Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification

Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning algorithm to utilize the structural information and further make use of unlabeled and weakly-labeled data. We use a generative model to leverage the large amount of unlabeled data and introduce path constraints into the learning algorithm to incorporate the structural information of the class hierarchy. The posterior probabilities of both unlabeled and weakly labeled data can be incorporated with path-dependent scores. Since we put a structure-sensitive cost to the learning algorithm to constrain the classification consistent with the class hierarchy and do not need to reconstruct the feature vectors for different structures, we can significantly reduce the computational cost compared to structural output learning. Experimental results on two hierarchical text classification benchmarks show that our approach is not only effective but also efficient to handle the semi-supervised and weakly supervised hierarchical text classification.Comment: Aceepted by 2019 World Wide Web Conference (WWW19

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Algorithmic Governance from the Bottom Up

Artificial intelligence and machine learning are both a blessing and a curse for governance. In theory, algorithmic governance makes government more efficient, more accurate, and more fair. But the emergence of automation in governance also rests on public-private collaborations that expand both public and private power, aggravate transparency and accountability gaps, and create significant obstacles for those seeking algorithmic justice. In response, a nascent body of law proposes technocratic policy changes to foster algorithmic accountability, ethics, and transparency. This Article examines an alternative vision of algorithmic governance, one advanced primarily by social and labor movements instead of technocrats and firms. The use of algorithmic governance in increasingly high-stakes settings has generated an outpouring of activism, advocacy, and resistance. This mobilization draws on the same concerns that animate budding policy responses. But social and labor movements offer an alternative source of constraints on algorithmic governance: direct resistance from the bottom up. These movements confront head-on the entanglement of economic power, racial hierarchy, and government surveillance. Using three case studies, this Article explores how tech workers and social movements are resisting and mobilizing against technologies that expand surveillance and funnel wealth to the private sector. Each case study illustrates how the intermingling of state and private power has required movements to engage both within and outside firms to counteract the growing appeal of automation. Yet the dominant approaches to regulating the government’s uses of technology continue to afford a privileged role to private firms and elite institutions, sidelining movement demands. The fundamental challenge posed by these movements will be whether — and how — law and policy can accommodate demands for bottom-up control. This Article sketches a new vision for algorithmic accountability, with a more vibrant role for workers and for the public in determining how firms and government institutions work together

Algorithmic Governance from the Bottom Up

Artificial intelligence and machine learning are both a blessing and a curse for governance. In theory, algorithmic governance makes government more efficient, more accurate, and more fair. But the emergence of automation in governance also rests on public-private collaborations that expand both public and private power, aggravate transparency and accountability gaps, and create significant obstacles for those seeking algorithmic justice. In response, a nascent body of law proposes technocratic policy changes to foster algorithmic accountability, ethics, and transparency.This Article examines an alternative vision of algorithmic governance, one advanced primarily by social and labor movements instead of technocrats and firms. The use of algorithmic governance in increasingly high-stakes settings has generated an outpouring of activism, advocacy, and resistance. This mobilization draws on the same concerns that animate budding policy responses. But social and labor movements offer an alternative source of constraints on algorithmic governance: direct resistance from the bottom up. These movements confront head-on the entanglement of economic power, racial hierarchy, and government surveillance. Using three case studies, this Article explores how tech workers and social movements are resisting and mobilizing against technologies that expand surveillance and funnel wealth to the private sector. Each case study illustrates how the intermingling of state and private power has required movements to engage both within and outside firms to counteract the growing appeal of automation. Yet the dominant approaches to regulating the government’s uses of technology continue to afford a privileged role to private firms and elite institutions, sidelining movement demands. The fundamental challenge posed by these movements will be whether—and how—law and policy can accommodate demands for bottom-up control. This Article sketches a new vision for algorithmic accountability, with a more vibrant role for workers and for the public in determining how firms and government institutions work together

Ancillary Services Market Design in Distribution Networks: Review and Identification of Barriers

The high proliferation of converter-dominated Distributed Renewable Energy Sources (DRESs) at the distribution grid level has gradually replaced the conventional synchronous generators (SGs) of the transmission system, resulting in emerging stability and security challenges. The inherent characteristics of the SGs are currently used for providing ancillary services (ASs), following the instructions of the Transmission System Operator, while the DRESs are obliged to o er specific system support functions, without being remunerated for these functions, but only for the energy they inject. This changing environment has prompted the integration of energy storage systems as a solution for transfusing new characteristics and elaborating their business in the electricity markets, while the smart grid infrastructure and the upcoming microgrid architectures contribute to the transformation of the distribution grid. This review investigates the existing ASs in transmission system with the respective markets (emphasizing the DRESs’ participation in these markets) and proposes new ASs at distribution grid level, with emphasis to inertial response, active power ramp rate control, frequency response, voltage regulation, fault contribution and harmonic mitigation. The market tools and mechanisms for the procurement of these ASs are presented evolving the existing role of the Operators. Finally, potential barriers in the technical, regulatory, and financial framework have been identified and analyzed.Unión Europea (Programa Horizonte 2020) 76409

1. Helgoland Power and Energy Conference - 24. Dresdener Kreis 2023

Der Sammelband "1. Helgoland Power and Energy Conference" beinhaltet neben einem kurzen Bericht zum 24. Treffen des Dresdener Kreises 2023 wissenschaftliche Beiträge von Doktoranden der beteiligten Hochschulinstitute zum Thema Elektroenergieversorgung. Der Dresdener Kreis setzt sich aus der Professur für Elektroenergieversorgung der Technischen Universität Dresden, dem Fachgebiet Elektrische Anlagen und Netze der Universität Duisburg-Essen, dem Fachgebiet Elektrische Energieversorgung der Leibniz Universität Hannover und dem Lehrstuhl Elektrische Netze und Erneuerbare Energie der Otto-von-Guericke Universität Magdeburg zusammen und trifft sich einmal im Jahr zum fachlichen Austausch an einer der beteiligten Universitäten

Transformer for Object Re-Identification: A Survey

Object Re-Identification (Re-ID) aims to identify and retrieve specific objects from varying viewpoints. For a prolonged period, this field has been predominantly driven by deep convolutional neural networks. In recent years, the Transformer has witnessed remarkable advancements in computer vision, prompting an increasing body of research to delve into the application of Transformer in Re-ID. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single-/cross modal tasks. Besides, this survey also covers a wide range of Re-ID research objects, including progress in animal Re-ID. Given the diversity of species in animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task to facilitate future research. Finally, we discuss some important yet under-investigated open issues in the big foundation model era, we believe it will serve as a new handbook for researchers in this field

Population Genomics of Selection in the Eastern Oyster Contact Zone

Intraspecific clinal systems are ideal for investigating the how divergence occurs in the presence of gene flow because they represent a balance between selection and gene flow prior to speciation. High dispersal marine species with clinal variation are particularly informative to test for divergent selection because selection likely is strong enough to counteract high gene flow. The degree of population structure varies considerably among loci, such that the genome acts as a sieve allowing gene flow at neutral loci and impeding it at selected loci, creating a genomic mosaic of differentiation. In this study, I examine genomic and geographic patterns of differentiation among parapatric populations of the eastern oyster (Crassostrea virginica) along their contact zone in Florida estuaries. The planktotrophic larval phase of this species gives it the potential for regular long-distance dispersal and genetically homogeneous populations. However Florida populations at the center of its range exhibit a sharp step cline at some loci, suggesting a role for divergent selection. Using 217 AFLP loci, including seven candidate loci for differential selection between the two populations, I genotyped 1,011 spat over two seasons and 274 adults at sites along the contact zone. I examined: (1) whether genome scans can detect divergent selection in a clinal system, (2) the genomic and geographic patterns of differentiation along the cline at neutral and selected loci, and (3) regional patterns of differentiation and genotypic distributions among the life stages. Results demonstrated: (1) candidate loci for regionally divergent selection, (2) a genomic and geographic mosaic of differentiation, (3) regional and localized selection at a non-trivial portion of loci, (4) lower recruitment and some mortality in the center of the cline, and (5) strong exogenous, post-settlement viability selection against intermediate and non-native-like genotypes. While a combination of neutral and adaptive processes likely shape genomic and geographic patterns of differentiation, this study revealed evidence for divergent selection in an estuarine species with high potential for gene flow. Overall, these results point to a major role for post-zygotic, environment-dependent selection in the maintenance of the contact zone between Atlantic and Gulf-type oyster populations

Effects of home-, school-, and individual-level factors on children’s deliberate memory development in elementary school

Children enter kindergarten with a variety of experiences and skills. In this transition to formal school, they are expected to adapt quickly to new demands such as remembering specific pieces of information, knowing when to retrieve this information, and understanding how to use this information to complete specific tasks. These skills have been referred to as children’s deliberate memory skills and are thought to serve children’s long term academic success. However, limited research has focused on specific aspects of children’s everyday contexts that play a role in the development of these skills – such as adult-to-child language exchanges in home and school settings. Therefore, the goals of the current study were to (a) understand the role of children’s every day, lived experiences such as parent–child reminiscing and teacher–child linguistic exchanges (i.e., cognitive processing language in classrooms) on the initial acquisition and sustained use of mnemonic strategies across the kindergarten and first-grade years, and (b) describe the interplay between individual-level factors – such as other components of children’s cognition – and these adult-to-child scaffolding practices on children’s memory development. Drawing on a sample of 79 children nested in 10 kindergarten classrooms, children’s deliberate memory skills were assessed at 6 timepoints from kindergarten entry to the end of first grade. Kindergarten teachers’ instruction was recorded using GoPro cameras during regular mathematics and language arts lessons; these recordings were subsequently coded for the prevalence use of cognitive processing language (Coffman et al., 2008; 2019). Parent–child dyads took part in a parent-child reminiscing task in which they were asked to reminisce about two recent events. Conversations were coded for parents’ elaborative reminiscing style (Reese et al., 1993; Langley et al., 2017). Finally, children’s executive function and self-regulation skills were assessed during the kindergarten year using the Dimensional Change Card Sort Task (Zelazo, 2006) and the Head Toes Knees Shoulders Task, (Ponitz et al., 2009; McClelland et al., 2014) respectively. Results from a series of growth curve models using a multilevel modeling framework revealed significant predictors of children’s deliberate memory skills at the start of kindergarten and at the end of first grade, as well as of the rate at which changes in these skills occurred as a function of home-, school-, and individual-level factors. First, although children of parents with high levels of elaborative reminiscing entered kindergarten with higher levels of deliberate memory skills, it was children who had parents who used lower levels of elaborative reminiscing who developed more rapidly over the course of the kindergarten and first grade years. Second, children who were exposed to teachers who used higher levels of cognitive processing language (CPL) in kindergarten developed strategic sorting skills more rapidly over the course of first grade and ended the year with higher levels of deliberate strategy use than their peers who were exposed to lower levels of cognitive processing language. Finally, for children with lower self-regulation skills, those exposed to higher levels of CPL in kindergarten evidenced higher levels of deliberate strategy use at the end of first grade than their peers who were exposed to lower levels of CPL. Taken together, these findings provide insight to the role of parent-child and teacher-child processes on the development of children’s deliberate memory skills during the first two years of elementary school. Strengths, limitations, and future directions for researchers and educators are discussed