Reviewerly/exHarmony huggingface dataset:
DIY Graphics Tab: A Cost-Effective Alternative to Graphics Tablet for Educators

Everyday, more and more people are turning to online learning, which has altered our traditional classroom method. Recording lectures has always been a normal task for online educators, and it has lately become even more important during the epidemic because actual lessons are still being postponed in several countries. When recording lectures, a graphics tablet is a great substitute for a whiteboard because of its portability and ability to interface with computers. This graphic tablet, however, is too expensive for the majority of instructors. In this paper, we propose a computer vision-based alternative to the graphics tablet for instructors and educators, which functions largely in the same way as a graphic tablet but just requires a pen, paper, and a laptop's webcam. We call it "Do-It-Yourself Graphics Tab" or "DIY Graphics Tab". Our system receives a sequence of images of a person's writing on paper acquired by a camera as input and outputs the screen containing the contents of the writing from the paper. The task is not straightforward since there are many obstacles such as occlusion due to the person's hand, random movement of the paper, poor lighting condition, perspective distortion due to the angle of view, etc. A pipeline is used to route the input recording through our system, which conducts instance segmentation and preprocessing before generating the appropriate output. We also conducted user experience evaluations from the teachers and students, and their responses are examined in this paper.
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations

Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain specialists must aggregate and extract information from thousands of publications, primarily of randomised controlled trial (RCT) results, into evidence tables. This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: \textit{named entity recognition}, which identifies key entities within text, such as drug names, and \textit{relation extraction}, which maps their relationships for separating them into ordered tuples. We focus on the automatic tabulation of sentences from published RCT abstracts that report the results of the study outcomes. Two deep neural net models were developed as part of a joint extraction pipeline, using the principles of transfer learning and transformer-based language representations. To train and test these models, a new gold-standard corpus was developed, comprising almost 600 result sentences from six disease areas. This approach demonstrated significant advantages, with our system performing well across multiple natural language processing tasks and disease areas, as well as in generalising to disease domains unseen during training. Furthermore, we show these results were achievable through training our models on as few as 200 example sentences. The final system is a proof of concept that the generation of evidence tables can be semi-automated, representing a step towards fully automating systematic reviews.
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance concerning state-of-the-art techniques.
Minimal embedding of the Standard Model into intersecting D-brane  configurations with a bulk leptonic $U(1)$

It has been recently shown that the discrepancy between the theoretical and experimental values of the anomalous magnetic moment of the muon can be fully accommodated by considering the contribution of few Kaluza-Klein (KK) states of the gauged lepton number with masses lighter than the LEP energy, consistently with present experimental limits. In this article, we construct the minimal embedding of the Standard Model (SM) into D-brane configurations with a gauged lepton number. In order to give rise to such KK modes, the lepton number gauge boson must live on an abelian $U(1)_L$ brane extended along at least one "large" extra dimension in the bulk, with a compactification scale $M_L\sim\mathcal{O}(10-10^2~{\rm GeV})$ for a string scale $M_s\sim 10~{\rm TeV}$. As a consequence, $U(1)_L$ cannot participate to the hypercharge linear combination. We show that the minimal realisation of this framework contains five stacks of branes: the SM color $U(3)_c$, weak $U(2)_w$ and abelian $U(1)$ stacks extended effectively only in four dimensions, the bulk $U(1)_L$, as well as a fifth $U(1)^{'}$ brane. With these five abelian factors, one finds besides the hypercharge a second anomaly-free linear combination which does not couple to the SM spectrum, both in the non-supersymmetric case as well as in the minimal supersymmetric extension of the model. It is also shown how the right-handed neutrino can be implemented in the spectrum, and how fermions arising from the two non-SM branes and coupled to the SM through the $U(1)_L$ KK modes can provide Dark Matter candidates. Finally, the possibility of breaking Lepton Flavour Universality is studied by replacing $U(1)_L$ with a brane gauging only the muonic lepton number, avoiding most experimental constraints and enlarging the parameter space for explaining the discrepancy on the muon magnetic moment.
Spectral Complexity-scaled Generalization Bound of Complex-valued Neural Networks

Complex-valued neural networks (CVNNs) have been widely applied to various fields, especially signal processing and image recognition. However, few works focus on the generalization of CVNNs, albeit it is vital to ensure the performance of CVNNs on unseen data. This paper is the first work that proves a generalization bound for the complex-valued neural network. The bound scales with the spectral complexity, the dominant factor of which is the spectral norm product of weight matrices. Further, our work provides a generalization bound for CVNNs when training data is sequential, which is also affected by the spectral complexity. Theoretically, these bounds are derived via Maurey Sparsification Lemma and Dudley Entropy Integral. Empirically, we conduct experiments by training complex-valued convolutional neural networks on different datasets: MNIST, FashionMNIST, CIFAR-10, CIFAR-100, Tiny ImageNet, and IMDB. Spearman's rank-order correlation coefficients and the corresponding p values on these datasets give strong proof that the spectral complexity of the network, measured by the weight matrices spectral norm product, has a statistically significant correlation with the generalization ability.
Triangle Attack: A Query-efficient Decision-based Adversarial Attack

Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label. Great efforts have been made recently to decrease the number of queries; however, existing decision-based attacks still require thousands of queries in order to generate good quality adversarial examples. In this work, we find that a benign sample, the current and the next adversarial examples can naturally construct a triangle in a subspace for any iterative attacks. Based on the law of sines, we propose a novel Triangle Attack (TA) to optimize the perturbation by utilizing the geometric information that the longer side is always opposite the larger angle in any triangle. However, directly applying such information on the input image is ineffective because it cannot thoroughly explore the neighborhood of the input sample in the high dimensional space. To address this issue, TA optimizes the perturbation in the low frequency space for effective dimensionality reduction owing to the generality of such geometric property. Extensive evaluations on ImageNet dataset show that TA achieves a much higher attack success rate within 1,000 queries and needs a much less number of queries to achieve the same attack success rate under various perturbation budgets than existing decision-based attacks. With such high efficiency, we further validate the applicability of TA on real-world API, i.e., Tencent Cloud API.
Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning

In still image human action recognition, existing studies have mainly leveraged extra bounding box information along with class labels to mitigate the lack of temporal information in still images; however, preparing extra data with manual annotation is time-consuming and also prone to human errors. Moreover, the existing studies have not addressed action recognition with long-tailed distribution. In this paper, we propose a two-phase multi-expert classification method for human action recognition to cope with long-tailed distribution by means of super-class learning and without any extra information. To choose the best configuration for each super-class and characterize inter-class dependency between different action classes, we propose a novel Graph-Based Class Selection (GCS) algorithm. In the proposed approach, a coarse-grained phase selects the most relevant fine-grained experts. Then, the fine-grained experts encode the intricate details within each super-class so that the inter-class variation increases. Extensive experimental evaluations are conducted on various public human action recognition datasets, including Stanford40, Pascal VOC 2012 Action, BU101+, and IHAR datasets. The experimental results demonstrate that the proposed method yields promising improvements. To be more specific, in IHAR, Sanford40, Pascal VOC 2012 Action, and BU101+ benchmarks, the proposed approach outperforms the state-of-the-art studies by 8.92%, 0.41%, 0.66%, and 2.11 % with much less computational cost and without any auxiliary annotation information. Besides, it is proven that in addressing action recognition with long-tailed distribution, the proposed method outperforms its counterparts by a significant margin.
Generation and entanglement study of N-mode single photon perfect  W-state

We propose schemes to generate N-mode single photon perfect W-state and derive entanglement conditions to detect the entanglement of all generalized N-mode single photon perfect W-states. These states are suitable for perfect teleportation and superdense coding over other maximally entangled states belonging to W-class. Based on the evolution of single photon wave function in scalable integrated photonic lattices, we present schemes for the preparation of N-mode single photon perfect W-state at desired propagation distance. The integrated waveguide structures can precisely be fabricated and offer low photon propagation losses. We consider both planar and ring type waveguide structures for generation of the N-mode single photon perfect W-state. We derive set of generalized entanglement detection conditions using sum uncertainty relations of generalized su(2) algebra operators. We show that any given genuinely entangled N-mode single photon state is a squeezed state of a specific su(2) algebra operator and can be expressed as superposition of a pair of generalized N-mode single photon perfect W-states which are eigenstates of that specific su(2) algebra operator. We further prove that the squeezed states of an su(2) algebra operator satisfy the entanglement condition obtained using that operator and hence the usefulness of the proposed set of entanglement conditions to detect the entanglement of genuinely entangled single photon states. Finally, we propose an experimental scheme to verify the entanglement of generalized N-mode single photon perfect W-states using an integrated photonic circuit that consists of directional couplers and phase shifters and show that the same photonic circuit can also be used to generate generalized N-mode single photon perfect W-states.
User-Driven Support for Visualization Prototyping in D3

Templates have emerged as an effective approach to simplifying the visualization design and programming process. For example, they enable users to quickly generate multiple visualization designs even when using complex toolkits like D3. However, these templates are often treated as rigid artifacts that respond poorly to changes made outside of the template's established parameters, limiting user creativity. Preserving the user's creative flow requires a more dynamic approach to template-based visualization design, where tools can respond gracefully to users' edits when they modify templates in unexpected ways. In this paper, we leverage the structural similarities revealed by templates to design resilient support features for prototyping D3 visualizations: recommendations to suggest complementary interactions for a user's D3 program; and code augmentation to implement recommended interactions with a single click, even when users deviate from pre-defined templates. We demonstrate the utility of these features in Mirny, a d design-focused prototyping environment for D3. In a user study with 20 D3 users, we find that these automated features enable participants to prototype their design ideas with significantly fewer programming iterations. We also characterize key modification strategies used by participants to customize D3 templates. Informed by our findings and participants' feedback, we discuss the key implications of the use of templates for interleaving visualization programming and design.
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography

In this research, we extend the universal reinforcement learning (URL) agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form the tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. Instead of Turing machines, we estimate the cost metrics on a high-level language to allow realistic experimentation. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework is termed Quantum Knowledge Seeking Agent (QKSA). Despite its importance, few quantum reinforcement learning models exist in contrast to the current thrust in quantum machine learning. QKSA is the first proposal for a framework that resembles the classical URL models. Similar to how AIXI-tl is a resource-bounded active version of Solomonoff universal induction, QKSA is a resource-bounded participatory observer framework to the recently proposed algorithmic information-based reconstruction of quantum mechanics. QKSA can be applied for simulating and studying aspects of quantum information theory. Specifically, we demonstrate that it can be used to accelerate quantum variational algorithms which include tomographic reconstruction as its integral subroutine.
Hidden variables in Mermin GHZ machine with quantum assistance

Three experiments, with an IBM superconducting quantum computer, are presented, where the setting combinations on a three qubit GHZ(like) state were selected by two additional assistant qubits. The average of the polynomial of Mermin for the three entangled qubits was calculated; the results showed violation of the inequality of Mermin. However, given that the assistant qubits selected, imposed and informed the type of settings, it was possible to interpret the results in terms of arranged relations among hidden variables of the assistants and the entanglement BEFORE each shot; the hidden variables may or may not be local depending on the way the qubits were initialized.
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN

In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propose a novel unsupervised deep learning approach to improve 3-D US image quality. In particular, using {\em unmatched} high-quality 2-D US images as a reference, we trained a recently proposed switchable CycleGAN architecture so that every mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to the switchable architecture, our network can also provide real-time control of image enhancement level based on user preference, which is ideal for a user-centric scanner setup. Extensive experiments with clinical evaluation confirm that our method offers significantly improved image quality as well user-friendly flexibility.
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?

This paper does not contain technical novelty but introduces our key discoveries in a data generation protocol, a database and insights. We aim to address the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasible. To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data. Specifically, we discover three types of Action Units (AUs) that can constitute trainable MiEs. These AUs come from real-world MiEs, early frames of macro-expression videos, and the relationship between AUs and expression categories defined by human expert knowledge. With these AUs, our protocol then employs large numbers of face images of various identities and an off-the-shelf face generator for MiE synthesis, yielding the MiE-X dataset. MiE recognition models are trained or pre-trained on MiE-X and evaluated on real-world test sets, where very competitive accuracy is obtained. Experimental results not only validate the effectiveness of the discovered AUs and MiE-X dataset but also reveal some interesting properties of MiEs: they generalize across faces, are close to early-stage macro-expressions, and can be manually defined.
Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning

Reinforcement Learning (RL) based solutions are being adopted in a variety of domains including robotics, health care and industrial automation. Most focus is given to when these solutions work well, but they fail when presented with out of distribution inputs. RL policies share the same faults as most machine learning models. Out of distribution detection for RL is generally not well covered in the literature, and there is a lack of benchmarks for this task. In this work we propose a benchmark to evaluate OOD detection methods in a Reinforcement Learning setting, by modifying the physical parameters of non-visual standard environments or corrupting the state observation for visual environments. We discuss ways to generate custom RL environments that can produce OOD data, and evaluate three uncertainty methods for the OOD detection task. Our results show that ensemble methods have the best OOD detection performance with a lower standard deviation across multiple environments.
Spectral bounds for the quantum chromatic number of quantum graphs

Quantum graphs are an operator space generalization of classical graphs that have emerged in different branches of mathematics including operator theory, non-commutative topology and quantum information theory. In this paper, we obtain lower bounds for the classical and quantum chromatic number of a quantum graph using eigenvalues of the quantum adjacency matrix. In particular, we prove a quantum generalization of Hoffman's bound and introduce quantum analogues for the edge number, Laplacian and signless Laplacian. We generalize all the spectral bounds given by Elphick and Wocjan (2019) to the quantum graph setting and demonstrate the tightness of these bounds in the case of complete quantum graphs. Our results are achieved using techniques from linear algebra and a combinatorial definition of quantum graph coloring, which is obtained from the winning strategies of a quantum-to-classical nonlocal graph coloring game, given by M. Brannan, P. Ganesan and S. Harris (2020).
Polarization-path-frequency entanglement using interferometry and frequency shifters

Higher dimensional Hilbert space along with ability to control multiple degrees of freedom of photon and entangle them has enabled new quantum protocols for various quantum information processing applications. Here, we propose a scheme to generate and control polarization-path frequency entanglement using the operative elements required to implement a polarization-controlled quantum walk in the path(position) space and frequency domain. Hyperentangled states manifests in the controlled dynamics using an interferometric setup where half-wave plates, beam-splitters and frequency shifters such as those based on the electro-optic effect are used to manipulate the polarization, path and frequency degrees of freedom respectively. The emphasis is on utilizing the polarization to influence the movement to a specific value in the frequency and position space. Negativity between the subspaces is calculated to demonstrate the controllability of the entanglement between them. Progress reported with experimental demonstration of realization of quantum walk using quantum states of light makes quantum walks a practical approach to generate hyperentangled states.