Miracle-dz/UniFAR-FTUs huggingface dataset:
A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays

Accurate abnormality localization in chest X-rays (CXR) can beneﬁt the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed by experienced radiologists, and it is tedious and time-consuming, thus diﬃcult to acquire. Such a situation results in a diﬃculty to develop a fully-supervised abnormality localization system for CXR. In this regard, we propose to train the CXR abnormality localization framework via a weakly semi-supervised strategy, termed Point Beyond Class (PBC), which utilizes a small number of fully annotated CXRs with lesion-level bounding boxes and extensive weakly annotated samples by points. Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost. Particularly, the core idea behind our PBC is to learn a robust and accurate mapping from the point annotations to the bounding boxes against the variance of annotated points. To achieve that, a regularization term, namely multi-point consistency, is proposed, which drives the model to generate the consistent bounding box from diﬀerent point annotations inside the same abnormality. Furthermore, a self-supervision, termed symmetric consistency, is also proposed to deeply exploit the useful information from the weakly annotated data for abnormality localization. Experimental results on RSNA and VinDr-CXR datasets justify the eﬀectiveness of the proposed method. When ≤ 20% box-level labels are used for training, an improvement of ∼ 5% in mAP can be achieved by our PBC, compared to the current state-of-the-art method ( i.e., Point DETR). Code is available at https://github.com/HaozheLiu-ST/Point-Beyond-Class .
A Comprehensive Evaluation of Android ICC Resolution Techniques

Inter-component communication (ICC) is a widely used mechanism in mobile apps, which enables message-based control flow transferring and data passing between Android components. Effective ICC resolution requires precisely identifying entry points, analyzing data values of ICC fields, modeling related framework APIs, etc. Due to various control-flow- and data-flow-related characteristics involved and the lack of oracles for real-world apps, the comprehensive evaluation of ICC resolution techniques is challenging. To fill this gap, we collect multiple-type benchmark suites with 4,104 apps, covering hand-made apps, open-source, and commercial ones. Considering their differences, various evaluation metrics, e.g., number count, graph structure, and reliable oracle based metrics, are adopted on-demand. As the oracle for real-world apps is unavailable, we design a dynamic analysis approach to extract the real ICC links triggered during GUI exploration. By auditing the code implementations, we carefully check the extracted ICCs and confirm 1,680 ones to form a reliable oracle set, in which each ICC is labeled with 25 code characteristic tags. The evaluation performed on six state-of-the-art ICC resolution tools shows that 1) the completeness of static ICC resolution results on real-world apps is not satisfactory, as up to 38%-85% ICCs are missed by tools; 2) many wrongly reported ICCs are sent from or received by only a few components and the graph structure information can help the identification; 3) the efficiency of fundamental tools, like ICC resolution ones, should be optimized in both engineering and research aspects. By investigating both the missed and wrongly reported ICCs, we discuss the strengths of different tools for users and summarize eight common FN/FP patterns in ICC resolution for tool developers.
Extending Life of Lithium-Ion Battery Systems by Embracing Heterogeneities via an Optimal Control-Based Active Balancing Strategy

This article formulates and solves a multiobjective fast charging-minimum degradation optimal control problem (OCP) for a lithium-ion battery module made of series-connected cells equipped with an active balancing circuitry. The cells in the module are subject to heterogeneity induced by manufacturing defects and nonuniform operating conditions. Each cell is expressed via a coupled nonlinear electrochemical, thermal, and aging model and the direct collocation approach is employed to transcribe the OCP into a nonlinear programming problem (NLP). The proposed OCP is formulated under two different schemes of charging operation: 1) same charging time (OCP-SCT) and 2) different charging time (OCP-DCT). The former assumes simultaneous charging of all cells irrespective of their initial conditions, whereas the latter allows for different charging times of the cells to account for heterogeneous initial conditions. The problem is solved for a module with two series-connected cells with intrinsic heterogeneity among them in terms of state of charge and state of health. Results show that the OCP-DCT scheme provides more flexibility to deal with heterogeneity, boasting of lower temperature increase, charging current amplitudes, and degradation. Finally, a comparison with the common practice of constant current (CC) charging over a long-term cycling operation shows that promising savings, in terms of retained capacity, are attainable under both control (OCP-SCT and OCP-DCT) schemes.
CLIP-Mesh: Generating textured meshes from text using pretrained image-text models

We present a technique for zero-shot generation of a 3D model using only a target text prompt. Without any 3D supervision our method deforms the control shape of a limit subdivided surface along with its texture map and normal map to obtain a 3D asset that corresponds to the input text prompt and can be easily deployed into games or modeling applications. We rely only on a pre-trained CLIP model that compares the input text prompt with differentiably rendered images of our 3D model. While previous works have focused on stylization or required training of generative models we perform optimization on mesh parameters directly to generate shape, texture or both. To constrain the optimization to produce plausible meshes and textures we introduce a number of techniques using image augmentations and the use of a pretrained prior that generates CLIP image embeddings given a text embedding.
A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Model

— An efficient fake news detector becomes essential as the accessibility of social media platforms increases rapidly. Previous works mainly focused on designing the models solely based on individual datasets and might suffer from degradable performance. Therefore, developing a robust model for a combined dataset with diverse knowledge becomes crucial. However, designing the model with a combined dataset requires extensive training time and sequential workload to obtain optimal performance without having some prior knowledge about the model’s parameters. The presented work here will help solve these issues by introducing the unified training strategy to have a base structure for the classifier and all hyperparameters from individual models using a pre-trained transformer model. The performance of the proposed model is noted using three publicly available datasets, namely ISOT and others from the Kaggle website. The results indicate that the proposed unified training strategy surpassed the existing models like Random Forests, Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), etc., with 97% accuracy and achieved the F1 score of 0.97. Furthermore, there was a significant reduction in training time by almost 1.5 to 1.8 × by removing words lower than three letters from the input samples. We also did extensive performance analysis by varying the number of encoder blocks to build compact models and trained on the combined dataset. We justify that reducing encoder blocks resulted in lower performance from the obtained results.
Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel

In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A numerical simulation is provided to demonstrate the effectiveness of our method.
Embedding-Based Music Emotion Recognition Using Composite Loss

Most music emotion recognition approaches perform classification or regression that estimates a general emotional category from a distribution of music samples, but without considering emotional variations (e.g., happiness can be further categorised into much, moderate or little happiness). We propose an embedding-based music emotion recognition approach that associates music samples with emotions in a common embedding space by considering both general emotional categories and fine-grained discrimination within each category. Since the association of music samples with emotions is uncertain due to subjective human perceptions, we compute composite loss-based embeddings obtained to maximise two statistical characteristics, one being the correlation between music samples and emotions based on canonical correlation analysis, and the other being a probabilistic similarity between a music sample and an emotion with KL-divergence. The experiments on two benchmark datasets demonstrate the effectiveness of our embedding-based approach, the composite loss and learned acoustic features. In addition, detailed analysis shows that our approach can accomplish robust bidirectional music emotion recognition that not only identifies music samples matching with a specific emotion but also detects emotions expressed in a certain music sample.
The roots of physics students' motivations: Fear and Integrity

Too often, physics students are beset by feelings of failure and isolation rather than experiencing the creative joys of discovery that physics has to offer. This dissertation research was founded on the desire of a teacher to make physics class exciting and motivating to his students. This work explores how various aspects of learning environments interact with student motivation. This work uses qualitative and quantitative methods to explore how students are motivated to engage in physics and how they feel about themselves while engaging in physics. The collection of four studies in this dissertation culminates in a sociocultural perspective on motivation and identity. This perspective uses two extremes of how students experience physics as a lens for understanding motivation: fear and self-preservation versus integrity and self-expression. Rather than viewing motivation as a property of the student, or viewing students as inherently interested or disinterested in physics, the theoretical perspective on motivation and identity helps examine features of the learning environments that determine how students experience themselves through physics class. This perspective highlights the importance of feeling a sense of belonging in the context of physics and the power that teachers have in shaping student motivations through the construction of their classroom learning environments. Findings demonstrate how different ways that students experience themselves in physics class impact their performance and interest in physics. This dissertation concludes with a set of design principles that can foster integration and integrity among students in physics learning environments.
Equivariant Self-Supervision for Musical Tempo Estimation

Self-supervised methods have emerged as a promising avenue for representation learning in the recent years since they alleviate the need for labeled datasets, which are scarce and expensive to acquire. Contrastive methods are a popular choice for self-supervision in the audio domain, and typically provide a learning signal by forcing the model to be invariant to some transformations of the input. These methods, however, require measures such as negative sampling or some form of regularisation to be taken to prevent the model from collapsing on trivial solutions. In this work, instead of invariance, we propose to use equivariance as a self-supervision signal to learn audio tempo representations from unlabelled data. We derive a simple loss function that prevents the network from collapsing on a trivial solution during training, without requiring any form of regularisation or negative sampling. Our experiments show that it is possible to learn meaningful representations for tempo estimation by solely relying on equivariant self-supervision, achieving performance comparable with supervised methods on several benchmarks. As an added benefit, our method only requires moderate compute resources and therefore remains accessible to a wide research community.
Analise da influencia de indicadores economicos na escolha da estrategia de produção

A decisao de uma empresa em realizar investimentos na producao petrolifera depende principalmente do desempenho economico esperado da jazida, do perfil economico da empresa e das caracteristicas economicas e regulatorias do pais. Uma das decisoes importantes do processo e a escolha da estrategia de producao, em que o valor presente liquido (VPL) vem sendo usado como indicador economico. Neste trabalho, procura-se mostrar que outros indicadores podem ser usados para auxiliar a decisao de investimento, pois, de acordo com as prioridades estabelecidas pela empresa, um so indicador pode nao ser suficiente. Para isso, sao analisadas diversas estrategias de producao considerando um conjunto de indicadores economicos: VPL, retorno sobre o investimento (ROI), producao atualizada e producao de oleo (Np). Os indicadores sao utilizados para avaliar o desempenho do campo e de pocos de producao e injecao. Uma analise de sensibilidade e tambem considerada para mostrar a utilizacao dos indicadores em cenarios incertos (preco do oleo, taxacao, entre outros). Pode-se mostrar que a utilizacao de mais de um indicador possibilita respostas diferentes e mais confiaveis para cada cenario tracado, influenciando o tomador de decisao. O foco da empresa em um ou mais indicadores podera trazer vantagens principalmente em cenarios de incerteza. Os resultados mostram que a utilizacao de varios indicadores proporciona uma tomada de decisao com menor grau de risco, bem como torna possivel capturar outras caracteristicas do projeto que nem sempre podem ser representadas pelo uso tradicional do VPL. Abstract
Detrended cross-correlation analysis of electroencephalogram

In the paper we use detrended cross-correlation analysis (DCCA) to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject's brain function is healthy or not.
Waveguiding driven by the Pancharatnam-Berry phase

We theoretically and numerically investigate the properties of waveguides based on the Pancharatnam-Berry phase, obtained by a longitudinally periodic rotation of the optic axis in a transversely-twisted birefringent medium. In this paper we study the case where the period of the longitudinal modulation is chosen so that a net accumulation of geometric phase in propagation occurs. First, the interplay between different contributions to the optical potential is addressed. Second, a continuous evolution of the polarization structure of the quasi-modes is observed in the numerical simulations. We explain it by a combination of plane-wave-based models and gauge transformations. We discover that, beyond the longitudinal oscillations, the polarization of the quasi-mode also varies through its cross-section. The analogies with respect to charged particles moving in a magnetic field are outlined.
FairSNA: Algorithmic Fairness in Social Network Analysis

In recent years, designing fairness-aware methods has received much attention in various domains, including machine learning, natural language processing, and information retrieval. However, in social network analysis (SNA), designing fairness-aware methods for various research problems by considering structural bias and inequalities of large-scale social networks has not received much attention. In this work, we highlight how the structural bias of social networks impacts the fairness of different SNA methods. We further discuss fairness aspects that should be considered while proposing network structure-based solutions for different SNA problems, such as link prediction, influence maximization, centrality ranking, and community detection. This survey-cum-vision clearly highlights that very few works have considered fairness and bias while proposing solutions; even these works are mainly focused on some research topics, such as link prediction, influence maximization, and PageRank. However, fairness has not yet been addressed for other research topics, such as influence blocking and community detection. We review state-of-the-art for different research topics in SNA, including the considered fairness constraints, their limitations, and our vision. This survey also covers evaluation metrics, available datasets, and synthetic network generating models used in such studies. Finally, we highlight various open research directions that require researchers’ attention to bridge the gap between fairness and SNA.
Finger Multimodal Feature Fusion and Recognition Based on Channel Spatial Attention

Due to the instability and limitations of unimodal biometric systems, multimodal systems have attracted more and more attention from researchers. However, how to exploit the independent and complementary information between different modalities remains a key and challenging problem. In this paper, we propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial Attention Fusion Module, FPV-CSAFM). Specifically, for each pair of fingerprint and finger vein images, we first propose a simple and effective Convolutional Neural Network (CNN) to extract features. Then, we build a multimodal feature fusion module (Channel Spatial Attention Fusion Module, CSAFM) to fully fuse the complementary information between fingerprints and finger veins. Different from existing fusion strategies, our fusion method can dynamically adjust the fusion weights according to the importance of different modalities in channel and spatial dimensions, so as to better combine the information between different modalities and improve the overall recognition performance. To evaluate the performance of our method, we conduct a series of experiments on multiple public datasets. Experimental results show that the proposed FPV-CSAFM achieves excellent recognition performance on three multimodal datasets based on fingerprints and finger veins.
CloudVision: DNN-based Visual Localization of Autonomous Robots using Prebuilt LiDAR Point Cloud

In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data from an RGB camera. The 3D map is obtained utilizing an advanced LiDAR-based simultaneous localization and mapping (SLAM) algorithm capable of collecting a precise sparse map. The features extracted from the camera images are compared with the points of the 3D map, and then the geometric optimization problem is being solved to achieve precise visual localization. Our approach allows employing a scout robot equipped with an expensive LiDAR only once — for mapping of the environment, and multiple operational robots with only RGB cameras onboard — for performing mission tasks, with the localization accuracy higher than common camera-based solutions. The proposed method was tested on the custom dataset collected in the Skolkovo Institute of Science and Technology (Skoltech). During the process of assessing the localization accuracy, we managed to achieve centimeter-level accuracy; the median translation error was as low as 1.3 cm. The precise positioning achieved with only cameras makes possible the usage of autonomous mobile robots to solve the most complex tasks that require high localization accuracy.
Induced Cycles and Paths Are Harder Than You Think

The goal of the paper is to give fine-grained hardness results for the Subgraph Isomorphism (SI) problem for fixed size induced patterns H, based on the k-Clique hypothesis that the current best algorithms for Clique are optimal. Our first main result is that for any pattern graph H that is a core, the SI problem for H is at least as hard as t-Clique, where t is the size of the largest clique minor of H. This improves (for cores) the previous known results [Dalirrooyfard-Vassilevska W. STOC’20] that the SI for H is at least as hard as k-clique where k is the size of the largest clique subgraph in H, or the chromatic number of H (under the Hadwiger conjecture). For detecting any graph pattern H, we further remove the dependency of the result of [Dalirrooyfard-Vassilevska W. STOC’20] on the Hadwiger conjecture at the cost of a sub-polynomial decrease in the lower bound. The result for cores allows us to prove that the SI problem for induced k-Path and k-Cycle is harder than previously known. Previously [Floderus et al. Theor. CS 2015] had shown that k-Path and k-Cycle are at least as hard to detect as a $\lfloor$k/2$\rfloor -$Clique. We show that they are in fact at least as hard as 3k/4-O(1)-Clique, improving the conditional lower bound exponent by a factor of 3/2. This shoivs for instance that the knoivn $O(n^{5})$ combinatorial algorithm for 7-cycle detection is conditionally tight. Finally, we provide a new conditional lower bound for detecting induced 4-cycles: $n^{2-o(1)}$ time is necessary even in graphs with n nodes and $O(n^{15})$ edges. The 4-cycle is the smallest induced pattern whose running time is not well-understood. It can be solved in matrix multiplication, $O(n^{\omega})$ time, but no conditional lower bounds were known until ours. We provide evidence that certain types of reductions from triangle detection to 4-Cycle would not be possible. We do this by studying a new problem called Paired Pattern Detection.
“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations

As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.