Miracle-dz/UniFAR-FTUs huggingface dataset:
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance

Human-translated text displays distinct features from naturally written text in the same language. This phenomena, known as translationese, has been argued to confound the machine translation (MT) evaluation. Yet, we find that existing work on translationese neglects some important factors and the conclusions are mostly correlational but not causal. In this work, we collect CausalMT, a dataset where the MT training data are also labeled with the human translation directions. We inspect two critical factors, the train-test direction match (whether the human translation directions in the training and test sets are aligned), and data-model direction match (whether the model learns in the same direction as the human translation direction in the dataset). We show that these two factors have a large causal effect on the MT performance, in addition to the test-model direction mismatch highlighted by existing work on the impact of translationese. In light of our findings, we provide a set of suggestions for MT training and evaluation. Our code and data are at https://github.com/EdisonNi-hku/CausalMT
Rapid Locomotion via Reinforcement Learning

Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m/s. This system runs and turns fast on natural terrains like grass, ice, and gravel and responds robustly to disturbances. Our controller is a neural network trained in simulation via reinforcement learning and transferred to the real world. The two key components are (i) an adaptive curriculum on velocity commands and (ii) an online system identification strategy for sim-to-real transfer. Videos of the robot’s behaviors are available at https://agility.csail. mit.edu/ .
Quasiperiodic Behavior in the γ-Ray Light Curve of the Blazar PKS 0405-385

We analyze the quasiperiodic oscillation (QPO) of the historical light curve of flat-spectrum radio quasars PKS 0405-385 detected by the Fermi Large Area Telescope from 2008 August to 2021 November. To identify and determine the QPO signal of PKS 0405-385 in the γ-ray light curve, we use four time series analysis techniques based on frequency and time domains, i.e., the Lomb–Scargle periodogram (LSP), the weighted wavelet z-transform (WWZ), the REDFIT, and the epoch folding. The results show that PKS 0405-385 has a quasiperiodic behavior of ∼2.8 yr with the significance of ∼4. 3σ in Fermi long-term monitoring. Remarkably, we also performed QPO analysis in the G-band light curve observed from 2014 October to 2021 October using LSP and WWZ technology, and the results (∼4σ of significance) are consistent with the periodic detection in γ-ray. This may imply that the optical emission is radiated by an electron population in the same way as the γ-ray emission. In discussing the possible mechanism of quasiperiodic behavior, either the helical motion within a jet or the supermassive black hole binary system provides a viable explanation for the QPO of 2.8 yr, and the relevant parameters have been estimated.
CE-based white-box adversarial attacks will not work using super-fitting

Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation to the input will make the model obtain wrong results. This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i.e., the accuracy under adversarial attacks). This paper mathematically proves the effectiveness of super-fitting and enables the model to reach this state quickly by minimizing unrelated category scores (MUCS). Theoretically, super-fitting can resist any existing (even future) CE-based white-box adversarial attacks. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting, and the proposed method is compared with nearly 50 defense models from recent conferences. The experimental results show that the super-fitting method in this paper can make the trained model obtain the highest adversarial robustness.
OCR Synthetic Benchmark Dataset for Indic Languages

We present the largest publicly available synthetic OCR benchmark dataset for Indic languages. The collection contains a total of 90k images and their ground truth for 23 Indic languages. OCR model validation in Indic languages require a good amount of diverse data to be processed in order to create a robust and reliable model. Generating such a huge amount of data would be difficult otherwise but with synthetic data, it becomes far easier. It can be of great importance to fields like Computer Vision or Image Processing where once an initial synthetic data is developed, model creation becomes easier. Generating synthetic data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model. Accuracy for labeled real-time data is sometimes quite expensive while accuracy for synthetic data can be easily achieved with a good score.
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation

Multi-view depth estimation plays a critical role in reconstructing and understanding the 3D world. Recent learning-based methods have made significant progress in it. However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence. Thus, they suffer unsatisfactory precision and generalization capability. In this paper, we are the first to explore more general image correlations to establish correspondences dynamically for depth estimation. We design a novel iterative multi-view depth estimation framework mimicking the optimization process, which consists of 1) a correlation volume construction module that models the pixel similarity between a reference image and source images as all-to-all correlations; 2) a flow-based depth initialization module that estimates the depth from the 2D optical flow; 3) a novel correlation-guided depth refinement module that reprojects points in different views to effectively fetch relevant correlations for further fusion and integrate the fused correlation for iterative depth update. Without predefined depth hypotheses, the fused correlations establish multi-view correspondence in an efficient way and guide the depth refinement heuristically. We conduct sufficient experiments on ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method on multi-view depth estimation and its best generalization ability.
Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation

Open-domain conversational systems are assumed to generate equally good responses on multiple domains. Previous work achieved good performance on the single corpus, but training and evaluating on multiple corpora from different domains are less studied. This paper explores methods of generating relevant responses for each of multiple multi-domain corpora. We first examine interleaved learning which intermingles multiple corpora as the baseline. We then investigate two multi-domain learning methods, labeled learning and multi-task labeled learning, which encode each corpus through a unique corpus embedding. Furthermore, we propose Domain-specific Frequency (DF), a novel word-level importance weight that measures the relative importance of a word for a specific corpus compared to other corpora. Based on DF, we propose weighted learning, a method that integrates DF to the loss function. We also adopt DF as a new evaluation metric. Extensive experiments show that our methods gain significant improvements on both automatic and human evaluation. We share our code and data for reproducibility
PBH assisted search for QCD axion dark matter

The entropy production prior to BBN era is one of ways to prevent QCD axion with the decay constant Fa ∈ [1012 GeV, 1016 GeV] from overclosing the universe when the misalignment angle is θ i = 𝒪(1). As such, it is necessarily accompanied by an early matter-dominated era (EMD) provided the entropy production is achieved via the decay of a heavy particle. In this work, we consider the possibility of formation of primordial black holes during the EMD era with the assumption of the enhanced primordial scalar perturbation on small scales (k > 104 Mpc-1). In such a scenario, it is expected that PBHs with axion halo accretion develop to ultracompact minihalos (UCMHs). We study how UCMHs so obtained could be of great use in the experimental search for QCD axion dark matter with Fa ∈ [1012 GeV, 1016 GeV].
Fill and dump measurement of the neutron lifetime using an asymmetric magneto-gravitational trap

The past two decades have yielded several new measurements and reanalyses of older measurements of the neutron lifetime. These have led to a 4.4 standard deviation discrepancy between the most precise measurements of the neutron decay rate producing protons in cold neutron beams and the lifetime measured in neutron storage experiments. Measurements using different techniques are important for investigating whether there are unidentified systematic effects in any of the measurements . In this paper we report a new measurement using the Los Alamos asymmetric magneto-gravitational trap where the surviving neutrons are counted external to the trap using the fill and dump method. The new measurement gives a free neutron lifetime of Although this measurement is not as precise, it is in statistical agreement with previous results using in situ counting in the same apparatus.
On Disentangled and Locally Fair Representations

We study the problem of performing classification in a manner that is fair for sensitive groups, such as race and gender. This problem is tackled through the lens of disentangled and locally fair representations. We learn a locally fair representation, such that, under the learned representation, the neighborhood of each sample is balanced in terms of the sensitive attribute. For instance, when a decision is made to hire an individual, we ensure that the $K$ most similar hired individuals are racially balanced. Crucially, we ensure that similar individuals are found based on attributes not correlated to their race. To this end, we disentangle the embedding space into two representations. The first of which is correlated with the sensitive attribute while the second is not. We apply our local fairness objective only to the second, uncorrelated, representation. Through a set of experiments, we demonstrate the necessity of both disentangled and local fairness for obtaining fair and accurate representations. We evaluate our method on real-world settings such as predicting income and re-incarceration rate and demonstrate the advantage of our method.
SMC and MPC based composite control for a constrained second-order nonlinear system with external disturbances

—The paper proposes a novel strucuture of composite control consisting of sliding mode control (SMC) and model predictive control (MPC) for a constrained second-order nonlinear system with external disturbances. Based on linear sliding mode surface, the control law is ﬁrst generated by MPC problem to drive the system state to a certain region, and then is switched to SMC which can promise the input-to state stable (ISS). The recursive feasibility of MPC and closed-loop stability is guaranteed, while the whole process behaves robustness and constraints satisfaction. Finally, a simulation case is provided to demonstate the effectiveness of the composite control.
Time-multiplexed Neural Holography: A Flexible Framework for Holographic Near-eye Displays with Fast Heavily-quantized Spatial Light Modulators

Holographic near-eye displays offer unprecedented capabilities for virtual and augmented reality systems, including perceptually important focus cues. Although artificial intelligence–driven algorithms for computer-generated holography (CGH) have recently made much progress in improving the image quality and synthesis efficiency of holograms, these algorithms are not directly applicable to emerging phase-only spatial light modulators (SLM) that are extremely fast but offer phase control with very limited precision. The speed of these SLMs offers time multiplexing capabilities, essentially enabling partially-coherent holographic display modes. Here we report advances in camera-calibrated wave propagation models for these types of holographic near-eye displays and we develop a CGH framework that robustly optimizes the heavily quantized phase patterns of fast SLMs. Our framework is flexible in supporting runtime supervision with different types of content, including 2D and 2.5D RGBD images, 3D focal stacks, and 4D light fields. Using our framework, we demonstrate state-of-the-art results for all of these scenarios in simulation and experiment.
Confined Continuous-Flow Plasma Source For High-Average-Power Laser Plasma Acceleration

Over the last decades, significant advances in high-power laser systems have enabled rapid progress in the development of laser-driven plasma accelerators. Today, the results obtained in beam stability and reproducibility present laser plasma acceleration as a viable and promising alternative to conventional accelerators. As several electron beam and secondary sources applications require high average currents, a major focus is now on increasing the beam's repetition rate. In the following, we introduce a novel plasma source for kHz electron acceleration, providing a continuous and spatially confined gas flow, while minimising the gas load in the acceleration chamber.
Diversifying Neural Dialogue Generation via Negative Distillation

Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios. Recently, an interesting approach, namely negative training, has been proposed to alleviate this problem by reminding the model not to generate high-frequency responses during training. However, its performance is hindered by two issues, ignoring low-frequency but generic responses and bringing low-frequency but meaningless responses. In this paper, we propose a novel negative training paradigm, called negative distillation, to keep the model away from the undesirable generic responses while avoiding the above problems. First, we introduce a negative teacher model that can produce query-wise generic responses, and then the student model is required to maximize the distance with multi-level negative knowledge. Empirical results show that our method outperforms previous negative training methods significantly.
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data

Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) with generalized extreme value (GEV) distribution to forecast the block maximum value of a time series. Implementing such a network is a challenge as the framework must preserve the inter-dependent constraints among the GEV model parameters even when the DNN is initialized. We describe our approach to address this challenge and present an architecture that enables both conditional mean and quantile prediction of the block maxima. The extensive experiments performed on both real-world and synthetic data demonstrated the superiority of DeepExtrema compared to other baseline methods.
Characterizing player’s playing styles based on player vectors for each playing position in the Chinese Football Super League

ABSTRACT Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player’s style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016–2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.
Investigating the impact of intellectual capital on financial performance in Indian manufacturing sector

This paper empirically examine the association of intellectual capital efficiency with financial performance measures namely productivity, profitability and market valuation in Indian manufacturing sector respectively. Value added intellectual coefficient (VAICTM) has been used for measuring intellectual capital efficiency. 10 years data of Top 50 companies from the manufacturing sector have been selected from CMIE Prowess database. The result evince that IC is significantly affecting the profitability, while it is not playing any significant role in increasing the productivity and market valuation of the companies. There is an immediate need for companies to utilize their intellectual capital effectively and efficiently to increase the financial performance.
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution

Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing. Recently, Wasserstein patch priors (WPP), which are based on the comparison of the patch distributions of the unknown image and a reference image, were successfully used as data-driven regularizers in the variational formulation of superresolution. However, for each input image, this approach requires the solution of a non-convex minimization problem which is computationally costly. In this paper, we propose to learn two kind of neural networks in an unsupervised way based on WPP loss functions. First, we show how convolutional neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is learned, it can be very efficiently applied to any input image. Second, we incorporate conditional normalizing flows to provide a tool for uncertainty quantification. Numerical examples demonstrate the very good performance of WPPNets for superresolution in various image classes even if the forward operator is known only approximately.
1-to-1 or 1-to-n? Investigating the Effect of Function Inlining on Binary Similarity Analysis

Binary similarity analysis is critical to many code-reuse-related issues, where function matching is its fundamental task. “1-to-1” mechanism has been applied in most binary similarity analysis works, in which one function in a binary file is matched against one function in a source file or binary file. However, we discover that the function mapping is a more complex problem of “1-to-n” (one binary function matches multiple source functions or binary functions) or even “n-to-n” (multiple binary functions match multiple binary functions) due to the existence of function inlining, different from traditional understanding. In this article, we investigate the effect of function inlining on binary similarity analysis. We carry out three studies to investigate the extent of function inlining, the performance of existing works under function inlining, and the effectiveness of existing inlining-simulation strategies. Firstly, a scalable and lightweight identification method is designed to recover function inlining in binaries. 88 projects (compiled in 288 versions and resulting in 32,460,156 binary functions) are collected and analyzed to construct four inlining-oriented datasets for four security tasks in the software supply chain, including code search, OSS (Open Source Software) reuse detection, vulnerability detection, and patch presence test. Datasets reveal that the proportion of function inlining ranges from 30–40% when using O3 and sometimes can reach nearly 70%. Then, we evaluate four existing works on our dataset. Results show most existing works neglect inlining and use the “1-to-1” mechanism. The mismatches cause a 30% loss in performance during code search and a 40% loss during vulnerability detection. Moreover, most inlined functions would be ignored during OSS reuse detection and patch presence test, thus leaving these functions risky. Finally, we analyze two inlining-simulation strategies on our dataset. It is shown that they miss nearly 40% of the inlined functions, and there is still a large space for promotion. By precisely recovering when function inlining happens, we discover that inlining is usually cumulative when optimization increases. Thus, conditional inlining and incremental inlining are recommended to design a low-cost and high-coverage inlining-simulation strategy.
The role of cohomology in quantum computation with magic states

A web of cohomological facts relates quantum error correction, measurement-based quantum computation, symmetry protected topological order and contextuality. Here we extend this web to quantum computation with magic states. In this computational scheme, the negativity of certain quasiprobability functions is an indicator for quantumness. However, when constructing quasiprobability functions to which this statement applies, a marked difference arises between the cases of even and odd local Hilbert space dimension. At a technical level, establishing negativity as an indicator of quantumness in quantum computation with magic states relies on two properties of the Wigner function: their covariance with respect to the Clifford group and positive representation of Pauli measurements. In odd dimension, Gross' Wigner function – an adaptation of the original Wigner function to odd-finite-dimensional Hilbert spaces – possesses these properties. In even dimension, Gross' Wigner function doesn't exist. Here we discuss the broader class of Wigner functions that, like Gross', are obtained from operator bases. We find that such Clifford-covariant Wigner functions do not exist in any even dimension, and furthermore, Pauli measurements cannot be positively represented by them in any even dimension whenever the number of qudits is n≥2. We establish that the obstructions to the existence of such Wigner functions are cohomological.