Miracle-dz/UniFAR-FTUs huggingface dataset:
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction

Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose BiPed, a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multi-modal data. Our method benefits from 1) a bifold encoding approach where different data modalities are processed in-dependently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations. Using public pedestrian behavior benchmark datasets for driving, PIE and JAAD, we highlight the benefits of the proposed method for behavior prediction and show that our model achieves state-of-the-art performance and improves trajectory and action prediction by up to 22% and 9% respectively. We further investigate the contributions of the proposed reasoning techniques via extensive ablation studies.
Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields

A multivariate distribution can be described by a triangular transport map from the target distribution to a simple reference distribution. We propose Bayesian nonparametric inference on the transport map by modeling its components using Gaussian processes. This enables regularization and uncertainty quantification of the map estimation, while still resulting in a closed-form and invertible posterior map. We then focus on inferring the distribution of a nonstationary spatial field from a small number of replicates. We develop specific transport-map priors that are highly flexible and are motivated by the behavior of a large class of stochastic processes. Our approach is scalable to high-dimensional distributions due to data-dependent sparsity and parallel computations. We also discuss extensions, including Dirichlet process mixtures for flexible marginals. We present numerical results to demonstrate the accuracy, scalability, and usefulness of our methods, including statistical emulation of non-Gaussian climate-model output.
Spectral and Timing Analysis of NuSTAR and Swift/XRT Observations of the X-Ray Transient MAXI J0637–430

We present results for the first observed outburst from the transient X-ray binary source MAXI J0637–430. This study is based on eight observations from the Nuclear Spectroscopic Telescope Array (NuSTAR) and six observations from the Neil Gehrels Swift Observatory X-Ray Telescope (Swift/XRT) collected from 2019 November 19 to 2020 April 26 as the 3–79 keV source flux declined from 8.2 × 10−10 to 1.4 × 10−12 erg cm−2 s−1. We see the source transition from a soft state with a strong disk-blackbody component to a hard state dominated by a power-law or thermal Comptonization component. NuSTAR provides the first reported coverage of MAXI J0637–430 above 10 keV, and these broadband spectra show that a two-component model does not provide an adequate description of the soft-state spectrum. As such, we test whether blackbody emission from the plunging region could explain the excess emission. As an alternative, we test a reflection model that includes a physical Comptonization continuum. Finally, we also test a spectral component based on reflection of a blackbody illumination spectrum, which can be interpreted as a simple approximation to the reflection produced by returning disk radiation due to the bending of light by the strong gravity of the black hole. We discuss the physical implications of each scenario and demonstrate the value of constraining the source distance.
Quantum induced broadening: A challenge for cosmic neutrino background discovery

A recent preprint by Cheipesh et al. pointed out that the zero-point motion of Tritium atoms bound to Graphene may blur the measured energies of β electrons. Smearing due to zero point motion is well known. Such an effect features in studies of the β spectrum expected in experiments like KATRIN using diatomic Tritium. The recent preprint may, however, challenge new planned experiments seeking to discover the Cosmic Neutrino Background (CNB) neutrinos (and/or other neutrinos of masses smaller than 0.1 eV) which plan to use Tritium adsorbed onto Graphene or other materials. Our paper clarifies these issues and examines the more general problem of smearing induced by quantum uncertainty. We find that the effect of Cheipesh et al. is reduced considerably. The importance of the chemical evolution of the H atom hosting the Tritium nucleus into a tightly bound neutral He atom is emphasized. We estimate the excess blurring caused by the dense spectrum near the lowest state of the Graphene or other hosts of the Tritium atom, generated by the electronic response to the “sudden” escape of the β electron. Our analysis suggests yet larger effects and difficulties facing many experiments searching for small mass neutrinos. We speculate on a possible experimental setup which could minimize quantum broadening.
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval

In this paper, we present our approaches for the case law retrieval and the legal case entailment task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2021. As first stage retrieval methods combined with neural re-ranking methods using contextualized language models like BERT achieved great performance improvements for information retrieval in the web and news domain, we evaluate these methods for the legal domain. A distinct characteristic of legal case retrieval is that the query case and case description in the corpus tend to be long documents and therefore exceed the input length of BERT. We address this challenge by combining lexical and dense retrieval methods on the paragraph-level of the cases for the first stage retrieval. Here we demonstrate that the retrieval on the paragraph-level outperforms the retrieval on the document-level. Furthermore the experiments suggest that dense retrieval methods outperform lexical retrieval. For re-ranking we address the problem of long documents by summarizing the cases and fine-tuning a BERT-based re-ranker with the summaries. Overall, our best results were obtained with a combination of BM25 and dense passage retrieval using domain-specific embeddings.
Asymptotic expansions about infinity for solutions of nonlinear differential equations with coherently decaying forcing functions

This paper studies, in fine details, the long-time asymptotic behavior of decaying solutions of a general class of dissipative systems of nonlinear differential equations in complex Euclidean spaces. The forcing functions decay, as time tends to infinity, in a coherent way expressed by combinations of the exponential, power, logarithmic and iterated logarithmic functions. The decay may contain sinusoidal oscillations not only in time but also in the logarithm and iterated logarithm of time. It is proved that the decaying solutions admit corresponding asymptotic expansions, which can be constructed concretely. In the case of the real Euclidean spaces, the real-valued decaying solutions are proved to admit real-valued asymptotic expansions. Our results unite and extend the theory investigated in many previous works.
Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data

We present a novel approach to joint depth and normal estimation for time-of-flight (ToF) sensors. Our model learns to predict the high-quality depth and normal maps jointly from ToF raw sensor data. To achieve this, we meticulously constructed the first large-scale dataset (named ToF-100) with paired raw ToF data and ground-truth high-resolution depth maps provided by an industrial depth camera. In addition, we also design a simple but effective framework for joint depth and normal estimation, applying a robust Chamfer loss via jittering to improve the performance of our model. Our experiments demonstrate that our proposed method can efficiently reconstruct high-resolution depth and normal maps and significantly outperforms state-of-the-art approaches.
Transverse mode selective laser with gain regulation by a digital micromirror device

A transverse mode selective laser system with gain regulation by a digital micromirror device (DMD) is presented in this letter. The gain regulation in laser medium is adjusted by the switch of the patterns loaded on DMD. Structured pump beam patterns can be obtained after the reflection of the loaded patterns on DMD, and then it’s defocused into a microchip laser medium by a short focal lens, so that the pump patterns can be transferred to the gain medium to regulate the gain distribution. Corresponding structured laser beams can be generated by this laser system. The laser beam pattern can be regulated easily and quickly, by switching the loaded patterns on DMD. Through this method, we show a simple and flexible laser system to generate on-demand laser beam patterns.
Machine Translation of Low-Resource Indo-European Languages

In this work, we investigate methods for the challenging task of translating between low- resource language pairs that exhibit some level of similarity. In particular, we consider the utility of transfer learning for translating between several Indo-European low-resource languages from the Germanic and Romance language families. In particular, we build two main classes of transfer-based systems to study how relatedness can benefit the translation performance. The primary system fine-tunes a model pre-trained on a related language pair and the contrastive system fine-tunes one pre-trained on an unrelated language pair. Our experiments show that although relatedness is not necessary for transfer learning to work, it does benefit model performance.
Tackling Consistency-related Design Challenges of Distributed Data-Intensive Systems: An Action Research Study

Background: Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue. Aims: In this paper, we report on our study of the effectiveness and applicability of the novel design guidelines we are proposing in this regard. Method: We used action research and conducted it in the context of the software architecture design process of a multi-site platform development project. Results: Our hypotheses regarding effectiveness and applicability have been accepted in the context of the study. The initial design guidelines were refined throughout the study. Thus, we also contribute concrete guidelines for architecting distributed data-intensive systems with eventually consistent data. The guidelines are an advancement of Domain-Driven Design and provide additional patterns for the tactical design part. Conclusions: Based on our results, we recommend using the guidelines to architect safe eventually consistent systems. Because of the relevance of distributed data-intensive systems, we will drive this research forward and evaluate it in further domains.
Co-learning: Learning from Noisy Labels with Self-supervision

Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance. Self-supervised learning works in the absence of labels and thus eliminates the negative impact of noisy labels. Motivated by co-training with both supervised learning view and self-supervised learning view, we propose a simple yet effective method called Co-learning for learning with noisy labels. Co-learning performs supervised learning and self-supervised learning in a cooperative way. The constraints of intrinsic similarity with the self-supervised module and the structural similarity with the noisily-supervised module are imposed on a shared common feature encoder to regularize the network to maximize the agreement between the two constraints. Co-learning is compared with peer methods on corrupted data from benchmark datasets fairly, and extensive results are provided which demonstrate that Co-learning is superior to many state-of-the-art approaches.
Rigidity of the round cylinders in Ricci shrinkers

In this paper, we prove that the round cylinders are rigid in the space of Ricci shrinkers. Namely, any Ricci shrinker that is sufficiently close to $S^{n-1}\times \mathbb R$ in the pointed-Gromov-Hausdorff topology must itself be isometric to $S^{n-1}\times \mathbb R$.
A forecast of using fast radio burst observations to constrain holographic dark energy

Recently, about five hundred fast radio bursts (FRBs) detected by CHIME/FRB Project have been reported. The vast amounts of data would make FRBs a promising low-redshift cosmological probe in the forthcoming years, and thus the issue of how many FRBs are needed for precise cosmological parameter estimation in different dark energy models should be detailedly investigated. Different from the usually considered w(z)-parameterized models in the literature, in this work we investigate the holographic dark energy (HDE) model and the Ricci dark energy (RDE) model, which originate from the holographic principle of quantum gravity, using the simulated localized FRB data as a cosmological probe for the first time. We show that the Hubble constant H 0 can be constrained to about 2% precision in the HDE model with the Macquart relation of FRB by using 10000 accurately-localized FRBs combined with the current CMB data, which is similar to the precision of the SH0ES value. Using 10000 localized FRBs combined with the CMB data can achieve about 6% constraint on the dark-energy parameter c in the HDE model, which is tighter than the current BAO data combined with CMB. We also study the combination of the FRB data and another low-redshift cosmological probe, i.e. gravitational wave (GW) standard siren data, with the purpose of measuring cosmological parameters independent of CMB. Although the parameter degeneracies inherent in FRB and in GW are rather different, we find that more than 10000 FRBs are demanded to effectively improve the constraints in the holographic dark energy models.
Factorization in hard exclusive processes. Computation of one-loop corrections to the diphoton photoproduction on proton

Generalized Parton Distributions (GPDs) carry information on the internal structure of hadrons such as the angular momentum of quarks and gluons, or their spacelike distribution. They can be experimentally studied in exclusive experiments with hadrons, i.e. processes in which all initial and final states are measured. The tool that creates the necessary bridge between the theoretical predictions and the experiments is the collinear factorization. It allows disentangling perturbatively computable parts of the amplitude, which describes interactions of quarks and gluons with the external particles, from the non-perturbative quantities, which are identified as GPDs. In this work, I extend the theoretical analysis of the process photoproduction of photon pairs on a proton to the next-to-leading order in perturbative Quantum Chromodynamics within the framework of collinear factorization. I give the proof that all collinear divergences which arise in one-loop computations cancel at the level of the amplitude. This result enlarges the family of reactions, which can be studied using the collinear factorization by processes of the type $2 \rightarrow 3$, which have not been previously studied within this theoretical framework beyond the leading QCD order. Furthermore, I compute the full form of the amplitude of the discussed process at the one-loop order. That improves the accuracy of theoretical predictions for this experiment, which may be used for planning future experiments in JLAB or EIC.
Weak ergodicity breaking through the lens of quantum entanglement

Recent studies of interacting systems of quantum spins, ultracold atoms and correlated fermions have shed a new light on how isolated many-body systems can avoid rapid equilibration to their thermal state. It has been shown that many such systems can “weakly” break ergodicity: they possess a small number of non-thermalising eigenstates and/or display slow relaxation from certain initial conditions, while majority of other initial states equilibrate fast, like in conventional thermalising systems. In this chapter, we provide a pedagogical introduction to weak ergodicity breaking phenomena, including Hilbert space fragmentation and quantum many-body scars. Central to these developments have been the tools based on quantum entanglement, in particular matrix product states and tangent space techniques, which have allowed to analytically construct non-thermal eigenstates in various non-integrable quantum models, and to explore semiclassical quantisation of such systems in the absence of a large-N or mean field limit. We also discuss recent experimental realisations of weak ergodicity breaking phenomena in systems of Rydberg atoms and tilted optical lattices.
LeafMask: Towards Greater Accuracy on Leaf Segmentation

Leaf segmentation is the most direct and effective way for high-throughput plant phenotype data analysis and quantitative researches of complex traits. Currently, the primary goal of plant phenotyping is to raise the accuracy of the autonomous phenotypic measurement. In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate original masks; 2) the mask refining module elaborating leaf boundaries from the mask assembly module by the point selection strategy and predictor. In addition, we also design a novel and flexible multi-scale attention module for the dual attention-guided mask (DAG-Mask) branch to effectively enhance information expression and produce more accurate bases. Our main contribution is to generate the final improved masks by combining the mask assembly module with the mask refining module under the anchor-free instance segmentation paradigm. We validate our LeafMask through extensive experiments on Leaf Segmentation Challenge (LSC) dataset. Our proposed model achieves the 90.09% BestDice score outperforming other state-of-the-art approaches.
Convergence of the Inexact Online Gradient and Proximal-Gradient Under the Polyak-Łojasiewicz Condition

—This paper focuses on the online gradient and proximal-gradient methods for optimization and learning problems with data streams. The performance of the online gradient descent method is ﬁrst examined in a setting where the cost satisﬁes the Polyak-Łojasiewicz (PL) inequality and inexact gradient information is available. Convergence results show that the instantaneous regret converges linearly up to an error that depends on the variability of the problem and the statistics of the gradient error; in particular, we provide bounds in expectation and in high probability (that hold iteration-wise), with the latter derived by leveraging a sub-Weibull model for the errors affecting the gradient. Similar convergence results are then provided for the online proximal-gradient method, under the assumption that the composite cost satisﬁes the proximal-PL condition. The convergence results are applicable to a number of data processing, learning, and feedback-optimization tasks, where the cost functions may not be strongly convex, but satisﬁes the PL inequity. In the case of static costs, the bounds provide a new characterization of the convergence of gradient and proximal-gradient methods with a sub-Weibull gradient error. Illustrative simulations are provided for a real-time demand response problem in the context of power systems.
Triplet Contrastive Learning for Brain Tumor Classification

Brain tumor is a common and fatal form of cancer which affects both adults and children. The classification of brain tumors into different types is hence a crucial task, as it greatly influences the treatment that physicians will prescribe. In light of this, medical imaging techniques, especially those applying deep convolutional networks followed by a classification layer, have been developed to make possible computer-aided classification of brain tumor types. In this paper, we present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. Along with using triplet loss variants, our approach applies contrastive learning to performing unsupervised pre-training, combined with a rare-case data augmentation module to effectively ameliorate the lack of data problem in the brain tumor imaging analysis domain. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare. With a common encoder during all the experiments, we compare our approach with a baseline classification-layer based model, and the results well prove the effectiveness of our approach across all measured metrics.
A Neural Approach for Detecting Morphological Analogies

Analogical proportions are statements of the form “A is to B as C is to D” that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.
Preparing for performance analysis at exascale

Performance tools for emerging heterogeneous exascale platforms must address two principal challenges when analyzing execution measurements. First, measurement of large-scale executions may record mountains of performance data. Second, performance measurements for parallel programs are sparse in two ways: the set of metrics present for any context and the set of contexts present in different threads. For GPU-accelerated applications, an important source of sparsity is that none of the myriad of GPU metrics apply to any of the many CPU contexts. To address these challenges, we developed a novel streaming aggregation approach to postmortem analysis that employs both shared and distributed memory parallelism to aggregate sparse performance measurements from every rank, thread, and GPU stream of an application, and attributes heterogeneous call path profiles and traces to source code. Using the same amount of resources, our approach analyzes large-scale performance measurements of GPU-accelerated applications over an order of magnitude faster than HPCToolkit and its sparse analysis results are as much as three orders of magnitude smaller than HPC-Toolkit's dense representation of metrics.