Reviewerly/exHarmony huggingface dataset:
Covert Communication in Autoencoder Wireless Systems

Hiding the wireless communication by transmitter Alice to intended receiver Bob from a capable and attentive adversary Willie has been widely studied under the moniker "covert communications". However, when such covert communication is done in the presence of allowable system communications, there has been little study of both hiding the signal and preserving the performance of those allowable communications. Here, by treating Alice, Bob, and Willie as a generator, decoder, and discriminator neural network, we perform joint training in an adversarial setting to yield a covert communication scheme that can be added to any normal autoencoder. The method does not depend on the characteristics of the cover signal or the type of channel and it is developed for both single-user and multi-user systems. Numerical results indicate that we are able to establish a reliable undetectable channel between Alice and Bob, regardless of the cover signal or type of fading, and that the signal causes almost no disturbance to the ongoing normal operation of the system.
Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection

Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resource-constrained vehicles. One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations. To address this issue, we propose an adaptive inference framework called Ada3D, which focuses on exploiting the input-level spatial redundancy. Ada3D adaptively filters the redundant input, guided by a lightweight importance predictor and the unique properties of the Lidar point cloud. Additionally, we utilize the BEV features' intrinsic sparsity by introducing the Sparsity Preserving Batch Normalization. With Ada3D, we achieve 40% reduction for 3D voxels and decrease the density of 2D BEV feature maps from 100% to 20% without sacrificing accuracy. Ada3D reduces the model computational and memory cost by 5x, and achieves 1.52x/1.45x end-to-end GPU latency and 1.5x/4.5x GPU peak memory optimization for the 3D and 2D backbone respectively.
Phase Transitions of Structured Codes of Graphs

We consider the symmetric difference of two graphs on the same vertex set $[n]$, which is the graph on $[n]$ whose edge set consists of all edges that belong to exactly one of the two graphs. Let $\mathcal{F}$ be a class of graphs, and let $M_{\mathcal{F}}(n)$ denote the maximum possible cardinality of a family $\mathcal{G}$ of graphs on $[n]$ such that the symmetric difference of any two members in $\mathcal{G}$ belongs to $\mathcal{F}$. These concepts are recently investigated by Alon, Gujgiczer, K\"{o}rner, Milojevi\'{c}, and Simonyi, with the aim of providing a new graphic approach to coding theory. In particular, $M_{\mathcal{F}}(n)$ denotes the maximum possible size of this code. Existing results show that as the graph class $\mathcal{F}$ changes, $M_{\mathcal{F}}(n)$ can vary from $n$ to $2^{(1+o(1))\binom{n}{2}}$. We study several phase transition problems related to $M_{\mathcal{F}}(n)$ in general settings and present a partial solution to a recent problem posed by Alon et. al.
Mixed-state additivity properties of magic monotones based on quantum relative entropies for single-qubit states and beyond

We prove that the stabilizer fidelity is multiplicative for the tensor product of an arbitrary number of single-qubit states. We also show that the relative entropy of magic becomes additive if all the single-qubit states but one belong to a symmetry axis of the stabilizer octahedron. We extend the latter results to include all the $\alpha$-$z$ R\'enyi relative entropy of magic. This allows us to identify a continuous set of magic monotones that are additive for single-qubit states. We also show that all the monotones mentioned above are additive for several standard two and three-qubit states subject to depolarizing noise. Finally, we obtain closed-form expressions for several states and tighter lower bounds for the overhead of probabilistic one-shot magic state distillation.
CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation

Automatic diagnosis (AD), a critical application of AI in healthcare, employs machine learning techniques to assist doctors in gathering patient symptom information for precise disease diagnosis. The Transformer-based method utilizes an input symptom sequence, predicts itself through auto-regression, and employs the hidden state of the final symptom to determine the disease. Despite its simplicity and superior performance demonstrated, a decline in disease diagnosis accuracy is observed caused by 1) a mismatch between symptoms observed during training and generation, and 2) the effect of different symptom orders on disease prediction. To address the above obstacles, we introduce the CoAD, a novel disease and symptom collaborative generation framework, which incorporates several key innovations to improve AD: 1) aligning sentence-level disease labels with multiple possible symptom inquiry steps to bridge the gap between training and generation; 2) expanding symptom labels for each sub-sequence of symptoms to enhance annotation and eliminate the effect of symptom order; 3) developing a repeated symptom input schema to effectively and efficiently learn the expanded disease and symptom labels. We evaluate the CoAD framework using four datasets, including three public and one private, and demonstrate that it achieves an average 2.3% improvement over previous state-of-the-art results in automatic disease diagnosis. For reproducibility, we release the code and data at https://github.com/KwanWaiChung/coad.
Influence of the Commutator Properties of Hamiltonians on the Robustness of Quantum Circuits

We have proved new estimates for the coherent control errors of quantum circuits used in quantum computing. These estimates essentially take into account the commutator properties of the Hamiltonians and are based on the formulas of the commutator calculus.
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML

Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe that GES may not always be optimal, as it performs a simple deterministic greedy search. In this work, we introduce two novel population-based ensemble selection methods, QO-ES and QDO-ES, and compare them to GES. While QO-ES optimises solely for predictive performance, QDO-ES also considers the diversity of ensembles within the population, maintaining a diverse set of well-performing ensembles during optimisation based on ideas of quality diversity optimisation. The methods are evaluated using 71 classification datasets from the AutoML benchmark, demonstrating that QO-ES and QDO-ES often outrank GES, albeit only statistically significant on validation data. Our results further suggest that diversity can be beneficial for post hoc ensembling but also increases the risk of overfitting.
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning

We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development. We utilize LiDAR to guide the data selection and training of monocular 3D detectors without introducing any overhead in the inference phase. During training, we leverage the LiDAR teacher, monocular student cross-modal framework from semi-supervised learning to distill information from unlabeled data as pseudo-labels. To handle the differences in sensor characteristics, we propose a data noise-based weighting mechanism to reduce the effect of propagating noise from LiDAR modality to monocular. For selecting which samples to label to improve the model performance, we propose a sensor consistency-based selection score that is also coherent with the training objective. Extensive experimental results on KITTI and Waymo datasets verify the effectiveness of our proposed framework. In particular, our selection strategy consistently outperforms state-of-the-art active learning baselines, yielding up to 17% better saving rate in labeling costs. Our training strategy attains the top place in KITTI 3D and birds-eye-view (BEV) monocular object detection official benchmarks by improving the BEV Average Precision (AP) by 2.02.
A Privacy-Preserving Blockchain-based E-voting System

Within a modern democratic nation, elections play a significant role in the nation's functioning. However, with the existing infrastructure for conducting elections using Electronic Voting Systems (EVMs), many loopholes exist, which illegitimate entities might leverage to cast false votes or even tamper with the EVMs after the voting session is complete. The need of the hour is to introduce a robust, auditable, transparent, and tamper-proof e-voting system, enabling a more reliable and fair election process. To address such concerns, we propose a novel solution for blockchain-based e-voting, focusing on the security and privacy aspects of the e-voting process. We consider the security risks and loopholes and aim to preserve the anonymity of the voters while ensuring that illegitimate votes are properly handled. Additionally, we develop a prototype as a proof of concept using the Ethereum blockchain platform. Finally, we perform experiments to demonstrate the performance of the system.
Quantum abstract machines without circuits: the need for higher algorithmic expressiveness

Existing abstract models of quantum computation make reference to circuit elements, much in contrast to their classical counterparts. Circuits, as a model of computation, substantially limit algorithmic expression and obscure high-level connections between problems and quantum resources. It is argued here that new models are needed to achieve high-level algorithmic expressiveness that allow composable procedural abstractions to manifest, leading to the development of instructions in the sense usually understood in high-level programming languages. Doing so appears essential to the discovery of new quantum algorithms, and deeper understanding of how quantum resources compose into useful patterns, or \emph{quantum motifs}. To achieve this, stronger investment in the intersection between higher-algebra, mathematical physics and quantum science is required to cope with future challenges brought forth by \textit{very large quantum scale integration}.
Active Learning for Object Detection with Non-Redundant Informative Sampling

Curating an informative and representative dataset is essential for enhancing the performance of 2D object detectors. We present a novel active learning sampling strategy that addresses both the informativeness and diversity of the selections. Our strategy integrates uncertainty and diversity-based selection principles into a joint selection objective by measuring the collective information score of the selected samples. Specifically, our proposed NORIS algorithm quantifies the impact of training with a sample on the informativeness of other similar samples. By exclusively selecting samples that are simultaneously informative and distant from other highly informative samples, we effectively avoid redundancy while maintaining a high level of informativeness. Moreover, instead of utilizing whole image features to calculate distances between samples, we leverage features extracted from detected object regions within images to define object features. This allows us to construct a dataset encompassing diverse object types, shapes, and angles. Extensive experiments on object detection and image classification tasks demonstrate the effectiveness of our strategy over the state-of-the-art baselines. Specifically, our selection strategy achieves a 20% and 30% reduction in labeling costs compared to random selection for PASCAL-VOC and KITTI, respectively.
ENERGY EFFICIENCY ANALYSIS METHODS OF REFRIGERATION PLANTS

Cold supply systems occupy a leading place in many industries. Their improvement is currently associated with two main areas: energy saving and ecology.&#x0D; The problems of energy saving are paid great attention both at the state level (Federal Law No. 261-FZ "On Energy Saving and on Improving Energy Efficiency and on Amending Certain Legislative Acts of the Russian Federation") and by the owners of refrigeration equipment.&#x0D; The costs of electricity consumed by cold supply systems make up a significant part both in the total balance of energy consumption of the enterprise (for catering enterprises it ranges from 48% to 60%) and in the country's total energy consumption (for example, the share of energy consumption of air conditioning systems in Europe ranges from 2% to 6%).&#x0D; Along with this, the ratification by the Russian Federation of Montreal (Resolution No. 539 of the Government of the Russian Federation of 27.08.2005) and the Kyoto (FZ 128-FZ of November 4, 2004) Protocols, the entry into force of European Regulations 517/2014 governing the decommissioning of refrigerants with global warming potential GWP above 2500, for example, the R404A and R507A currently in active use, as well as the adoption by the Russian Federation of new environmental legislation in connection with the signing of the Paris Agreement, leads to the need to use new refrigerants, which are not always more efficient than traditionally used solutions and require changes to the technological scheme of refrigeration systems.&#x0D; These factors lead to the need to improve efficiency calculation methods based on classical methods of thermodynamic analysis.
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures. Current methods either perceive shape patterns using only 3D coordinates or import extra images with well-calibrated intrinsic parameters to guide the geometry estimation of the missing parts. However, these approaches do not always fully leverage the cross-modal self-structures available for accurate and high-quality point cloud completion. To this end, we first design a Self-view Fusion Network that leverages multiple-view depth image information to observe incomplete self-shape and generate a compact global shape. To reveal highly detailed structures, we then introduce a refinement module, called Self-structure Dual-generator, in which we incorporate learned shape priors and geometric self-similarities for producing new points. By perceiving the incompleteness of each point, the dual-path design disentangles refinement strategies conditioned on the structural type of each point. SVDFormer absorbs the wisdom of self-structures, avoiding any additional paired information such as color images with precisely calibrated camera intrinsic parameters. Comprehensive experiments indicate that our method achieves state-of-the-art performance on widely-used benchmarks. Code will be available at https://github.com/czvvd/SVDFormer.
Optimal Ramsey interferometry with echo protocols based on one-axis twisting

We study a variational class of generalised Ramsey protocols that include two one-axis twisting (OAT) operations, one performed before the phase imprint and the other after. In this framework, we optimise the axes of the signal imprint, the OAT interactions, and the direction of the final projective measurement. We distinguish between protocols that exhibit symmetric or antisymmetric dependencies of the spin projection signal on the measured phase. Our results show that the quantum Fisher information, which sets the limits on the sensitivity achievable with a given uniaxially twisted input state, can be saturated within our class of variational protocols for almost all initial twist strengths. By incorporating numerous protocols previously documented in the literature, our approach creates a unified framework for Ramsey echo protocols with OAT states and measurements.
METHOD FOR EVALUATING EFFICIENCY OF REFRIGERATION UNITS BASED ON SINGLE-STAGE COMPRESSION CYCLE

The development of automation and programming tools is one of the priority areas determined by the State Program "National Technology Initiative," which requires the development of methods on the basis of which algorithms will be built.At the same time, energy saving is one of the directions for the development of refrigeration equipment. This issue is paid attention both at the state level (Federal Law No. 261-FZ "On Energy Saving and Increasing Energy Efficiency and Amending Certain Legislative Acts of the Russian Federation") and by the users of refrigeration equipment themselves, since the share of electricity consumed by refrigeration equipment is a significant part of the total energy balance of a particular enterprise. For example, the share of energy consumption of air conditioning systems in Europe ranges from 2% to 6%.The existing systems for monitoring the operating parameters of the refrigeration plant do not fully use their resource, since they are very often used only to identify emergencies and alarm them.This article describes the procedure for assessing the energy efficiency of refrigeration plants operating on a single-stage compression cycle with single throttling, which allows to evaluate both at the design stage and during the operation of the refrigeration plant, and allows to obtain a distribution of losses by elements and processes of the refrigeration plant.
Results on Counterfactual Invariance

In this paper we provide a theoretical analysis of counterfactual invariance. We present a variety of existing definitions, study how they relate to each other and what their graphical implications are. We then turn to the current major question surrounding counterfactual invariance, how does it relate to conditional independence? We show that whilst counterfactual invariance implies conditional independence, conditional independence does not give any implications about the degree or likelihood of satisfying counterfactual invariance. Furthermore, we show that for discrete causal models counterfactually invariant functions are often constrained to be functions of particular variables, or even constant.
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise

We study the problem of learning general (i.e., not necessarily homogeneous) halfspaces with Random Classification Noise under the Gaussian distribution. We establish nearly-matching algorithmic and Statistical Query (SQ) lower bound results revealing a surprising information-computation gap for this basic problem. Specifically, the sample complexity of this learning problem is $\widetilde{\Theta}(d/\epsilon)$, where $d$ is the dimension and $\epsilon$ is the excess error. Our positive result is a computationally efficient learning algorithm with sample complexity $\tilde{O}(d/\epsilon + d/(\max\{p, \epsilon\})^2)$, where $p$ quantifies the bias of the target halfspace. On the lower bound side, we show that any efficient SQ algorithm (or low-degree test) for the problem requires sample complexity at least $\Omega(d^{1/2}/(\max\{p, \epsilon\})^2)$. Our lower bound suggests that this quadratic dependence on $1/\epsilon$ is inherent for efficient algorithms.
A Nested U-Structure for Instrument Segmentation in Robotic Surgery

Robot-assisted surgery has made great progress with the development of medical imaging and robotics technology. Medical scene understanding can greatly improve surgical performance while the semantic segmentation of the robotic instrument is a key enabling technology for robot-assisted surgery. However, how to locate an instrument's position and estimate their pose in complex surgical environments is still a challenging fundamental problem. In this paper, pixel-wise instrument segmentation is investigated. The contributions of the paper are twofold: 1) We proposed a two-level nested U-structure model, which is an encoder-decoder architecture with skip-connections and each layer of the network structure adopts a U-structure instead of a simple superposition of convolutional layers. The model can capture more context information from multiple scales and better fuse the local and global information to achieve high-quality segmentation. 2) Experiments have been conducted to qualitatively and quantitatively show the performance of our approach on three segmentation tasks: the binary segmentation, the parts segmentation, and the type segmentation, respectively.
Effects of Academic Self-efficacy and Illusion of Competency of middle school students on Grade Prediction and Academic Achievement

이 연구는 학생들이 자신의 성적을 예측할 때 과신하는 경향이 있다는 것에 주목하여 자신의 학습능력에 대한 개인적 신념인 학업적 자기효능감이 성적예측 시 어떠한 영향을 미치는지를 분석하며, 역량의 착각과 학업적 자기효능감이 실제 학업성과에 미치는 영향을 확인하는 것을 목적으로 하였다. 연구결과, 중학생들에게서도 자신의 성적을 예측할 때 과신하는 경향이 동일하게 확인되었으며, 실제성적을 기준으로 사분위로 나눌 경우 더닝-크루거 효과도 나타났다. 회귀분석 결과 학업적 자기효능감은 성적예측에 유의한 정적영향이 나타나, 학업적 자기효능감은 학습판단에 영향을 미치는 요인 중 하나로서 학생들의 성적예측을 과신하게 하는 메커니즘에 기여하고 있음을 확인할 수 있었다. 역량의 착각은 실제성적에 유의한 부적영향이 있으며, 학업적 자기효능감이 미치는 영향보다 훨씬 강력하여 학업성취 향상을 위해 역량의 착각을 극복할 수 있는 교육적인 방안을 모색하는 것이 필요시 된다. 실제성적과 예상성적 간 차이로 나타나는 역량의 착각이 음의 값인지 양의 값인지에 따라 두 그룹으로 나눌 경우, 실제성적에 대한 역량의 착각의 회귀모형은 자신의 성적을 과대평가하는 그룹에게서만 유의하였으며 이는 역량의 착각을 깨뜨리기 위한 교육적 개입은 주로 학업성취가 낮은 학생들에게 초점을 맞출 필요가 있다는 것을 시사할 수 있다. 역량의 착각을 줄이는 방안 중 하나로서 학습전략에 대한 명시적인 교육을 도입하되 특히 인출연습의 유익에 대해 알리고 활용할 수 있도록 권장할 것을 제안하였다.The purpose of this study that focused on students' tendency to overconfidence in predicting their grades was to analyze how academic self-efficacy, a personal belief in learning ability, affects performance prediction, and to investigate the effect of illusion of competence and academic self-efficacy on actual academic achievement. As a result of the study, it was confirmed that middle school students also have the same tendency to overconfidence when predicting their grades, and the Dunning-Kruger effect was also found when divided into quartiles based on actual grades. As a result of regression analysis, it was confirmed that academic self-efficacy had a significant positive effect on grade prediction, and academic self-efficacy was one of the factors influencing judgment of learning, contributing to the mechanism that overconfidence students' grade prediction. The illusion of competency has a significant negative effect on actual grades, and it is much stronger than the impact of academic self-efficacy, so it is necessary to find educational ways to overcome the illusion of competency to improve academic achievement. When divided into two groups according to whether the illusion of competency is positive or negative, the regression model of the illusion of competency for actual performance was significant only in the group that overestimated their performance. This may suggest that educational intervention to break the illusion of competency needs to focus mainly on students with low academic achievement. As one of the ways to reduce the illusion of competency, it was proposed to introduce explicit education on learning strategies, but to encourage them to inform and utilize the benefits of retrieval practice