Reviewerly/exHarmony huggingface dataset:
Bipartite and tripartite steering by a nonlinear medium in a cavity

Nonlocal quantum correlations are important for potential applications in quantum optics and quantum information. Multipartite quantum correlations are relevant to access to high-dimensional systems such as qudits. There are also different proposals for the generation of quantum steering based on nonlinear systems and cavities. Here, we consider the Hamiltonian of a triple photon parametric down-conversion process to investigate the regions where bipartite and tripartite steering are generated. In this model, three beams interact with a nonlinear medium inside a cavity through the process of parametric down-conversion, creating three output fields. The positive P representation is used to analyze this system and steady-state solutions are obtained. We calculated the intracavity fluctuation spectra to analyze steering in the frequency domain and used different criteria to certify this quantum correlation. We certified bipartite steering for the three output fields from the cavity. In the case of full tripartite two-way steering inseparability, it is also present for all the bipartitions under consideration. Our results show regimes where bipartite and tripartite steering is certified for this model.
A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms

Purpose To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015–June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45–65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the “low” POM group had malignant lesions, while in the “high” POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs. Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license. See also the commentary by McDonald and Conant in this issue.
On the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$, which is generated by the family $\mathscr{F}_n$ of finite bounded intervals of $\omega$

We study the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$, which is introduced in the paper [Visnyk Lviv Univ. Ser. Mech.-Mat. 2020, 90, 5-19 (in Ukrainian)], in the case when the ${\omega}$-closed family $\mathscr{F}_n$ generated by the set $\{0,1,\ldots,n\}$. We show that the Green relations $\mathscr{D}$ and $\mathscr{J}$ coincide in $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$, the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ is isomorphic to the semigroup $\mathscr{I}_\omega^{n+1}(\overrightarrow{\mathrm{conv}})$ of partial convex order isomorphisms of $(\omega,\leqslant)$ of the rank $\leqslant n+1$, and $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ admits only Rees congruences. Also, we study shift-continuous topologies on the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$. In particular, we prove that for any shift-continuous $T_1$-topology $\tau$ on the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ every non-zero element of $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ is an isolated point of $(\boldsymbol{B}_{\omega}^{\mathscr{F}_n},\tau)$, $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ admits the unique compact shift-continuous $T_1$-topology, and every $\omega_{\mathfrak{d}}$-compact shift-continuous $T_1$-topology is compact. We describe the closure of the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ in a Hausdorff semitopological semigroup and prove the criterium when a topological inverse semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}_n}$ is $H$-closed in the class of Hausdorff topological semigroups.
Post-Quantum Signatures in DNSSEC via Request-Based Fragmentation

The Domain Name System Security Extensions (DNSSEC) provide authentication of DNS responses using digital signatures. DNS operates primarily over UDP, which leads to several constraints: notably, DNS packets should be at most 1232 bytes long to avoid problems during transmission. Larger DNS responses would either need to be fragmented into several UDP responses or the request would need to be repeated over TCP, neither of which is sufficiently reliable in today’s DNS ecosystem. While RSA or elliptic curve digital signatures are sufficiently small to avoid this problem, even for DNSSEC packets containing both a public key and a signature, this problem is unavoidable when considering the larger sizes of post-quantum schemes. We propose ARRF, a method of fragmenting DNS resource records at the application layer (rather than the transport layer) that is request-based, meaning the initial response contains a truncated fragment and then the requester sends follow-up requests for the remaining fragments. Using request-based fragmentation avoids problems identified for several previously proposed—and rejected—application-level DNS fragmentation techniques. We implement our approach and evaluate its performance in a simulated network when used for the three post-quantum digital signature schemes selected by NIST for standardization (Falcon, Dilithium, and SPHINCS+) at the 128-bit security level. Our experiments show that our request-based fragmentation approach provides substantially lower resolution times compared to standard DNS over UDP with TCP fallback, for all the tested post-quantum algorithms, and with less data transmitted in the case of both Falcon and Dilithium. Furthermore, our request-based fragmentation design can be implemented relatively easily: our implementation is in fact a small daemon that can sit in front of a DNS name server or resolver to fragment/reassemble transparently. As well, our request-based application-level fragmentation over UDP may avoid problems that arise on poorly configured network devices with other approaches for handling large DNS responses.
Toward a Flexible Metadata Pipeline for Fish Specimen Images

Flexible metadata pipelines are crucial for supporting the FAIR data principles. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000 digital fish specimen images, harvested from multiple data repositories and fish collections. The images and their associated metadata are being used for AI-related scientific research involving automated species identification, segmentation and trait extraction. The paper provides contextual background, followed by the presentation of a four-phased approach involving: 1. Assessment of the Problem, 2. Investigation of Solutions, 3. Implementation, and 4. Refinement. The work is part of the NSF Harnessing the Data Revolution, Biology Guided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute. An RDF graph prototype pipeline is presented, followed by a discussion of research implications and conclusion summarizing the results.
Design and Evaluating a Method Using Project Corpus for Inferring Software Description

Obsolete software developed in the past is gradually phased out over time. However, the source code of such software contains a wealth of information that can be re-purposed and possesses a high value as an asset. Thus, understanding the characteristics of existing software can aid in developing new software. In a previous study, we proposed a method for inferring the architecture of an existing system using a project corpus and conducted preliminary experiments to verify its feasibility. The findings revealed that the project corpus could be used to infer a system's purpose, functionality, and technology. In this present study, we confirm the validity of the project corpus from a perspective that was not examined in previous studies. We established three verification items and conducted an experiment in which we employed project corpus to infer the functionality of the system, the technology utilized in the system, and the architecture of the system. From the experiment results, we confirmed that the accuracy of the project's inference depends on two factors: first, that the project corpus accurately reflects the system's information, and second, the participants' familiarity with the words in the corpus.
Sentiment Analysis of Imbalanced Comment Texts Under the Framework of BiLSTM

This paper tries to improve the performance of imbalanced comment texts sentiment analysis by combining deep learning and class imbalanced learning methods. A sentiment analysis method for imbalanced comment texts based on BiLSTM framework is proposed. For the case of more negative samples than positive samples, when the degree of imbalance is low, the minority class samples are Adaptive Synthetic Sampling(ADASYN), and the CNN-BiLSTM model is proposed to realize sentiment classification by constructing Sigmoid. When the degree of imbalance is high, the samples of majority class are sampled multiple times until the original dataset is divided into multiple low imbalance datasets. Then, multiple groups of equalization ADASYN is carried out for the samples of minority class, and BiLSTM model is learned for each group of training data respectively. Finally, Ensemble learning is adopted to obtain the final sentiment classification results. Experimental results show that this paper method is superior to the traditional imbalanced comment texts sentiment analysis method.
Lightweight Mood Estimation Algorithm For Faces Under Partial Occlusion

The latest advancements in Machine Learning have led to impressive capabilities in distinguishing emotions from facial expressions, allowing computers and smart devices to accurately detect and interpret human emotions through computer vision. While a lot of work has been conducted on understanding human expressions by utilizing visual information, most of them assume that the faces are fully exposed. In this work, we present the implementation of a lightweight mood estimation deep learning model in the presence of partial occlusion where the user is wearing eyewear equipment that completely covers the area around their eyes. Examples of such eyewear are glasses for visually impaired people or a head-mounted display in a virtual reality setting. Rather than collecting a new dataset of images illustrating individuals wearing such eyewear or virtual reality equipment, we utilized a dataset based on a previous work of ours, where the occlusion arising from such headsets was obtained through simulation. That way, we were able to make use of the transfer learning approach by fine-tuning an efficient model that was pre-trained on a typical Facial Expression Recognition task.
Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording

Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance.Despite the countless out-of-the-box applications, LLMs are still not reliable.A lot of work is being done to improve the factual accuracy, consistency, and ethical standards of these models through fine-tuning, prompting, and Reinforcement Learning with Human Feedback (RLHF), but no systematic analysis of the responses of these models to different categories of statements, or on their potential vulnerabilities to simple prompting changes is available.In this work, we analyze what confuses GPT-3: how the model responds to certain sensitive topics and what effects the prompt wording has on the model response.We find that GPT-3 correctly disagrees with obvious Conspiracies and Stereotypes but makes mistakes with common Misconceptions and Controversies.The model responses are inconsistent across prompts and settings, highlighting GPT-3's unreliability.
Visions of Control: The Head-Up Display, Perceptual Labor, and a Lesson for Augmented Reality

abstract: Traditional workplace studies focus on the bodily integration of familiar technologies like the desktop computer and smartphone. To better understand augmented reality (AR) technologies that provide new ways to perform daily tasks, this article turns to an overlooked yet critical forerunner of AR: the head-up display (HUD). Extensive archival research and oral histories show that the HUD caused pilots to depend on a single interface, curtailing their autonomy for integrating disparate information that could inform critical, often lethal decision-making processes. This history provides evidence that once a single device subsumes many functions of a work environment, adaptive articulation work can drive information workers to undertake increasingly insulated information practices. The HUD's isolating and insulating perceptual legacy suggests that AR will similarly train users in many contexts how to trust interactive data overlays in ways that preclude cross-checking alternative data sources that could inform decision-making processes.
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing

Emotion recognition in conversation, which aims to predict the emotion for all utterances, has attracted considerable research attention in recent years. It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels. In this paper, we propose a novel framework which mimics the thinking process when modeling these factors. Specifically, we first comprehend the conversational context with a history-oriented prompt to selectively gather information from predecessors of the target utterance. We then model the speaker's background with an experience-oriented prompt to retrieve the similar utterances from all conversations. We finally differentiate the subtle label semantics with a paraphrasing mechanism to elicit the intrinsic label related knowledge. We conducted extensive experiments on three benchmarks. The empirical results demonstrate the superiority of our proposed framework over the state-of-the-art baselines.
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation

Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation.
Sph2Pob: Boosting Object Detection on Spherical Images with Planar Oriented Boxes Methods

Object detection on panoramic/spherical images has been developed rapidly in the past few years, where IoU-calculator is a fundamental part of various detector components, i.e. Label Assignment, Loss and NMS. Due to the low efficiency and non-differentiability of spherical Unbiased IoU, spherical approximate IoU methods have been proposed recently. We find that the key of these approximate methods is to map spherical boxes to planar boxes. However, there exists two problems in these methods: (1) they do not eliminate the influence of panoramic image distortion; (2) they break the original pose between bounding boxes. They lead to the low accuracy of these methods. Taking the two problems into account, we propose a new sphere-plane boxes transform, called Sph2Pob. Based on the Sph2Pob, we propose (1) an differentiable IoU, Sph2Pob-IoU, for spherical boxes with low time-cost and high accuracy and (2) an agent Loss, Sph2Pob-Loss, for spherical detection with high flexibility and expansibility. Extensive experiments verify the effectiveness and generality of our approaches, and Sph2Pob-IoU and Sph2Pob-Loss together boost the performance of spherical detectors. The source code is available at https://github.com/AntXinyuan/sph2pob.
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a flexible model poisoning attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks.
Clustered-patch Element Connection for Few-shot Learning

Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections between support and query instances through embedding patch features to generate discriminative representations. However, we observe that there exists semantic mismatches (foreground/ background) among these local patches, because the location and size of the target object are not fixed. What is worse, these mismatches result in unreliable similarity confidences, and complex dense connection exacerbates the problem. According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem. The CEC layer leverages Patch Cluster and Element Connection operations to collect and establish reliable connections with high similarity patch features, respectively. Moreover, we propose a CECNet, including CEC layer based attention module and distance metric. The former is utilized to generate a more discriminative representation benefiting from the global clustered-patch features, and the latter is introduced to reliably measure the similarity between pair-features. Extensive experiments demonstrate that our CECNet outperforms the state-of-the-art methods on classification benchmark. Furthermore, our CEC approach can be extended into few-shot segmentation and detection tasks, which achieves competitive performances.
Part Aware Contrastive Learning for Self-Supervised Action Recognition

In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local body parts, such as legs or hands, which are advantageous for skeleton-based action recognition. This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations. To achieve this, a multi-head attention mask module is employed to learn the soft attention mask features from the skeletons, suppressing non-salient local features while accentuating local salient features, thereby bringing similar local features closer in the feature space. Additionally, ample contrastive pairs are generated by expanding contrastive pairs based on salient and non-salient features with global features, which guide the network to learn the semantic representations of the entire skeleton. Therefore, with the attention mask mechanism, SkeAttnCLR learns local features under different data augmentation views. The experiment results demonstrate that the inclusion of local feature similarity significantly enhances skeleton-based action representation. Our proposed SkeAttnCLR outperforms state-of-the-art methods on NTURGB+D, NTU120-RGB+D, and PKU-MMD datasets. The code and settings are available at this repository: https://github.com/GitHubOfHyl97/SkeAttnCLR.