Reviewerly/exHarmony huggingface dataset:
GradingNet: Towards Providing Reliable Supervisions for Weakly Supervised Object Detection by Grading the Box Candidates

Weakly-Supervised Object Detection (WSOD) aims at training a model with limited and coarse annotations for precisely locating the regions of objects. Existing works solve the WSOD problem by using a two-stage framework, i.e., generating candidate bounding boxes with weak supervision information and then refining them by directly employing supervised object detection models. However, most of such works mainly focus on the performance boosting of the first stage, while ignoring the better usage of generated candidate bounding boxes. To address this issue, we propose a new two-stage framework for WSOD, named GradingNet, which can make good use of the generated candidate bounding boxes. Specifically, the proposed GradingNet consists of two modules: Boxes Grading Module (BGM) and Informative Boosting Module (IBM). BGM generates proposals of the bounding boxes by using standard one-stage weakly-supervised methods, then utilizes Inclusion Principle to pick out highly-reliable boxes and evaluate the grade of each box. With the above boxes and their grade information, an effective anchor generator and a grade-aware loss are carefully designed to train the IBM. Taking the advantages of the grade information, our GradingNet achieves state-of-the-art performance on COCO, VOC 2007 and VOC 2012 benchmarks.
Poverty reduction in sub-Saharan Africa: The mixed roles of democracy and trade openness

This paper examines the impact of democracy and trade openness on poverty. To this end, we estimate a poverty model using the generalized method of moments (system-GMM) on a sample of 24 sub-Saharan African countries during the period 2005–2016. The econometric estimates provide two main sets of results. On the linear effects side, we find that democracy increases income poverty in non-oil producing countries and has no effect in oil producing countries, while trade openness has no impact on poverty. On the non-linear side, we find that imports improve household living conditions in democratic oil-producing countries on the one hand, and contribute to reducing monetary poverty in democratic non-oil-producing countries on the other. Our results are robust to estimates by different democracy indicators. Democracy and trade openness should therefore not be considered in isolation, and simultaneous policies are needed to enhance their impact on poverty in sub-Saharan Africa.
Propagating Wigner-Negative States Generated from the Steady-State Emission of a Superconducting Qubit

We experimentally demonstrate the steady-state generation of propagating Wigner-negative states from a continuously driven superconducting qubit. We reconstruct the Wigner function of the radiation emitted into propagating modes defined by their temporal envelopes, using digital filtering. For an optimized temporal filter, we observe a large Wigner logarithmic negativity, in excess of 0.08, in agreement with theory. The fidelity between the theoretical predictions and the states generated experimentally is up to 99%, reaching state-of-the-art realizations in the microwave frequency domain. Our results provide a new way to generate and control nonclassical states, and may enable promising applications such as quantum networks and quantum computation based on waveguide quantum electrodynamics.
TIMEDIAL: Temporal Commonsense Reasoning in Dialog

Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events.Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored.In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TIMEDIAL.We formulate TIME-DIAL as a multiple choice cloze task with over 1.1K carefully curated dialogs.Empirical results demonstrate that even the best performing models struggle on this task compared to humans, with 23 absolute points of gap in accuracy.Furthermore, our analysis reveals that the models fail to reason about dialog context correctly; instead, they rely on shallow cues based on existing temporal patterns in context, motivating future research for modeling temporal concepts in text and robust contextual reasoning about them.The dataset is publicly available at:
Escaping Local Optima with Non-Elitist Evolutionary Algorithms

Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitist EAs outperform elitist EAs has been one of the most fundamental open problems in evolutionary computation. A recent analysis of a non-elitist EA shows that this algorithm does not outperform its elitist counterparts on the benchmark problem JUMP. We solve this open problem through rigorous runtime analysis of elitist and non-elitist population-based EAs on a class of multi-modal problems. We show that with 3-tournament selection and appropriate mutation rates, the non-elitist EA optimises the multi-modal problem in expected polynomial time, while an elitist EA requires exponential time with overwhelmingly high probability. A key insight in our analysis is the non-linear selection profile of the tournament selection mechanism which, with appropriate mutation rates, allows a small sub-population to reside on the local optimum while the rest of the population explores the fitness valley. In contrast, we show that the comma-selection mechanism which does not have this non-linear profile, fails to optimise this problem in polynomial time. The theoretical analysis is complemented with an empirical investigation on instances of the set cover problem, showing that non-elitist EAs can perform better than the elitist ones. We also provide examples where usage of mutation rates close to the error thresholds is beneficial when employing non-elitist population-based EAs.
Linear cutting blocking sets and minimal codes in the rank metric

This work investigates the structure of rank-metric codes in connection with concepts from finite geometry, most notably the q-analogues of projective systems and blocking sets. We also illustrate how to associate a classical Hamming-metric code to a rank-metric one, in such a way that various rank-metric properties naturally translate into the homonymous Hamming-metric notions under this correspondence. The most interesting applications of our results lie in the theory of minimal rank-metric codes, which we introduce and study from several angles. Our main contributions are bounds for the parameters of a minimal rank-metric codes, a general existence result based on a combinatorial argument, and an explicit code construction for some parameter sets that uses the notion of a scattered linear set. Throughout the paper we also show and comment on curious analogies/divergences between the theories of error-correcting codes in the rank and in the Hamming metric.
WeaQA: Weak Supervision via Captions for Visual Question Answering

Methodologies for training visual question answering (VQA) models assume the availability of datasets with human-annotated Image-Question-Answer (I-Q-A) triplets.This has led to heavy reliance on datasets and a lack of generalization to new types of questions and scenes.Linguistic priors along with biases and errors due to annotator subjectivity have been shown to percolate into VQA models trained on such samples.We study whether models can be trained without any human-annotated Q-A pairs, but only with images and their associated textual descriptions or captions.We present a method to train models with synthetic Q-A pairs generated procedurally from captions.Additionally, we demonstrate the efficacy of spatial-pyramid image patches as a simple but effective alternative to dense and costly object bounding box annotations used in existing VQA models.Our experiments on three VQA benchmarks demonstrate the efficacy of this weakly-supervised approach, especially on the VQA-CP challenge, which tests performance under changing linguistic priors.
Online Learning Effectiveness During the COVID-19 Pandemic

This paper is set out to explore the students’ attitudes towards online learning effectiveness using the Blackboard platform in three public Saudi universities (Taibah, Hail, and Al-Baha) during COVID 19 pandemic. It examines the learning activities which ensure the achievement of education quality during unprecedented online learning. The survey based- questionnaire method was used to elicit students’ responses. The numbers of students who participated in the survey are 333. The main section of the questionnaire contains questions about the main online learning activities. The coefficient relation of the p-value is highly correlated when tested using Pearson’s r and Spearman’s. The score of Cronbach’s Alpha is (0.93) which indicates (greater internal consistency) an acceptable level of reliability. The findings positively emphasize the effective influence of online learning on student’s academic achievements in most of learning factors except in an assessment factor where the development of new assessment methods is needed.
Quantum CDMA Communication Systems

Barcoding photons can provide a host of functionalities that could benefit future quantum communication systems and networks beyond today's imagination. As a significant application of barcoding photons, we introduce code division multiple-access (CDMA) communication systems for various applications. In this context, we introduce and discuss the fundamental principles of a novel quantum CDMA (QCDMA) technique based on spectrally encoding and decoding of continuous-mode quantum light pulses. In particular, we present the mathematical models of various QCDMA modules that are fundamental in describing an ideal and typical QCDMA system, such as quantum signal sources, quantum spectral encoding phase operators, M$\times$M quantum broadcasting star-coupler, quantum spectral phase decoding operators, and the quantum receivers. In describing a QCDMA system, this paper considers a unified approach where the input continuous-mode quantum light pulses can take on any form of pure states such as Glauber states and quantum number states. For input number states, one can observe features like entanglement and quantum interference. More interestingly, due to Heisenberg's uncertainty principle, the quantum signals sent by photon number states obtain complete phase uncertainty at the time of measurement. Therefore, at the receiver output, the multiaccess inter-signal interference vanishes. Due to Heisenberg's uncertainty principle, the received signal intensity at the photodetector's output changes from a coherent detection scheme for input Glauber states to an incoherent detection scheme for input number states. Our mathematical model is valuable in the signal design and data modulations of point-to-point quantum communications, quantum pulse shaping, and quantum radar signals and systems where the inputs are continuous mode quantum signals.
Is Sparse Attention more Interpretable?

Sparse attention has been claimed to increase model interpretability under the assumption that it highlights influential inputs.Yet the attention distribution is typically over representations internal to the model rather than the inputs themselves, suggesting this assumption may not have merit.We build on the recent work exploring the interpretability of attention; we design a set of experiments to help us understand how sparsity affects our ability to use attention as an explainability tool.On three text classification tasks, we verify that only a weak relationship between inputs and co-indexed intermediate representations exists-under sparse attention and otherwise.Further, we do not find any plausible mappings from sparse attention distributions to a sparse set of influential inputs through other avenues.Rather, we observe in this setting that inducing sparsity may make it less plausible that attention can be used as a tool for understanding model behavior.
Single-Photon Detection with a Josephson Junction Coupled to a Resonator

We use a semiclassical formalism to optimize a microwave single-photon detector based on switching events of a current-biased Josephson junction coupled to a resonator. To detect very rare events, the average time between dark counts ${\ensuremath{\tau}}_{\mathrm{dark}}$ should be maximized taking into account that the switching time ${\ensuremath{\tau}}_{\mathrm{sw}}$ should be sufficiently small. We demonstrate that these times can be tuned in a wide range by changing the junction parameters, and ${\ensuremath{\tau}}_{\mathrm{dark}}/{\ensuremath{\tau}}_{\mathrm{sw}}\ensuremath{\sim}{10}^{9}$ can be achieved. Therefore, a junction-resonator arrangement can be used to detect extremely low photon fluxes (e.g., for searching for galactic axions).
Tailor: Generating and Perturbing Text with Semantic Controls

Controlled text perturbation is useful for evaluating and improving model generalizability.However, current techniques rely on training a model for every target perturbation, which is expensive and hard to generalize.We present Tailor, a semantically-controlled text generation system.Tailor builds on a pretrained seq2seq model and produces textual outputs conditioned on control codes derived from semantic representations.We craft a set of operations to modify the control codes, which in turn steer generation towards targeted attributes.These operations can be further composed into higher-level ones, allowing for flexible perturbation strategies.We demonstrate the effectiveness of these perturbations in multiple applications.First, we use Tailor to automatically create high-quality contrast sets for four distinct natural language processing (NLP) tasks.These contrast sets contain fewer spurious artifacts and are complementary to manually annotated ones in their lexical diversity.Second, we show that Tailor perturbations can improve model generalization through data augmentation.Perturbing just ∼2% of training data leads to a 5.8-point gain on an NLI challenge set measuring reliance on syntactic heuristics.
Scale Invariant Digital Color Image Encryption Using a 3D Modular Chaotic Map

Cryptography is one of the most important security mechanisms for the transmission of digital media on the Internet. Most proposed cryptographic image methods based on chaotic maps are dependent upon image sizes and most of them worked on square images. In this paper for tackling this problem, a scale-invariant color image encryption method in three-dimensional space is presented. At first, the two-dimensional color image is converted into three-dimensional space, in this case, the red, green, and blue color spectrums are divided into a set of gray-level square sub-images. Then, to have confusion and diffusion properties, the 3D substitution and 3D permutation operation are performed on the sub-images. In substitution operations, the pixel values of the sub-images are changed with the help of XOR and circular shift operators with appropriate keys. In permutation operation, the position of the pixels is changed using modular three-dimensional chaos mappings. To have scale-invariant three-dimensional permutation, the sub-images are divided into one or more windows with equal size, and then 3D modular chaotic map operations are performed on each window with separate keys. Depending on the number of sub-images, there may be two last windows that have overlapping. To increase the speed of color image encryption, the steps of a 3D modular chaotic map on the windows can be implemented in parallel. The proposed approach relative to the similar color image encryption algorithms increases the key space and improves standard parameters, such as entropy, sensitivity, adjacent pixel correlations, and histogram uniformity.
Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals

Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing.
HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection

This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection.This task aims to locate those spans that attribute to the text's toxicity within a text, which is crucial for semiautomated moderation in online discussions.We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models.Next, we integrate predictions of these models to produce a more credible and complement result.Our system achieves a char-level score of 70.83%, ranking 1/91.In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.
Learning Efficient GANs for Image Translation via Differentiable Masks and Co-Attention Distillation

Generative Adversarial Networks (GANs) have been widely-used in image translation, but their high computational and storage costs impede the deployment on mobile devices. Prevalent methods for CNN compression cannot be directly applied to GANs due to the specificity of GAN tasks and the unstable adversarial training. To solve these, in this paper, we introduce a novel GAN compression method, termed DMAD, by proposing a Differentiable Mask and a co-Attention Distillation. The former searches for a light-weight generator architecture in a training-adaptive manner. To overcome channel inconsistency when pruning the residual connections, an adaptive cross-block group sparsity is further incorporated. The latter simultaneously distills informative attention maps from both the generator and discriminator of a pre-trained model to the searched generator, effectively stabilizing the adversarial training of our light-weight model. Experiments show that DMAD can reduce the Multiply Accumulate Operations (MACs) of CycleGAN by 13x and that of Pix2Pix by 4x while retaining a comparable performance against the full model. Our code can be available at https://github.com/SJLeo/DMAD.
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data

Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in real-world applications. For more accurate prediction, methods had better grasp more data characteristics. Different from ordinary time series, ISTS is characterized by irregular time intervals of intra-series and different sampling rates of inter-series. However, existing methods have suboptimal predictions due to artificially introducing new dependencies in a time series and biasedly learning relations among time series when modeling these two characteristics. In this work, we propose a novel Time Encoding (TE) mechanism. TE can embed the time information as time vectors in the complex domain. It has the properties of absolute distance and relative distance under different sampling rates, which helps to represent two irregularities. Meanwhile, we create a new model named Time Encoding Echo State Network (TE-ESN). It is the first ESNs-based model that can process ISTS data. Besides, TE-ESN incorporates long short-term memories and series fusion to grasp horizontal and vertical relations. Experiments on one chaos system and three real-world datasets show that TE-ESN performs better than all baselines and has better reservoir property.
Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .