Reviewerly/exHarmony huggingface dataset:
Examining and Combating Spurious Features under Distribution Shift

A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical risk over finite or biased datasets often results in models latching on to spurious correlations between the training input/output pairs that are not fundamental to the problem at hand. In this paper, we define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution (i.e. covariate shift), models can still pick up spurious features from their training data. Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations that occur in the data. To address this, we further propose to minimize the worst-case losses over a more flexible set of distributions that are defined on the joint distribution of groups and instances, instead of treating each group as a whole at optimization time. Through extensive experiments on one image and two language tasks, we show that our model is significantly more robust than comparable baselines under various partitions. Our code is available at this https URL.
Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations

This paper investigates the use of machine learning models to classify unhealthy online conversations containing one or more forms of subtler abuse, such as hostility, sarcasm, and generalization. We leveraged a public dataset of 44K online comments containing healthy and unhealthy comments labeled with seven forms of subtle toxicity. We were able to distinguish between these comments with a micro F1-score, macro F1-score, and ROC-AUC of 88.76%, 67.98%, and 0.71, respectively. Hostile comments were easier to detect than other types of unhealthy comments. We also conducted a sentiment analysis that revealed that most unhealthy comments were associated with a slight negative sentiment, with hostile comments being the most negative.
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality

Utilizing clinical texts in survival analysis is difficult because they are largely unstructured.Current automatic extraction models fail to capture textual information comprehensively since their labels are limited in scope.Furthermore, they typically require a large amount of data and high-quality expert annotations for training.In this work, we present a novel method of using BERT-based hidden layer representations of clinical texts as covariates for proportional hazards models to predict patient survival outcomes.We show that hidden layers yield notably more accurate predictions than predefined features, outperforming the previous baseline model by 5.7% on average across C-index and time-dependent AUC.We make our work publicly available at https://github.com/bionlplab/heart_failure_mortality.
Robot Path Planning Method Based on Genetic Algorithm

Abstract Intelligent mobile robots are robots that can automatically complete driving tasks without human intervention. Route planning is the main problem in robotics research. The goal is to find the lowest cost, conflict-free route from the known starting point of the obstacle route to the known end point. Genetic algorithms are based on the natural selection of populations and the random, repetitive and evolutionary processes of genetics. This is a very effective algorithm in the field of route planning research. In this article, I will first explain the design principles of mobile robots. After comparing the advantages and disadvantages of various routing methods, a genetic algorithm was chosen to solve the routing problem of mobile robots. Then, through detailed research on genetic algorithm, a solution to robot path programming based on genetic algorithm in static and dynamic environments is proposed. In the design of genetic operons, standardization, insertion and deletion of operons have been added to make up for the lack of basic operons. At the same time, the genetic algorithm is optimized through adaptive mutation and crossover rate. You can improve the algorithm by adding new operators and adaptive methods to solve the problem that the target point cannot be reached due to the local minima in the evolution process. Finally, in three static environments with different levels of complexity, simulation experiments are carried out and the simulation results are analyzed. The influence of different fitness parameters on the path planning results is discussed, and the dynamic path planning simulation is carried out. Through comparison with other methods, it can be found that in the same environment, the path planning method based on genetic algorithm saves at least 5% time than Dijkstra algorithm in search time.
Adversarially-Trained Nonnegative Matrix Factorization

We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices with enhanced generalization abilities. Extensive simulations on synthetic and benchmark datasets demonstrate the superior predictive performance on matrix completion tasks of our proposed method compared to state-of-the-art competitors, including other variants of adversarial nonnegative matrix factorization.
Revisiting Epistemic Logic with Names

This paper revisits the multi-agent epistemic logic presented in [10], where agents and sets of agents are replaced by abstract, intensional "names".We make three contributions.First, we study its model theory, providing adequate notions of bisimulation and frame morphisms, and use them to study the logic's expressive power and definability.Second, we show that the logic has a natural neighborhood semantics, which in turn allows to show that the axiomatization in [10] does not rely on possibly controversial introspective properties of knowledge.Finally, we extend the logic with common and distributed knowledge operators, and provide a sound and complete axiomatization for each of these extensions.These results together put the original epistemic logic with names in a more modern context and opens the door for a logical analysis of epistemic phenomena where group membership is uncertain or variable.In [10], Grove and Halpern studied a generalized version of multi-agents epistemic logic where the usual labels for agents and sets of agents are replaced by abstract names whose extension might vary from state to state. 1 Despite being interpreted in standard multi-agents epistemic models, the resulting language does away with the familiar K i modalities, and instead contains two families of epistemic operators: S n , standing for "someone with name n knows", and E n , standing for "everyone with name n knows".This generalization is conceptually important.The "names" that index the S n and the E n modalities can refer intensionally to both individuals and groups.Since these extensions are not fixed in a given model, the logic allows to study social-epistemic phenomena that involve uncertainty or variability in the agents' identities or group membership.These phenomena are pervasive.[19,10] already provide convincing examples for distributed systems.Massive coordinated actions or social movements, especially online, also provide contemporary cases [1,4], where for instance we refer to group labels like "Trump supporters" or "trolls" without knowing exactly who the members of these groups are or even failing to know whether we, ourselves, are members of those groups. 2The study in [10], however, focuses on the two modalities mentioned above, and in particular leaves aside notions of common and distributed knowledge.These notions are, however, central to theories of social conventions [16, 2] and collective action [25].[19] study a closely related notion of common knowledge, to which we come back briefly in Section 4, but do not provide an axiomatization.Distributed knowledge for intensional or indexical group names has been studied in [20], but in a more expressive language with explicit quantification.
The Philosophy of the Modern Age: The Issue of the Source of Meaningful Presence

A brief analysis of the philosophy of the Modern Age has allowed establishing the following. (1) The idea of philosophy, formulated by Plato, has hold sway in modern history. It corresponds to the demands of a new reality, which is revealed as a process. (2) In the context of a new reality, the idea of philosophy revealed previously unknown qualities. Martin Heidegger proved that the idea of philosophy does not need Mediators or any other external source. Instead, the unity of certain discourse and way of life has an inner source, which Heidegger coined the term “the appropriated clearing” (die ereignete Lichtung). This source reveals in the idea of philosophy the potency (????????, dúnamis), which under certain conditions is capable of transforming an individual discourse and a way of life independently.
Sample-efficient deep reinforcement learning with directed associative graph

Reinforcement learning can be modeled as markov decision process mathematically. In consequence, the interaction samples as well as the connection relation between them are two main types of information for learning. However, most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes. In this paper, in order to utilize more sample information, we propose another learning system based on directed associative graph (DAG). The DAG is built on all trajectories in real time, which includes the whole connection relation of all samples among all episodes. Through planning with directed edges on DAG, we offer another perspective to estimate state-action pair, especially for the unknowns to deep neural network (DNN) as well as episodic memory (EM). Mixed loss function is generated by the three learning systems (DNN, EM and DAG) to improve the efficiency of the parameter update in the proposed algorithm. We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments. Furthermore, the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.
Self-consistent state and measurement tomography with fewer measurements

We describe a technique for self-consistently characterizing both the quantum state of a single-qubit system, and the positive-operator-valued measure (POVM) that describes measurements on the system. The method works with only 10 measurements. We assume that a series of unitary transformations performed on the quantum state are fully known, while making minimal assumptions about both the density operator of the state and the POVM. The technique returns maximum-likely estimates of both the density operator and the POVM. To experimentally demonstrate the method, we perform reconstructions of over 300 state-measurement pairs, and we compare them to their expected density operators and POVMs. We find that 95% of the reconstructed POVMs have fidelities of 0.98 or greater, and 92% of the density operators have fidelities that are 0.98 or greater.
Autonomous learning of semantic segmentation from Internet images

Collecting a large amount of manually labeled training data is labor-intensive,thus often becomes the major bottleneckwhen applying semantic segmentation techniquesto real-world applications,especially for new categories where no labeled data is available.In this paper, we aim at solving the problem of “webly-supervised” semanticsegmentation relying purely on web searched images,where users only need to provide a single keyword for each target category.A major challenge in this task is the existence of label noisein web images.To deal with the label noise, we design a noise erasing networkthat is able to learn cross-image knowledge from credible attention regions in imagesin a mini-batch and thenerase those regions unrelated to the search keywords from the web images.With this network, our system can automatically generate high-quality`proxy ground truth', for training semantic segmentation models.Extensive experiments on the popular benchmark,i.e., PASCAL VOC 2012, show surprisingly good resultsin both our task (mIoU = $62.0%$)and the weakly-supervised setting (mIoU = $66.1%$).
Research on the effect of monitoring the individual learning status of students in English teaching on the improvement of teaching effect

In order to improve the effect of English teaching, this paper proposes a complex interactive collective behavior recognition method based on dynamic kernel density, which can more accurately identify classroom behavior patterns with complex interactions. Moreover, this paper proposes a dynamic crowd path planning method based on the emotional communication model. This method introduces the subjective factor of emotions in path planning, which can more realistically show the diversity of group path choices. At the same time, this paper combines the actual needs of English teaching to build a student status monitoring system based on group feature recognition, which can monitor the status of groups and individuals in real time. In addition, this paper introduces emotion algorithms to recognize student emotions, and combines action recognition to complete student state monitoring. Finally, this paper designs experiments to verify the performance of the system constructed in this paper. The research results show that the system constructed in this paper has certain effects and can provide theoretical reference for related research.
Simulation of the five-qubit quantum error correction code on superconducting qubits

Experimental realization of stabilizer-based quantum error correction (QEC) codes that would yield superior logical qubit performance is one of the formidable task for state-of-the-art quantum processors. A major obstacle towards realizing this goal is the large footprint of QEC codes, even those with a small distance. We propose a circuit based on the minimal distance-3 QEC code, which requires only 5 data qubits and 5 ancilla qubits, connected in a ring with iSWAP gates implemented between neighboring qubits. Using a density-matrix simulation, we show that, thanks to its smaller footprint, the proposed code has a lower logical error rate than Surface-17 for similar physical error rates. We also estimate the performance of a neural network-based error decoder, which can be trained to accommodate the error statistics of a specific quantum processor by training on experimental data.
Exponential speedup of incoherent tunneling via dissipation

We study the escape rate of a particle in a metastable potential in presence of a dissipative bath coupled to the momentum of the particle. Using the semiclassical bounce technique, we find that this rate is exponentially enhanced. In particular, the influence of momentum dissipation depends on the slope of the barrier that the particle is tunneling through. We investigate also the influence of dissipative baths coupled to the position, and to the momentum of the particle, respectively. In this case the rate exhibits a non-monotonic behavior as a function of the dissipative coupling strengths. Remarkably, even in presence of position dissipation, momentum dissipation can enhance exponentially the escape rate in a large range of the parameter space. The influence of the momentum dissipation is also witnessed by the substantial increase of the average energy loss during inelastic (environment-assisted) tunneling.
An improved upper bound for SAT

We show that the CNF satisfiability problem can be solved in O⁎(1.2226m) time, where m is the number of clauses in the formula, improving the known upper bounds O⁎(1.234m) given by Yamamoto 15 years ago and O⁎(1.239m) given by Hirsch 22 years ago. By using an amortized technique and careful case analysis, we successfully avoid the bottlenecks in previous algorithms and get the improvement.
Petri Net Modeling for Ising Model Formulation in Quantum Annealing

Quantum annealing is an emerging new platform for combinatorial optimization, requiring an Ising model formulation for optimization problems. The formulation can be an essential obstacle to the permeation of this innovation into broad areas of everyday life. Our research is aimed at the proposal of a Petri net modeling approach for an Ising model formulation. Although the proposed method requires users to model their optimization problems with Petri nets, this process can be carried out in a relatively straightforward manner if we know the target problem and the simple Petri net modeling rules. With our method, the constraints and objective functions in the target optimization problems are represented as fundamental characteristics of Petri net models, extracted systematically from Petri net models, and then converted into binary quadratic nets, equivalent to Ising models. The proposed method can drastically reduce the difficulty of the Ising model formulation.
Mobile Application for Anemia Detection through Ocular Conjunctiva Images

This paper proposes the development of a mobile application for anemia detection using YOLO v5. The app aims to bring an easy diagnosis as support to health-care professionals to know the presence of anemia in children in places where mobile signals are missing (isolated). Here, we present the implementation and inclusion of the YOLO v5 neural network into the app. We used an image dataset obtained from Universidad Peruana Cayetano Heredia, which contains several pictures of children under five years old and their respective prognosis with the blood test. Although YOLO v5 gets good results using a computer, its implementation in a mobile app reduces its performance. Despite this, the app shows a sensitivity of 0,71 and a specificity of 0,89 to predict anemia.
Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Kernel clustering of categorical data is a useful tool to process the separable datasets and has been employed in many disciplines. Despite recent efforts, existing methods for kernel clustering remain a significant challenge due to the assumption of feature independence and equal weights. In this study, we propose a self-expressive kernel subspace clustering algorithm for categorical data (SKSCC) using the self-expressive kernel density estimation (SKDE) scheme, as well as a new feature-weighted non-linear similarity measurement. In the SKSCC algorithm, we propose an effective non-linear optimization method to solve the clustering algorithm’s objective function, which not only considers the relationship between attributes in a non-linear space but also assigns a weight to each attribute in the algorithm to measure the degree of correlation. A series of experiments on some widely used synthetic and real-world datasets demonstrated the better effectiveness and efficiency of the proposed algorithm compared with other state-of-the-art methods, in terms of non-linear relationship exploration among attributes.
Low complexity design of bit parallel polynomial basis systolic multiplier using irreducible polynomials

Encryption schemes like AES require finite field modular multiplication. The encryption speed is highly dependent on the performance of the finite field multiplier. Several high-speed systolic bit parallel multipliers with low area complexity have been proposed in the literature. In this paper, a modular multiplication algorithm is used to propose a bit parallel polynomial basis systolic multiplier which has achieved 89% less and 17% less area-delay product than the best existing multipliers. It has been observed that the area complexity of the proposed systolic multiplier for irreducible polynomials matches with the best-reported multiplier with 17% less time complexity. The results are further verified with the help of the FPGA implementation of the proposed multiplier for m = 8,163. Being generic, the proposed multiplier can be optimized further for trinomials and pentanomials.
Deep learning for breast cancer classification: Enhanced tangent function

Background and Aim: Recently, deep learning using convolutional neural network has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopathological images is comparatively low. This research aims to increase the accuracy of the classification of breast cancer images by utilizing a Patch-Based Classifier (PBC) along with deep learning architecture. Methodology: The proposed system consists of a Deep Convolutional Neural Network (DCNN) that helps in enhancing and increasing the accuracy of the classification process. This is done by the use of the Patch-based Classifier (PBC). CNN has completely different layers where images are first fed through convolutional layers using hyperbolic tangent function together with the max-pooling layer, drop out layers, and SoftMax function for classification. Further, the output obtained is fed to a patch-based classifier that consists of patch-wise classification output followed by majority voting. Results: The results are obtained throughout the classification stage for breast cancer images that are collected from breast-histology datasets. The proposed solution improves the accuracy of classification whether or not the images had normal, benign, in-situ, or invasive carcinoma from 87% to 94% with a decrease in processing time from 0.45 s to 0.2s on average. Conclusion: The proposed solution focused on increasing the accuracy of classifying cancer in the breast by enhancing the image contrast and reducing the vanishing gradient. Finally, this solution for the implementation of the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique and modified tangent function helps in increasing the accuracy.
Geometrical Optics Restricted Eavesdropping Analysis of Satellite-to-Satellite Secret Key Distillation

Traditionally, the study of quantum key distribution (QKD) assumes an omnipotent eavesdropper that is only limited by the laws of physics. However, this is not the case for specific application scenarios such as the QKD over a free-space link. In this invited paper, we introduce the geometrical optics restricted eavesdropping model for secret key distillation security analysis and apply to a few scenarios common in satellite-to-satellite applications.
The Pop-Up Stall: A Mass Object-Elicitation Method

This paper introduces a useful method for conducting creative social research into material objects and sensory phenomena, what we call the “pop-up mass object-elicitation stall” or “pop-up stall” for short. Our pop-up stalls involved using a curated collection of objects to elicit participant responses in commercial, community and public spaces. In the article, we position pop-up stalls as a material method that can be used within the facet methodology approach to offer strategic insights into research phenomena. We also relate pop-ups to intensive research approaches because of their rapid and voluminous production of varied qualitative data. We evaluate the pop-up stalls’ methodological effects and peculiarities, and explain for researchers things they might anticipate and consider during the planning, deployment and analytical phases of research. We propose three concepts for use in analyzing data generated by the pop-ups: situation, juxtaposition and suffusion.