Reviewerly/exHarmony huggingface dataset:
TDMPNet: Prototype Network with Recurrent Top-Down Modulation for Robust Object Classification Under Partial Occlusion

Despite deep convolutional neural networks’ great success in object classification, recent work has shown that they suffer from a severe generalization performance drop under occlusion conditions that do not appear in the training data. Due to the large variability of occluders in terms of shape and appearance, training data can hardly cover all possible occlusion conditions. However, in practice we expect models to reliably generalize to various novel occlusion conditions, rather than being limited to the training conditions. In this work, we integrate inductive priors including prototypes, partial matching and top-down modulation into deep neural networks to realize robust object classification under novel occlusion conditions, with limited occlusion in training data. We first introduce prototype learning as its regularization encourages compact data clusters for better generalization ability. Then, a visibility map at the intermediate layer based on feature dictionary and activation scale is estimated for partial matching, whose prior sifts irrelevant information out when comparing features with prototypes. Further, inspired by the important role of feedback connection in neuroscience for object recognition under occlusion, a structural prior, i.e. top-down modulation, is introduced into convolution layers, purposefully reducing the contamination by occlusion during feature extraction. Experiment results on partially occluded MNIST, vehicles from the PASCAL3D+ dataset, and vehicles from the cropped COCO dataset demonstrate the improvement under both simulated and real-world novel occlusion conditions, as well as under the transfer of datasets.
Comments on “Attribute-Based Data Sharing Scheme Revisited in Cloud Computing”

In this letter, we discuss the security weakness of Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s attribute-based data sharing scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2016.2549004). Through designing two concrete attacks, we identify two serious security flaws in their scheme. 1) First, we show that their scheme is insecure because in their scheme any authenticated user can freely tamper with the weight of his own attribute to gain higher level decryption privilege to arbitrarily decrypt the ciphertext belonging to another user with higher weight of attribute. 2) Second, we further demonstrate that their scheme is trivial insecure because in their scheme even any malicious authenticated user’s attribute does not match the access policy of a ciphertext, he/she still has the power to decrypt the ciphertext, i.e., the decryption power is independent of attributes, thus, their scheme is not a rigorous attribute-based scheme. The two weaknesses discovered may hinder their scheme infeasible for practical deployment. Accordingly, we present a remedy solution to the issues while preserving all the security features of the original scheme. We hope that our cryptoanalysis and remedy scheme may contribute to avoiding similar design flaws in future designs.
An Approach to Reduce Communication for Multi-agent Mapping Applications

In the context of a multi-agent system that uses a Gaussian process to estimate a spatial field of interest, we propose an approach that enables an agent to reduce the amount of data it shares with other agents. The main idea of the strategy is to rigorously assign a novelty metric to each measurement as it is collected, and only measurements that are sufficiently novel are communicated. We consider the ideal scenario where an agent can instantly share novel measurements, and we also consider the more practical scenario in which communication suffers from low bandwidth and is range-limited. For this scenario, an agent can only broadcast an informative subset of the novel measurements when the agent encounters other agents. We explore three different informative criteria for subset selection, namely entropy, mutual information, and a new criterion that reflects the value of a measurement. We apply our approach to three real-world datasets relevant to robotic mapping. The empirical findings show that an agent can reduce the amount of communicated measurements by two orders of magnitude and that the new criterion for subset selection yields superior predictive performance relative to entropy and mutual information.
Recovering or Testing Extended-Affine Equivalence

Extended Affine (EA) equivalence is the equivalence relation between two vectorial Boolean functions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> such that there exist two affine permutations <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> , and an affine function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> satisfying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G = A \circ F \circ B + C$ </tex-math></inline-formula> . While the problem has a simple formulation, it is very difficult in practice to test whether two functions are EA-equivalent. This problem has two variants: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EA-partitioning</i> deals with partitioning a set of functions into disjoint EA-equivalence classes, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EA-recovery</i> is about recovering the tuple <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(A,B,C)$ </tex-math></inline-formula> if it exists. In this paper, we present a new algorithm that efficiently solves the EA-recovery problem for quadratic functions. Although its worst-case complexity occurs when dealing with APN functions, it supersedes, in terms of performance, all previously known algorithms for solving this problem for all quadratic functions and in any dimension, even in the case of APN functions. This approach is based on the Jacobian matrix of the functions, a tool whose study in this context can be of independent interest. The best approach for EA-partitioning in practice mainly relies on class invariants. We provide an overview of the known invariants along with a new one based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ortho-derivative</i> . This new invariant is applicable to quadratic APN functions, a specific type of functions that is of great interest, and of which tens of thousands need to be sorted into distinct EA-classes. Our ortho-derivative-based invariant is very fast to compute, and it practically always distinguishes between EA-inequivalent quadratic APN functions.
Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis

Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal.This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model's dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling.The method was developed with the R statistical software and is made available through the R package 'irg' hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500.Supplementary data are available at Bioinformatics online.
Technical Lag of Dependencies in Major Package Managers

Background: Third party libraries used by a project (dependencies) can easily become outdated over time, a phenomenon called technical lag. Keeping dependencies up to date induces a significant overhead in terms of the resources (e.g, developer time), but necessary to maintain software quality. Aims: This study provides a large scale analysis of technical lag across the major package managers currently in use. Method: We conducted a mixed-methods study using open-source project data obtained from 14 package managers using the libraries.io dataset. Results: The majority of fixed version declarations, along with a significant number of flexible declarations, are outdated. Fixed declarations are not regularly updated, except in major updates, so they quickly lag. Despite the prevalence of breaking changes in updates, downgrading declarations to earlier versions are rare. Conclusions: Technical lag is prevalent but preventable across package managers - semantic versioning based declaration ranges would remove the majority of lag. Further tooling uptake is also recommended to minimise technical lag.
Top-1 CORSMAL Challenge 2020 Submission: Filling Mass Estimation Using Multi-modal Observations of Human-Robot Handovers

Human-robot object handover is a key skill for the future of human-robot collaboration. CORSMAL 2020 Challenge focuses on the perception part of this problem: the robot needs to estimate the filling mass of a container held by a human. Although there are powerful methods in image processing and audio processing individually, answering such a problem requires processing data from multiple sensors together. The appearance of the container, the sound of the filling, and the depth data provide essential information. We propose a multi-modal method to predict three key indicators of the filling mass: filling type, filling level, and container capacity. These indicators are then combined to estimate the filling mass of a container. Our method obtained Top-1 overall performance among all submissions to CORSMAL 2020 Challenge on both public and private subsets while showing no evidence of overfitting. Our source code is publicly available: github.com/v-iashin/CORSMAL.
Quantum Random Access Codes for Boolean Functions

An $n\overset{p}{\mapsto}m$ random access code (RAC) is an encoding of $n$ bits into $m$ bits such that any initial bit can be recovered with probability at least $p$, while in a quantum RAC (QRAC), the $n$ bits are encoded into $m$ qubits. Since its proposal, the idea of RACs was generalized in many different ways, e.g. allowing the use of shared entanglement (called entanglement-assisted random access code, or simply EARAC) or recovering multiple bits instead of one. In this paper we generalize the idea of RACs to recovering the value of a given Boolean function $f$ on any subset of fixed size of the initial bits, which we call $f$-random access codes. We study and give protocols for $f$-random access codes with classical ($f$-RAC) and quantum ($f$-QRAC) encoding, together with many different resources, e.g. private or shared randomness, shared entanglement ($f$-EARAC) and Popescu-Rohrlich boxes ($f$-PRRAC). The success probability of our protocols is characterized by the \emph{noise stability} of the Boolean function $f$. Moreover, we give an \emph{upper bound} on the success probability of any $f$-QRAC with shared randomness that matches its success probability up to a multiplicative constant (and $f$-RACs by extension), meaning that quantum protocols can only achieve a limited advantage over their classical counterparts.
Characterizing Midcircuit Measurements on a Superconducting Qubit Using Gate Set Tomography

Key applications of quantum computing, including error correction, rely critically on the capability to measure (read out) the state of selected quantum bits, without disturbing other qubits or terminating the computation. Such $m\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t$ $m\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}s$ are now being implemented in a few cutting-edge platforms, but their development is hindered by lack of a means to measure their performance and characterize the errors that they produce. The authors show how to extend a popular tomography method for precise characterization of midcircuit measurements, and using their QILGST protocol discover a pernicious kind of non-Markovian error, which once identified can be handled.
Research on Target Detection algorithm based on Deep Learning Technology

This paper summarizes the research progress of target detection using convolution neural network in recent years. These studies not only cover the design of all kinds of convolution neural network target detection algorithms, but also provide a deeper perspective for the development of computer vision. On the basis of consulting the data, this paper focuses on the representative Faster-RCNN, YOLO V3 and SSD algorithms. By reviewing their predecessor algorithms, covering the current mainstream target detection algorithms, and analyzing the technologies they use, summarize and analyze their advantages and disadvantages. And in the last part, it points out the still existing problems in target detection and the development direction in the future.
Potential Risk Detection System of Hyperledger Fabric Smart Contract based on Static Analysis

As an indispensable part of Hyperledger Fabric application system, smart contracts are mostly developed in general-purpose programming languages such as Golang. However, these smart contracts often have potential risks that cause serious problems such as failed transactions or sensitive information leakage on the ledger. Although there are already some detection tools for potential risks, e.g., Chaincode Scanner and Chaincode Analyzer, the accuracy and coverage of them are limited. In response to the above problems, this paper summarizes 16 potential risks in smart contracts, including three type risks: Non-determinism Risk, Logical Security Risk, and Private Data Security Risk. In order to detect them, we propose a new static analysis method based on Abstract Syntax Tree Analysis, Package Dependency Analysis, and Functional Dependency Analysis. Based on the new method, a detection system is designed that can detect 16 potential risks in smart contracts developed using Golang language more accurately and provide development suggestions to eliminate these risks.
ASSEM-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques

Recent works on voice conversion (VC) focus on preserving the rhythm and the intonation as well as the linguistic content. To preserve these features from the source, we decompose current non-parallel VC systems into two encoders and one decoder. We analyze each module with several experiments and reassemble the best components to propose Assem-VC, a new state-of-the-art any-to-many non-parallel VC system. We also examine that PPG and Cotatron features are speaker-dependent, and attempt to remove speaker identity with adversarial training. Code and audio samples are available at https://github.com/mindslab-ai/assem-vc.
Automated identification of glomeruli and synchronised review of special stains in renal biopsies by machine learning and slide registration: a cross‐institutional study

Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on slides from multiple institutions. We used machine learning to identify glomeruli on renal biopsies and compared performance between single and multiple institutions.Randomly selected, adequately sampled renal core biopsy cases (71) consisting of four stains each (haematoxylin and eosin, trichrome, silver, periodic acid Schiff) from three institutions were digitised at ×40. Glomeruli were manually annotated by three renal pathologists using a digital tool. Cases were divided into training/validation (n = 52) and evaluation (n = 19) cohorts. An algorithm was trained to develop three convolutional neural network (CNN) models which tested case cohorts intra- and inter-institutionally. Raw CNN search data from each of the four slides per case were merged into composite regions of interest containing putative glomeruli. The sensitivity and modified specificity of glomerulus detection (versus annotated truth) were calculated for each model/cohort. Intra-institutional (3) sensitivity ranged from 90 to 93%, with modified specificity from 86 to 98%. Interinstitutional (1) sensitivity was 77%, with modified specificity 97%. Combined intra- and inter-institutional (1) sensitivity was 86%, with modified specificity 92%.Feature detection sensitivity degrades when training and test material originate from different sites. Training using a combined set of digital slides from three institutions improves performance. Differing histology methods probably account for algorithm performance contrasts. Our data highlight the need for diverse training sets for the development of generalisable machine learning histology algorithms.
Pedestrian Detection Algorithm based on Improved Faster RCNN

To solve the problem of poor detection effect of small-scale pedestrian in the image, based on the existing deep convolutional neural network(CNN)model. We propose an improved pedestrian detection algorithm based on Faster RCNN. First, the image features are extracted by CNN, and the areas that may contain pedestrians are extracted by clustering and regional Recommendation Network (RPN), Secondly, a multi-layer feature fusion strategy based on cascade is proposed. The semantic information of the network can be enhanced by combining the features of the high level with those of the low level. Finally, the Online Hard Example Mining(OHEM) method is used to train the high loss samples to deal with the imbalance between the positive and negative samples, so as to significantly improve the detection performance of the algorithm. Experimental results show that this method greatly improves the accuracy of small target pedestrian detection. In PASCAL VOC 2007 and INRIA pedestrian data sets, the average accuracy was improved by 6.3% and 13.93% respectively.
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies

Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.Although an increasing number of biomedical QA datasets has been recently made available, those resources are still rather limited and expensive to produce.Transfer learning via pre-trained language models (LMs) has been shown as a promising approach to leverage existing general-purpose knowledge.However, finetuning these large models can be costly and time consuming, often yielding limited benefits when adapting to specific themes of specialised domains, such as the COVID-19 literature.To bootstrap further their domain adaptation, we propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM).We encourage masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning.The resulting strategy is a downstream process applicable to a wide variety of masked LMs, not requiring additional memory or components in the neural architectures.Experimental results show performance on par with state-of-the-art models on several biomedical QA datasets.
A Computer Vision System for Monitoring Disconnect Switches in Distribution Substations

Knowing the state of the disconnect switches in a power distribution substation is important, since incorrect operation may lead to problems such as accidents, outages, and damaged equipment. Although human errors when reporting switch positions are rare, they can have a large impact when they occur, making automatic monitoring an attractive proposition, especially in substations without permanent staff. In this paper, we describe a non-intrusive computer vision system for monitoring the state of disconnect switches in distribution substations. The system employs regular surveillance cameras, which can also be used for other purposes, leading to lower cost and simpler installation and maintenance compared to individual per-switch sensors. Several challenges were addressed: cluttered backgrounds, occlusions, variations in lighting, weather and switch aspect, and the fact that it is often not possible to maneuver the switches during installation. We addressed these challenges by combining data provided by a human operator during installation with multiple machine learning models, relying on techniques such as SVMs for classifying SIFT or HOG features, and deep learning. By combining multiple outputs, the system achieved a 99.7365% success rate in experiments performed at a real substation over several days.