Reviewerly/exHarmony huggingface dataset:
CAS: corpus of clinical cases in French

Abstract Background Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing those applications and the corresponding tools. They are also crucial for designing reliable methods and reproducible results. Yet, in some areas, such as the medical area, due to confidentiality or to ethical reasons, it is complicated or even impossible to access representative textual data. We propose the CAS corpus built with clinical cases, such as they are reported in the published scientific literature in French. Results Currently, the corpus contains 4,900 clinical cases in French, totaling nearly 1.7M word occurrences. Some clinical cases are associated with discussions. A subset of the whole set of cases is enriched with morpho-syntactic (PoS-tagging, lemmatization) and semantic (the UMLS concepts, negation, uncertainty) annotations. The corpus is being continuously enriched with new clinical cases and annotations. The CAS corpus has been compared with similar clinical narratives. When computed on tokenized and lowercase words, the Jaccard index indicates that the similarity between clinical cases and narratives reaches up to 0.9727. Conclusion We assume that the CAS corpus can be effectively exploited for the development and testing of NLP tools and methods. Besides, the corpus will be used in NLP challenges and distributed to the research community.
Demo Abstract: Narwhal: a DASH-based Point Cloud Video Streaming System over Wireless Networks

Hologram video is expected to be the next generation video by providing immersive viewing experiences with 6 degrees of freedom. How to efficiently transmit hologram video is one fundamental research issue to promote its applications. As one of the most popular ways to represent hologram video, point cloud video draws more and more attentions. Point cloud video streaming faces many challenges due to large source video rate and high encoding/decoding complexity. To this end, we propose a novel DASH-based point cloud video streaming system: Narwhal, which aims to maximize the user's viewing experiences by efficiently allocating the computational and communication resources. We prototype this system and verify its performance over state-of-the-art wireless networks.
Experimental Analysis of Fuzzy Clustering Algorithms

Fuzzy clustering is an unsupervised technique for partitioning data into fuzzy clusters. Fuzzy clustering has wide applications in various domains of science and technology. So, in this paper, we have drawn a performance comparison of five fuzzy clustering algorithms: FCM, PFCM, CFCM, IFCM, and NC. Their performance is analyzed on the bases of cluster homogeneity, clusters varying in size, shape, and density as well as when population of outliers increases. Four standard datasets: D12, D15, Dunn, and Noisy Dunn are used for this review work. This research paper will be very helpful to researchers to choose the right algorithm as per the features of their data clusters.
A Novel Surface Recovery Algorithm for Dual Wavelength White LED in Vertical Scanning Interferometry (VSI)

The two peaks characteristic of yellow and blue light in the spectrum of dual-wavelength white light emitting diodes (LEDs) introduce distinctive features to the interference signal of white light scanning interferometry (WLSI). The distinctive features are defined as discontinuities, so that the fringe contrast function cannot be modeled as a single Gaussian function, and causes the interferogram to have uneven distribution of fringes of different orders in the scanning interferometer. This phenomenon leads to the low accuracy of the zero-order fringe position in the envelope calculation, which affects the repeatability and accuracy of the interferometry. This paper proposes a new surface recovery algorithm based on the Hilbert phase envelope and adjacent reference points calculation, which can effectively overcome the influence of the discontinuous signal of dual-wavelength LED white light interference on the three-dimensional reconstruction of WLSI measurements. The reliability of the algorithm is verified by experiments, and the measurement accuracy of LED WLSI system is evaluated.
UVIRT—Unsupervised Virtual Try-on Using Disentangled Clothing and Person Features

Virtual Try-on is the ability to realistically superimpose clothing onto a target person. Due to its importance to the multi-billion dollar e-commerce industry, the problem has received significant attention in recent years. To date, most virtual try-on methods have been supervised approaches, namely using annotated data, such as clothes parsing semantic segmentation masks and paired images. These approaches incur a very high cost in annotation. Even existing weakly-supervised virtual try-on methods still use annotated data or pre-trained networks as auxiliary information and the costs of the annotation are still significantly high. Plus, the strategy using pre-trained networks is not appropriate in the practical scenarios due to latency. In this paper we propose Unsupervised VIRtual Try-on using disentangled representation (UVIRT). After UVIRT extracts a clothes and a person feature from a person image and a clothes image respectively, it exchanges a clothes and a person feature. Finally, UVIRT achieve virtual try-on. This is all achieved in an unsupervised manner so UVIRT has the advantage that it does not require any annotated data, pre-trained networks nor even category labels. In the experiments, we qualitatively and quantitatively compare between supervised methods and our UVIRT method on the MPV dataset (which has paired images) and on a Consumer-to-Consumer (C2C) marketplace dataset (which has unpaired images). As a result, UVIRT outperform the supervised method on the C2C marketplace dataset, and achieve comparable results on the MPV dataset, which has paired images in comparison with the conventional supervised method.
Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression

Edges are the fundamental visual element for discovering tiny obstacles using a monocular camera. Nevertheless, tiny obstacles often have weak and inconsistent edge cues due to various properties such as small size and similar appearance to the free space, making it hard to capture them. To this end, we propose an occlusion-based multilayer approach, which specifies the scene prior as multilayer regions and utilizes these regions in each obstacle discovery module, i.e., edge detection and proposal extraction. Firstly, an obstacle-aware occlusion edge is generated to accurately capture the obstacle contour by fusing the edge cues inside all the multilayer regions, which intensifies the object characteristics of these obstacles. Then, a multistride sliding window strategy is proposed for capturing proposals that enclose the tiny obstacles as completely as possible. Moreover, a novel obstacle-aware regression model is proposed for effectively discovering obstacles. It is formed by a primary-secondary regressor, which can learn two dissimilarities between obstacles and other categories separately, and eventually generate an obstacle-occupied probability map. The experiments are conducted on two datasets to demonstrate the effectiveness of our approach under different scenarios. And the results show that the proposed method can approximately improve accuracy by 19% over FPHT and PHT, and achieves comparable performance to MergeNet. Furthermore, multiple experiments with different variants validate the contribution of our method. The source code is available at https://github.com/XuefengBUPT/TOD_OMR.
Polarization-independent single-photon switch based on a fiber-optical Sagnac interferometer for quantum communication networks

An essential component of future quantum networks is an optical switch capable of dynamically routing single photons. Here we implement such a switch, based on a fiber-optical Sagnac interferometer design. The routing is implemented with a pair of fast electro-optical telecom phase modulators placed inside the Sagnac loop, such that each modulator acts on an orthogonal polarization component of the single photons, in order to yield polarization-independent capability that is crucial for several applications. We obtain an average extinction ratio of more than 19 dB between both outputs of the switch. Our experiment is built exclusively with commercial off-the-shelf components, thus allowing direct compatibility with current optical communication systems.
Not all fixations are created equal: The benefits of using ex-Gaussian modeling of fixation durations

Various cognitive and perceptual factors have been shown to modulate the duration of fixations during visual exploration of complex scenes. The majority of these studies have only considered the mean of the distribution of fixation durations. However, this distribution is skewed to the right, so that an increase in the mean may be driven by a lengthening of all fixations (i.e., a right shift of the whole distribution) or only the relatively longer ones (i.e., a longer right tail of the distribution). To determine which factor is at play, the distribution can be modeled with an ex-Gaussian distribution, which is a convolution of a Gaussian and an exponential distribution. Here we demonstrate the usefulness of applying the ex-Gaussian model to empirical distributions of fixation durations and the reliability of its parameters across time. We demonstrate how the ex-Gaussian model had advantages over exclusive consideration of the mean, by showing that an increase in the mean can stem from specific changes in the components of the ex-Gaussian distribution. Specifically, the type of image leads to a change in the Gaussian component alone, indicating a right shift of the main mass of the distribution. By contrast, familiarity with the inspected image modifies the exponential component, and results in a more specific modulation of a subset of relatively long fixations. Hence, estimating the ex-Gaussian parameters may provide novel insights into the underlying processes that determine fixation duration and can contribute to the future development of process-based computational models of gaze behavior.
Idler-free channel position finding

Entanglement is a powerful tool for quantum sensing, and entangled states can greatly boost the discriminative power of protocols for quantum illumination, quantum metrology, or quantum reading. However, entangled state protocols generally require the retention of an idler state, to which the probes are entangled. Storing a quantum state is difficult and so technological limitations can make protocols requiring quantum memories impracticable. One alternative is idler-free protocols that utilize nonclassical sources but do not require any idler states to be stored. Here we apply such a protocol to the task of channel position finding. This involves finding a target channel in a sequence of background channels, and has many applications, including quantum sensing, quantum spectroscopy, and quantum reading.
Sample Optimization For Display Advertising

Sample optimization, which involves sample augmentation and sample refinement, is an essential but often neglected component in modern display advertising platforms. Due to the massive number of ad candidates, industrial ad service usually leverages a multi-layer funnel-shaped structure involving at least two stages: candidate generation and re-ranking. In the candidate generation step, an offline neural network matching model is often trained based on past click/conversion data to obtain the user feature vector and ad feature vector. However, there is a covariate shift problem between the user observed ads and all possible ones. As a result, the candidate generation model trained from the click/conversion history cannot fully capture users' potential intentions or generalize well to unseen ads. In this paper, we utilize several sample optimization strategies to alleviate the covariate shift problem for training candidate generation models. We have launched these strategies in Baidu display ad platform and achieved considerable improvements in offline metrics, including both offline click-recall, cost-recall, as well as online metric cost per mille (CPM).
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing

Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predicate-object and higher-order relations. As such, they offer a flexible modeling framework that has been applied to many areas, including biology and pharmacology – most recently, in the fight against COVID-19. The flexibility of KG modeling is both a blessing and a challenge from the learning point of view. In this paper we propose a novel coupled tensor-matrix framework for KG embedding. We leverage tensor factorization tools to learn concise representations of entities and relations in knowledge bases and employ these representations to perform drug repurposing for COVID-19. Our proposed framework is principled, elegant, and achieves 100% improvement over the best baseline in the COVID-19 drug repurposing task using a recently developed biological KG.
Can Encrypted DNS Be Fast?

In this paper, we study the performance of encrypted DNS protocols and conventional DNS from thousands of home networks in the United States, over one month in 2020. We perform these measurements from the homes of 2,693 participating panelists in the Federal Communications Commission's (FCC) Measuring Broadband America program. We found that clients do not have to trade DNS performance for privacy. For certain resolvers, DoT was able to perform faster than DNS in median response times, even as latency increased. We also found significant variation in DoH performance across recursive resolvers. Based on these results, we recommend that DNS clients (e.g., web browsers) should periodically conduct simple latency and response time measurements to determine which protocol and resolver a client should use. No single DNS protocol nor resolver performed the best for all clients.
Prostate Gland Segmentation in Histology Images via Residual and Multi-resolution U-NET

Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. The computer-aided diagnosis systems for this task are based on the glandular structural analysis in histology images. Hence, accurate gland detection and segmentation is crucial for a successful prediction. The methodological basis of this work is a prostate gland segmentation based on U-Net convolutional neural network architectures modified with residual and multi-resolution blocks, trained using data augmentation techniques. The residual configuration outperforms in the test subset the previous state-of-the-art approaches in an image-level comparison, reaching an average Dice Index of 0.77.
Simulation Extractable Versions of Groth’s zk-SNARK Revisited

Among various NIZK arguments, zk-SNARKs are the most efficient constructions in terms of proof size and verification which are two critical criteria for large scale applications. Currently, Groth’s construction, $$\textsf {Groth16}$$ , from Eurocrypt’16 is the most efficient and widely deployed one. However, it is proven to achieve only knowledge soundness, which does not prevent attacks from the adversaries who have seen simulated proofs. There has been considerable progress in modifying $$\textsf {Groth16}$$ to achieve simulation extractability to guarantee the non-malleability of proofs. We revise the Simulation Extractable (SE) version of $$\textsf {Groth16}$$ proposed by Bowe and Gabizon that has the most efficient prover and $$\mathsf {crs}$$ size among the candidates, although it adds Random Oracle (RO) to the original construction. We present a new version which requires 4 parings in the verification, instead of 5. We also get rid of the RO at the cost of a collision resistant hash function and a single new element in the $$\mathsf {crs}$$ . Our construction is proven in the generic group model and seems to result in the most efficient SE variant of $$\textsf {Groth16}$$ in most dimensions.
Certifiably Adversarially Robust Detection of Out-of-Distribution Data

Deep neural networks are known to be overconfident when applied to out-of-distribution (OOD) inputs which clearly do not belong to any class. This is a problem in safety-critical applications since a reliable assessment of the uncertainty of a classifier is a key property, allowing the system to trigger human intervention or to transfer into a safe state. In this paper, we aim for certifiable worst case guarantees for OOD detection by enforcing not only low confidence at the OOD point but also in an $l_\infty$-ball around it. For this purpose, we use interval bound propagation (IBP) to upper bound the maximal confidence in the $l_\infty$-ball and minimize this upper bound during training time. We show that non-trivial bounds on the confidence for OOD data generalizing beyond the OOD dataset seen at training time are possible. Moreover, in contrast to certified adversarial robustness which typically comes with significant loss in prediction performance, certified guarantees for worst case OOD detection are possible without much loss in accuracy.
Extension of the Alberti-Ulhmann criterion beyond qubit dichotomies

The Alberti-Ulhmann criterion states that any given qubit dichotomy can be transformed into any other given qubit dichotomy by a quantum channel if and only if the testing region of the former dichotomy includes the testing region of the latter dichotomy. Here, we generalize the Alberti-Ulhmann criterion to the case of arbitrary number of qubit or qutrit states. We also derive an analogous result for the case of qubit or qutrit measurements with arbitrary number of elements. We demonstrate the possibility of applying our criterion in a semi-device independent way.
Classical and Quantum Algorithms for Tensor Principal Component Analysis

We present classical and quantum algorithms based on spectral methods for a problem in tensor principal component analysis. The quantum algorithm achieves a quartic speedup while using exponentially smaller space than the fastest classical spectral algorithm, and a super-polynomial speedup over classical algorithms that use only polynomial space. The classical algorithms that we present are related to, but slightly different from those presented recently in Ref. 1. In particular, we have an improved threshold for recovery and the algorithms we present work for both even and odd order tensors. These results suggest that large-scale inference problems are a promising future application for quantum computers.
Adaptable and Verifiable BDI Reasoning

Long-term autonomy requires autonomous systems to adapt as their capabilities no longer perform as expected.To achieve this, a system must first be capable of detecting such changes.In this position paper, we describe a system architecture for Belief-Desire-Intention (BDI) autonomous agents capable of adapting to changes in a dynamic environment and outline the required research.Specifically, we describe an agent-maintained self-model with accompanying theories of durative actions and learning new action descriptions in BDI systems.
Completeness and consistency analysis for evolving knowledge bases

Assessing the quality of an evolving knowledge base is a challenging task as it often requires to identify correct quality assessment procedures. Since data is often derived from autonomous, and increasingly large data sources, it is impractical to manually curate the data, and challenging to continuously and automatically assess their quality. In this paper, we explore two main areas of quality assessment related to evolving knowledge bases: (i) identification of completeness issues using knowledge base evolution analysis, and (ii) identification of consistency issues based on integrity constraints, such as minimum and maximum cardinality, and range constraints. For completeness analysis, we use data profiling information from consecutive knowledge base releases to estimate completeness measures that allow predicting quality issues. Then, we perform consistency checks to validate the results of the completeness analysis using integrity constraints and learning models. The approach has been tested both quantitatively and qualitatively by using a subset of datasets from both DBpedia and 3cixty knowledge bases. The performance of the approach is evaluated using precision, recall, and F1 score. From completeness analysis, we observe a 94% precision for the English DBpedia KB and 95% precision for the 3cixty Nice KB. We also assessed the performance of our consistency analysis by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. We observed that the best performing model in our experimental setup is the Random Forest, reaching an F1 score greater than 90% for minimum and maximum cardinality and 84% for range constraints.
Multiplexed Single Photons from Deterministically Positioned Nanowire Quantum Dots

Solid-state quantum emitters are excellent sources of on-demand indistinguishable or entangled photons and can host long-lived spin memories, crucial resources for photonic quantum information applications. However, their scalability remains an outstanding challenge. Here we present a scalable technique to multiplex streams of photons from multiple independent quantum dots, on-chip, into a fiber network for use off-chip. Multiplexing is achieved by incorporating a multi-core fiber into a confocal microscope and spatially matching the multiple foci, seven in this case, to quantum dots in an array of deterministically positioned nanowires. First, we report the coherent control of the emission of biexciton-exciton cascade from a single nanowire quantum dot under resonant two-photon excitation. Then, as a proof-of-principle demonstration, we perform parallel spectroscopy on the nanowire array to identify two nearly identical quantum dots at different positions which are subsequently tuned into resonance with an external magnetic field. Multiplexing of background-free single photons from these two quantum dots is then achieved. Our approach, applicable to all types of quantum emitters, can readily be scaled up to multiplex $>100$ quantum light sources, providing a breakthrough in hardware for photonic based quantum technologies. Immediate applications include quantum communication, quantum simulation, and quantum computation.