Miracle-dz/UniFAR-FTUs huggingface dataset:
Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation

Neural Machine Translation(NMT) models are usually trained via unidirectional decoder which corresponds to optimizing one-step-ahead prediction. However, this kind of unidirectional decoding framework may incline to focus on local structure rather than global coherence. To alleviate this problem, we propose a novel method, Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation(SBD-NMT). We deploy a backward decoder which can act as an effective regularization method to the forward decoder. By leveraging the backward decoder's information about the longer-term future, distilling knowledge learned in the backward decoder can encourage auto-regressive NMT models to plan ahead. Experiments show that our method is significantly better than the strong Transformer baselines on multiple machine translation data sets.
Adaptive Relaying for Streaming Erasure Codes in a Three Node Relay Network

This paper investigates adaptive streaming codes over a three-node relayed network. In this setting, a source node transmits a sequence of message packets to a destination with help of a relay. The source-to-relay and relay-to-destination links are unreliable and introduce at most <inline-formula> <tex-math notation="LaTeX">$N_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{2}$ </tex-math></inline-formula> packet erasures, respectively. The destination node must recover each message packet within a strict delay constraint <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>. The paper presents a new construction of streaming codes for all feasible parameters <inline-formula> <tex-math notation="LaTeX">$\{N_{1}, N_{2}, T\}$ </tex-math></inline-formula>. Our work improves upon the construction in Fong et al. by adapting the relaying strategy based on the erasure patterns from source to relay. Specifically, the code employs the notion of symbol estimates, which allows the relay to forward information about symbols before it can decode that symbol, and variable-rate encoding, which decreases the rate used to encode a packet as more erasures affect that packet. The codes proposed in this paper achieve rates higher than the ones proposed by Fong et al. whenever <inline-formula> <tex-math notation="LaTeX">$N_{2} > N_{1}$ </tex-math></inline-formula>, and achieve the same rate when <inline-formula> <tex-math notation="LaTeX">$N_{2} \leq N_{1}$ </tex-math></inline-formula>, in which case the rate is optimal. The paper also presents an upper bound on the achievable rate that takes into account erasures in both links in order to bound the rate in the second link. The upper bound is shown to be tighter than a trivial bound that considers only the erasures in the second link.
Experiments on Thrust Measurement of Multi-Cycle Pulse Detonation Engine

In order to measure directly the thrust of multi-cycle pulse detonation engine,the rig of drag and net thrust measurement was established.With a detonation tube 114mm in inner diameter and 1100mm in length,the characteristics of overall pressure recovery and flow drag loss of pulse detonation engine and the main parts was investigated with filling air.The drag of overflow of pulse detonation engine and the accessory was devolved to the rig and the influence of overflow on thrust was eliminated.Much detonation experimentation was carried out and the kerosene/air aero-valve pulse detonation engine could be operated at 40Hz in frequency.The net thrust of pulse detonation engine was measured with high frequency dynamic thrust sensor and the variety course was obtained.This investigation is of importance to establish basics for investigating thoroughly the propulsion performance of pulse detonation engine.
Context is Everything: Implicit Identification for Dynamics Adaptation

Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be precisely measured or inferred, even during training. We propose Implicit Identification for Dynamics Adaptation (IIDA), a simple method to allow predictive models to adapt to changing environment dynamics. IIDA assumes no access to the true variations in the world and instead implicitly infers properties of the environment from a small amount of contextual data. We demonstrate IIDA's ability to perform well in unseen environments through a suite of simulated experiments on MuJoCo environments and a real robot dynamic sliding task. In general, IIDA significantly reduces model error and results in higher task performance over commonly used methods. Our code, video of the method, and latest paper is available here https://bennevans.github. io/icra-iida/
Iitaka fibrations for dlt pairs polarized by a nef and log big divisor

Abstract We study lc pairs polarized by a nef and log big divisor. After proving the minimal model theory for projective lc pairs polarized by a nef and log big divisor, we prove the effectivity of the Iitaka fibrations and some boundedness results for dlt pairs polarized by a nef and log big divisor.
Assessing Phenotype Definitions for Algorithmic Fairness

Phenotyping is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical to ensure that cohorts are representative of all patients, independently of their demographics or social determinants of health. In this paper, we propose a set of best practices to assess the fairness of phenotype definitions. We leverage established fairness metrics commonly used in predictive models and relate them to commonly used epidemiological metrics. We describe an empirical study for Crohn's disease and diabetes type 2, each with multiple phenotype definitions taken from the literature across gender and race. We show that the different phenotype definitions exhibit widely varying and disparate performance according to the different fairness metrics and subgroups. We hope that the proposed best practices can help in constructing fair and inclusive phenotype definitions.
Critical structure and emergent symmetry of Dirac fermion systems

Emergent symmetry in Dirac system means that the system acquires an enlargement of two basic symmetries at some special critical point. The continuous quantum criticality between the two symmetry broken phases can be described within the framework of Gross–Neveu–Yukawa (GNY) model. Using the first-order ε expansion in 4−ϵ dimensions, we study the critical structure and emergent symmetry of the O(N)-GNY model with N f flavors of four-component Dirac fermions coupled strongly to an O(N) scalar field under a small O(N)-symmetry breaking perturbation. After determining the stable fixed point, we calculate the inverse correlation length exponent and the anomalous dimensions (bosonic and fermionic) for general N and N f . Further, we discuss the emergent-symmetry and the emergent supersymmetric critical point for N⩾4 on the basis of O(N)-GNY model. It turns out that the O(N)-GNY universality class is physically meaningful if and only if N<2Nf+4 . On this premise, the small O(N)-symmetry breaking perturbation is always irrelevant in the O(N)-GNY universality class. Our studies show that the emergent symmetry in Dirac systems has an upper limit O(2Nf+3) , depending on the flavor numbers N f . As a result, the emergent-O(4) and O(5) symmetries are possible to be found in Dirac systems with fermion flavor Nf=1 , and the emergent-O(4), O(5), O(6) and O(7) symmetries are expected to be found in the systems with fermion flavor Nf=2 . Our result suggests some richer transitions with emergent- Z2×O(2)×O(3) symmetry, and so on. Interestingly, in the O(4)-GNY universality class, we find that there is a new supersymmetric critical point which is expected to be found in Dirac systems with fermion flavor Nf=1 .
Stability of structure-aware Taylor methods for tents

Structure-aware Taylor (SAT) methods are a class of timestepping schemes designed for propagating linear hyperbolic solutions within a tent-shaped spacetime region. Tents are useful to design explicit time marching schemes on unstructured advancing fronts with built-in locally variable timestepping for arbitrary spatial and temporal discretization orders. The main result of this paper is that an $s$-stage SAT timestepping within a tent is weakly stable under the time step constraint $\Delta t \leq Ch^{1+1/s}$, where $\Delta t$ is the time step size and $h$ is the spatial mesh size. Improved stability properties are also presented for high order SAT time discretizations coupled with low order spatial polynomials. A numerical verification of the sharpness of proven estimates is also included.
Optical Flow Training under Limited Label Budget via Active Learning

Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against annotation cost. We use a simple yet effective semi-supervised training method to show that even a small fraction of labels can improve flow accuracy by a significant margin over unsupervised training. In addition, we propose active learning methods based on simple heuristics to further reduce the number of labels required to achieve the same target accuracy. Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel. We also analyze and show insights on the factors that may influence active learning performance. Code is available at https://github.com/duke-vision/optical-flow-active-learning-release.
Nonlinear bosonization of Fermi surfaces: The method of coadjoint orbits

Luca V. Delacrétaz,1, 2 Yi-Hsien Du,1 Umang Mehta,1, 3 and Dam Thanh Son1, 2, 4 1Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, Illinois 60637, USA 2Enrico Fermi Institute, University of Chicago, Chicago, Illinois 60637, USA 3Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California 93106, USA 4James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA (Dated: August 26, 2022)
Evaluation of Performance for Human In-Vivo Conductivity Estimation from EEG and sEEG Recorded in Simultaneous with Intracerebral Electrical Stimulation

Assigning accurate conductivity values in human head models is an essential factor for performing precise electroencephalographic (EEG) source localization and targeting of transcranial electrical stimulation (TES). Unfortunately, the literature reports diverging conductivity values of the different tissues in the human head. The current study analyzes first the performance of in-vivo conductivity estimation for different configurations concerning the localization of the electrical source and measurement. Then, it presents conductivity estimates for three epileptic patients using scalp EEG and intracerebral stereotactic EEG (sEEG) acquired in simultaneous with intracerebral electrical stimulation. The estimates of the conductivities were based on finite-element models of the human head with five tissue compartments of homogeneous and isotropic conductivities. The results of this study show that in-vivo conductivity estimation can lead to different estimated conductivities for the same patient when considering different stimulation positions, different measurement positions or different measurement modalities (sEEG or EEG). This work provides important guidelines to in-vivo conductivity estimation and explains the variability among the 1 ar X iv :2 20 3. 05 19 1v 1 [ qbi o. N C ] 1 0 M ar 2 02 2 conductivity values which have been reported in previous studies.
Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems

Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to incrementally increase their knowledge base. Therefore, in this article, the critical challenges in power forecasting and anomaly detection in the context of renewable energy systems are identified, and a potential TL framework to meet these challenges is proposed. This article also proposes a feature embedding approach to handle the missing sensors data. The proposed TL methods help to make a system more autonomous in the context of organic computing.
Robust Tipless Positioning Device for Near-Field Investigations: Press and Roll Scan (PROscan)

Scanning probe microscopes scan and manipulate a sharp tip in the immediate vicinity of a sample surface. The limited bandwidth of the feedback mechanism used for stabilizing the separation between the tip and the sample makes the fragile nanoscopic tip very susceptible to mechanical instabilities. We propose, demonstrate, and characterize an alternative device based on bulging a thin substrate against a second substrate and rolling them with respect to each other. We showcase the power of this method by placing gold nanoparticles and semiconductor quantum dots on the two opposite substrates and positioning them with nanometer precision to enhance the fluorescence intensity and emission rate. Furthermore, we exhibit the passive mechanical stability of the system over more than 1 h. Our design concept finds applications in a variety of other scientific and technological contexts, where nanoscopic features have to be positioned and kept near contact with each other.
Extreme mass-ratio inspirals as probes of scalar fields: Eccentric equatorial orbits around Kerr black holes

We study binary systems in which a stellar mass compact object spirals into a massive black hole, known as extreme mass ratio inspirals, in scenarios with a new fundamental scalar field. Earlier work has shown that, in most interesting such scenarios and to leading order in the mass ratio, the massive black holes can be adequately approximated by the Kerr metric and the imprint of the scalar field on the waveform is fully controlled by the scalar charge of the stellar mass object. Here we use this drastic simplification in the inspiral modelling and consider eccentric equatorial orbits. We study how the scalar charge affects the orbital evolution for different eccentricities and different values of the black hole spin. We then determine how changes in the orbital evolution get imprinted on the waveform and assess LISA's capability to detect or constrain the scalar charge.
A Look into the Problem of Preferential Sampling through the Lens of Survey Statistics

Abstract An evolving problem in the field of spatial and ecological statistics is that of preferential sampling, where biases may be present due to a relationship between sample data locations and a response of interest. This field of research bears a striking resemblance to the longstanding problem of informative sampling within survey methodology, although with some important distinctions. With the goal of promoting collaborative effort within and between these two problem domains, we make comparisons and contrasts between the two problem statements. Specifically, we review many of the solutions available to address each of these problems, noting the important differences in modeling techniques. Additionally, we construct a series of simulation studies to examine some of the methods available for preferential sampling, as well as a comparison analyzing heavy metal biomonitoring data.
Two-stream Hierarchical Similarity Reasoning for Image-text Matching

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods.
The Transitive Information Theory and its Application to Deep Generative Models

Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regularization coefficient for disentangling representation but ultimately generating blurry examples. Existing methods narrow the issues to the rate-distortion trade-off between compression and reconstruction. We argue that a good reconstruction model does learn high capacity latents that encode more details, however, its use is hindered by two major issues: the prior is random noise which is completely detached from the posterior and allow no controllability in the generation; mean-field variational inference doesn't enforce hierarchy structure which makes the task of recombining those units into plausible novel output infeasible. As a result, we develop a system that learns a hierarchy of disentangled representation together with a mechanism for recombining the learned representation for generalization. This is achieved by introducing a minimal amount of inductive bias to learn controllable prior for the VAE. The idea is supported by here developed transitive information theory, that is, the mutual information between two target variables could alternately be maximized through the mutual information to the third variable, thus bypassing the rate-distortion bottleneck in VAE design. In particular, we show that our model, named SemafoVAE (inspired by the similar concept in computer science), could generate high-quality examples in a controllable manner, perform smooth traversals of the disentangled factors and intervention at a different level of representation hierarchy.
The Pattern is in the Details: An Evaluation of Interaction Techniques for Locating, Searching, and Contextualizing Details in Multivariate Matrix Visualizations

Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations’ utility when exploring multivariate data, where each cell represents a data point with multiple values (referred to as details). Three well-established interaction approaches can be applicable in multivariate matrix visualizations (or MMV): focus+context, pan&zoom, and overview+detail. However, there is little empirical knowledge of how these approaches compare in exploring MMV. We report on two studies comparing them for locating, searching, and contextualizing details in MMV. We first compared four focus+context techniques and found that the fisheye lens overall outperformed the others. We then compared the fisheye lens, to pan&zoom and overview+detail. We found that pan&zoom was faster in locating and searching details, and as good as overview+detail in contextualizing details.
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach

Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural language sentence that indicates complex human activities in an untrimmed video, has drawn widespread attention over the past few years. However, recent studies have found that current benchmark datasets may have obvious moment annotation biases, enabling several simple baselines even without training to achieve state-of-the-art (SOTA) performance. In this paper, we take a closer look at existing evaluation protocols for TSGV, and find that both the prevailing dataset splits and evaluation metrics are the devils that lead to untrustworthy benchmarking. Therefore, we propose to re-organize the two widely-used datasets, making the ground-truth moment distributions different in the training and test splits, i.e., out-of-distribution (OOD) test. Meanwhile, we introduce a new evaluation metric “dR@n,IoU=m” that discounts the basic recall scores especially with small IoU thresholds, so as to alleviate the inflating evaluation caused by biased datasets with a large proportion of long ground-truth moments. New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress in TSGV. Furthermore, we propose a novel causality-based Multi-branch Deconfounding Debiasing (MDD) framework for unbiased moment prediction. Specifically, we design a multi-branch deconfounder to eliminate the effects caused by multiple confounders with causal intervention. In order to help the model better align the semantics between sentence queries and video moments, we enhance the representations during feature encoding. Specifically, for textual information, the query is parsed into several verb-centered phrases to obtain a more fine-grained textual feature. For visual information, the positional information has been decomposed from the moment features to enhance the representations of moments with diverse locations. Extensive experiments demonstrate that our proposed approach can achieve competitive results among existing SOTA approaches and outperform the base model with great gains.
Designing ML-resilient locking at register-transfer level

Various logic-locking schemes have been proposed to protect hardware from intellectual property piracy and malicious design modifications. Since traditional locking techniques are applied on the gate-level netlist after logic synthesis, they have no semantic knowledge of the design function. Data-driven, machine-learning (ML) attacks can uncover the design flaws within gate-level locking. Recent proposals on register-transfer level (RTL) locking have access to semantic hardware information. We investigate the resilience of ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the lessons learned to derive two ML-resilient RTL locking schemes built to reinforce ASSURE locking. We developed ML-driven security metrics to evaluate the schemes against an RTL adaptation of the state-of-the-art, ML-based SnapShot attack.