e.g., orders to trade) must be transformed. Event logs consist of traces corresponding to runs of a real system. By comparing CPNs and event logs, different types of deviations can be detected. Using this method, we report the validation of a real-life trading system.
Thermodynamics of Coupled Identical Oscillators within the Path Integral Formalism

A generalization of symmetrized density matrices in combination with the technique of generating functions allows to calculate the partition function of identical particles in a parabolic confining well. Harmonic two-body interactions (repulsive or attractive) are taken into account. Also the influence of a homogeneous magnetic field, introducing anisotropy in the model, is examined. Although the theory is developed for fermions and bosons, special attention is payed to the thermodynamic properties of bosons and their condensation.
Resonant spin Hall effect of light in random photonic arrays

It has been recently shown that the coherent component of light propagating in transversally disordered media, the so-called coherent mode, exhibits an optical spin Hall effect (SHE). In non-resonant materials, however, this phenomenon shows up at a spatial scale much larger than the mean free path, making its observation challenging due to the exponential attenuation of the coherent mode. Here, we show that in disordered photonic arrays exhibiting Mie resonances, the SHE on the contrary appears at a scale smaller than the mean free path if one operates in the close vicinity of the lowest transverse-magnetic resonance of the array. In combination with a weak measurement, this gives rise to a giant SHE that should be observable in optically-thin media. Furthermore, we show that by additionally exploiting the cooperative emission of a flash of light following the abrupt extinction of the incoming beam, one can achieve a time-dependent SHE, observable at large optical thickness as well.
VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values

—Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are widely used probabilistic graphical models. While some research has been published on the federated learning of Bayesian networks, publications on Bayesian networks in a vertically partitioned data setting are limited, with important omissions, such as handling missing data. We propose a novel method called VertiBayes to train Bayesian networks (structure and parameters) on vertically partitioned data, which can handle missing values as well as an arbitrary number of parties. For structure learning we adapted the K2 algorithm with a privacy-preserving scalar product protocol. For parameter learning, we use a two-step approach: ﬁrst, we learn an intermediate model using maximum likelihood, treating missing values as a special value, then we train a model on synthetic data generated by the intermediate model using the EM algorithm. The privacy guarantees of VertiBayes are equivalent to those provided by the privacy preserving scalar product protocol used. We experimentally show VertiBayes produces models comparable to those learnt using traditional algorithms. Finally, we propose two alternative approaches to estimate the performance of the model using vertically partitioned data and we show in experiments that these give accurate estimates.
Transferability-based Chain Motion Mapping from Humans to Humanoids for Teleoperation

Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper proposes a transferability-based mapping scheme to allow new robot and human input systems to leverage the mapping of existing trained pairs to form a mapping transfer chain, which will reduce the number of new pair-specific mappings that need to be generated. The first part of the mapping schematic is the development of a Synergy Mapping via DualAutoencoder (SyDa) method. This method uses the latent features from two autoencoders to extract the common synergy of the two agents. Secondly, a transferability metric is created that approximates how well the mapping between a pair of agents will perform compared to another pair before creating the motion mapping models. Thus, it can guide the formation of an optimal mapping chain for the new human-robot pair. Experiments with human subjects and a Pepper robot demonstrated 1) The SyDa method improves the accuracy and generalizability of the pair mappings, 2) the SyDa method allows for bi-directional mapping that does not prioritize the direction of mapping motion, and 3) the transferability metric measures how compatible two agents are for accurate teleoperation. The combination of the SyDa method and transferability metric creates generalizable and accurate mapping need to create the transfer mapping chain.
One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization

Decentralized learning has been studied intensively in recent years motivated by its wide applications in the context of federated learning. The majority of previous research focuses on the offline setting in which the objective function is static. However, the offline setting becomes unrealistic in numerous machine learning applications that witness the change of massive data. In this paper, we propose \emph{decentralized online} algorithm for convex and continuous DR-submodular optimization, two classes of functions that are present in a variety of machine learning problems. Our algorithms achieve performance guarantees comparable to those in the centralized offline setting. Moreover, on average, each participant performs only a \emph{single} gradient computation per time step. Subsequently, we extend our algorithms to the bandit setting. Finally, we illustrate the competitive performance of our algorithms in real-world experiments.
An Approach for Noisy, Crowdsourced Datasets Utilizing Ensemble Modeling, 'Human Softmax' Distributions, and Entropic Measures of Uncertainty

Noisy, crowdsourced image datasets prove challenging, even for the best neural networks. Two issues which complicate classiﬁcation on such datasets are class imbalance and ground-truth uncertainty in labeling. The AL-ALL and AL-PUB datasets—consisting of tightly cropped, individual characters from images of ancient Greek papyri—are strongly affected by both issues. The application of ensemble modeling to such a dataset can help identify images where the ground-truth is questionable and quantify the trustworthiness of those samples. We apply stacked generalization consisting of nearly identical ResNets: one utilizing cross-entropy (CXE) and the other Kullback-Liebler Divergence (KLD). The CXE network uses standard labeling drawn from the crowdsourced consensus. In contrast, the KLD network uses probabilistic labeling for each image derived from the distribution of crowdsourced annotations. We refer to this labeling as the Human Softmax (HSM) distribution. For our ensemble model, we apply a k -nearest neighbors model to the outputs of the CXE and KLD networks. Individually, the ResNet models have approximately 93% accuracy, while the ensemble model achieves an accuracy of > 95%. We also perform an analysis of the Shannon entropy of the various models’ output distributions to measure classiﬁcation uncertainty.