Reviewerly/exHarmony huggingface dataset:
ES-Net: Erasing Salient Parts to Learn More in Re-Identification

As an instance-level recognition problem, re-identification (re-ID) requires models to capture diverse features. However, with continuous training, re-ID models pay more and more attention to the salient areas. As a result, the model may only focus on few small regions with salient representations and ignore other important information. This phenomenon leads to inferior performance, especially when models are evaluated on small inter-identity variation data. In this paper, we propose a novel network, Erasing-Salient Net (ES-Net), to learn comprehensive features by erasing the salient areas in an image. ES-Net proposes a novel method to locate the salient areas by the confidence of objects and erases them efficiently in a training batch. Meanwhile, to mitigate the over-erasing problem, this paper uses a trainable pooling layer P-pooling that generalizes global max and global average pooling. Experiments are conducted on two specific re-identification tasks (i.e., Person re-ID, Vehicle re-ID). Our ES-Net outperforms state-of-the-art methods on three Person re-ID benchmarks and two Vehicle re-ID benchmarks. Specifically, mAP / Rank-1 rate: 88.6% / 95.7% on Market1501, 78.8% / 89.2% on DuckMTMC-reID, 57.3% / 80.9% on MSMT17, 81.9% / 97.0% on Veri-776, respectively. Rank-1 / Rank-5 rate: 83.6% / 96.9% on VehicleID (Small), 79.9% / 93.5% on VehicleID (Medium), 76.9% / 90.7% on VehicleID (Large), respectively. Moreover, the visualized salient areas show human-interpretable visual explanations for the ranking results.
Ultrahigh-fidelity composite rotational quantum gates

Composite pulse sequences, which produce arbitrary predefined rotations of a qubit on the Bloch sphere, are presented. The composite sequences contain up to 17 pulses and can compensate up to 8 orders of experimental errors in the pulse amplitude and the pulse duration. Composite sequences for three basic quantum gates, X (not), Hadamard and arbitrary rotation, are derived. Three classes of composite sequences are presented: one symmetric and two asymmetric. They contain as their lowest members two well-known composite sequences: the three-pulse symmetric SCROFULOUS pulse and the four-pulse asymmetric BB1 pulse, which compensate first- and second-order errors, respectively. The shorter sequences are derived analytically, and the longer ones numerically (instead by nesting and concatenation, as usually done hitherto). Consequently, the composite sequences derived here match or outperform the existing ones in terms of either speed or accuracy, or both. For example, we derive a second-order composite sequence, which is faster (by about 13%) than the famous BB1 sequence. For higher-order sequences, the speedup becomes much more pronounced. This is important for quantum information processing as the sequences derived here provide more options for finding the sweet spot between ultrahigh fidelity and high speed.
Calculating LRs for presence of body fluids from mRNA assay data in mixtures

<h2>Abstract</h2> Messenger RNA (mRNA) profiling can identify body fluids present in a stain, yielding information on what activities could have taken place at a crime scene. To account for uncertainty in such identifications, recent work has focused on devising statistical models to allow for probabilistic statements on the presence of body fluids. A major hurdle for practical adoption is that evidentiary stains are likely to contain more than one body fluid and current models are ill-suited to analyse such mixtures. Here, we construct a likelihood ratio (LR) system that can handle mixtures, considering the hypotheses H<sub>1</sub>: the sample contains at least one of the body fluids of interest (and possibly other body fluids); H<sub>2</sub>: the sample contains none of the body fluids of interest (but possibly other body fluids). Thus, the LR-system outputs an LR-value for any combination of mRNA profile and set of body fluids of interest that are given as input. The calculation is based on an augmented dataset obtained by <i>in silico</i> mixing of real single body fluid mRNA profiles. These digital mixtures are used to construct a probabilistic classification method (a 'multi-label classifier'). The probabilities produced are subsequently used to calculate an LR, via calibration. We test a range of different classification methods from the field of machine learning, ways to preprocess the data and multi-label strategies for their performance on <i>in silico</i> mixed test data. Furthermore, we study their robustness to different assumptions on background levels of the body fluids. We find logistic regression works as well as more flexible classifiers, but shows higher robustness and better explainability. We test the system's performance on lab-generated mixture samples, and discuss practical usage in case work.
Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study

Abstract Background The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. Methods Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups ( n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. Results The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses &gt;1 cm; there was a significant difference between the two groups ( P &lt; 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). Conclusions The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. Trial registration Chictr.org , ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.
SSD: A Unified Framework for Self-Supervised Outlier Detection

We ask the following question: what training information is required to design an effective outlier / out-of-distribution (OOD) detector, i.e, detecting samples that lie far away from training distribution? Since unlabeled data is easily accessible for many applications, the most compelling approach is to develop detectors based on only unlabeled in-distribution data. However, we observe that existing detectors based on unlabeled data perform poorly, often equivalent to a random prediction. In contrast, existing state-of-the-art OOD detectors achieve impressive performance but require access to fine-grained data labels for supervised training. We propose SSD, an outlier detector based on only unlabeled training data. We use self-supervised representation learning followed by a Mahalanobis distance based detection in the feature space. We demonstrate that SSD outperforms existing detectors based on unlabeled data by a large margin. Additionally, SSD achieves performance on par, and sometimes even better, with supervised training based detectors. Finally, we expand our detection framework with two key extensions. First, we formulate few-shot OOD detection, in which the detector has access to only one to five samples from the targeted OOD dataset. Second, we extend our framework to incorporate training data labels, if available. We find that our novel detection framework based on SSD displays enhanced performance with these extensions, and achieves state-of-the-art performance.
Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach

Background Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children’s health and development outcomes more integrally from multiple perspectives. Objective The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency. Methods We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study. Results During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks. Conclusions This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.
Designing and testing a blockchain application for patient identity management in healthcare

Abstract Objective Healthcare systems suffer from a lack of interoperability that creates “data silos,” causing patient linkage and data sharing problems. Blockchain technology’s unique architecture provides individuals greater control over their information and may help address some of the problems related to health data. A multidisciplinary team designed and tested a blockchain application, MediLinker, as a patient-centric identity management system. Methods The study used simulated data of “avatars” representing different types of patients. Thirty study participants were enrolled to visit simulated clinics, and perform various activities using MediLinker. Evaluation was based on Bouras’ criteria for patient-centric identity management and on the number of errors in entry and sharing of data by participants. Results Twenty-nine of the 30 participants completed all study activities. MediLinker fulfilled all of Bouras’ criteria except for one which was not testable. A majority of data errors were due to user error, such as wrong formatting and misspellings. Generally, the number of errors decreased with time. Due to COVID-19, sprint 2 was completed using “virtual” clinic visits. The number of user errors were less in virtual visits than in personal visits. Discussion The evaluation of MediLinker provides some evidence of the potential of a patient-centric identity management system using blockchain technology. The results showed a working system where patients have greater control over their information and can also easily provide consent for use of their data. Conclusion Blockchain applications for identity management hold great promise for use in healthcare but further research is needed before real-world adoption.
Can single photon excitation of two spatially separated modes lead to a violation of Bell inequality via weak-field homodyne measurements?

We reconsider the all-optical homodyne-measurement based experimental schemes that aim to reveal Bell nonclassicality of a single photon, often termed `nonlocality'. We focus on the schemes put forward by Tan, Walls and Collett (TWC, 1991) and Hardy (1994). In the light of our previous work the Tan, Walls and Collett setup can be described by a precise local hidden variable model, hence the claimed nonclassicality of this proposal is apparent, whereas the nonclassicality proof proposed by Hardy is impeccable. In this work we resolve the following problem: which feature of the Hardy's approach is crucial for its successful confirmation of nonclassicality. The scheme of Hardy differs from the Tan, Walls and Collett setup in two aspects. (i) It introduces a superposition of a single photon excitation with vacuum as the initial state of one of the input modes of a 50-50 beamsplitter, which creates the superposition state of two separable (exit) modes under investigation. (ii) In the final measurements Hardy's proposal utilises a varying strengths of the local oscillator fields, whereas in the TWC case they are constant. In fact the local oscillators in Hardy's scheme are either on or off (the local setting is specified by the presence or absence of the local auxiliary field). We show that it is the varying strength of the local oscillators, from setting to setting, which is the crucial feature enabling violation of local realism in the Hardy setup, whereas it is not necessary to use initial superposition of a single photon excitation with vacuum as the initial state of the input mode. Neither one needs to operate in the fully on/off detection scheme. Despite the failure of the Tan, Walls and Collett scheme in proving Bell nonclassicality, we show that their scheme can serve as an entanglement indicator.
DGattGAN: Cooperative Up-Sampling Based Dual Generator Attentional GAN on Text-to-Image Synthesis

Text-to-image synthesis task aims at generating images consistent with input text descriptions and is well developed by the Generative Adversarial Network (GAN).Although GAN based image generation approaches have achieved promising results, synthesizing quality is sometimes unsatisfied due to discursive generation of background and object.In this article, we propose a cooperative up-sampling based Dual Generator attentional GAN (DGattGAN) to generate high-quality images from text description.To achieve this, two generators with individual generation purpose are established to decouple object and background generation.In particular, we introduce a cooperative up-sampling mechanism to build cooperation between object and background generators during training.This strategy is potentially very useful as any dual generator architecture in GAN models can benefit from this mechanism.Furthermore, we propose an asymmetric information feeding scheme to distinguish two synthesis tasks, such that each generator only synthesizes based on semantic information they accept.Taking advantage of effective dual generator, the attention mechanism we incorporated on object generator could devote to fine-grained details generation on actual targeted objects.Experiments on Caltech-UCSD Bird (CUB) and Oxford-102 datasets suggest that generated images by the proposed model are more realistic and consistent with input text, and DGattGAN is competent compared to state-of-the-art methods according to Inception Score (IS) and R-precision metrics.Our codes are available at: https://github.com/ecfish/DGattGAN.
IEEE Standard for Augmented Reality Learning Experience Models

Augmented Reality has found its way into training scenarios, e.g., when used in companies to deliver close-to-practice instruction. As a prerequisite for wide-scale adoption, however, there is need for a structured approach to model and formalize training activity. For this reason, the Augmented Reality IEEE standard ARLEM was defined. The standard was developed with the help of a reference implementation, MIRAGE XR, to demonstrate how real-life training applications can be created and edited using an Augmented Reality editor for learning experiences. With IEEE ARLEM and Mirage XR, standardized and interoperable learning content can be created and exchanged.
Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

Abstract Our recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034 ) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.
High‐Fidelity Geometric Quantum Gates with Short Paths on Superconducting Circuits

Geometric phases are robust against certain types of local noises, and thus provide a promising way towards high-fidelity quantum gates. However, comparing with the dynamical ones, previous implementations of nonadiabatic geometric quantum gates usually require longer evolution time, due to the needed longer evolution path. Here, we propose a scheme to realize nonadiabatic geometric quantum gates with short paths based on simple pulse control techniques, instead of deliberated pulse control in previous investigations, which can thus further suppress the influence from the environment induced noises. Specifically, we illustrate the idea on a superconducting quantum circuit, which is one of the most promising platforms for realizing practical quantum computer. As the current scheme shortens the geometric evolution path, we can obtain ultra-high gate fidelity, especially for the two-qubit gate case, as verified by our numerical simulation. Therefore, our protocol suggests a promising way towards high-fidelity and roust quantum computation on a solid-state quantum system.
A general language-based framework for specifying and verifying notions of opacity

Opacity is an information flow property that captures the notion of plausible deniability in dynamic systems, that is whether an intruder can deduce that “secret” behavior has occurred. In this paper we provide a general framework of opacity to unify the many existing notions of opacity that exist for discrete event systems. We use this framework to discuss language-based and state-based notions of opacity over automata. We present several methods for language-based opacity verification, and a general approach to transform state-based notions into language-based ones. We demonstrate this approach for current-state and initial-state opacity, unifying existing results. We then investigate the notions of K-step opacity. We provide a language-based view of K-step opacity encompassing two existing notions and two new ones. We then analyze the corresponding language-based verification methods both formally and with numerical examples. In each case, the proposed methods offer significant reductions in runtime and space complexity.
Nonadiabatic geometric quantum gates that are insensitive to qubit-frequency drifts

Quantum manipulation based on geometric phases provides a promising way towards robust quantum gates. However, in the current implementation of nonadiabatic geometric phases, operational and/or random errors tend to destruct the conditions that induce geometric phases, thereby smearing their noise-resilient feature. In a recent experiment [Y. Xu et al., Phys. Rev. Lett. 124, 230503 (2020)], high-fidelity universal geometric quantum gates have been implemented in a superconducting circuit, which are robust to different types of errors under different configurations of the geometric evolution paths. Here, we apply the path-design strategy to explain in detail why both configurations can realize universal quantum gates in a single-loop way. Meanwhile, we purposefully induce our geometric manipulation by selecting the path configuration that is robust against the qubit-frequency-drift induced error, which is the dominant error source on realistic superconducting circuits and has not been deliberately addressed. Moreover, our proposal can further integrate with the composite scheme to enhance the gate robustness, which is verified by numerical simulations. Therefore, our scheme provides a promising way towards practical realization of high-fidelity and robust nonadiabatic geometric quantum gates.