Miracle-dz/UniFAR-FTUs huggingface dataset:
A Decomposition Algorithm for Two-Stage Stochastic Programs with Nonconvex Recourse

In this paper, we have studied a decomposition method for solving a class of nonconvex two-stage stochastic programs, where both the objective and constraints of the second-stage problem are nonlinearly parameterized by the first-stage variable. Due to the failure of the Clarke regularity of the resulting nonconvex recourse function, classical decomposition approaches such as Benders decomposition and (augmented) Lagrangian-based algorithms cannot be directly generalized to solve such models. By exploring an implicitly convex-concave structure of the recourse function, we introduce a novel decomposition framework based on the so-called partial Moreau envelope. The algorithm successively generates strongly convex quadratic approximations of the recourse function based on the solutions of the second-stage convex subproblems and adds them to the first-stage master problem. Convergence under both fixed scenarios and interior samplings is established. Numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm.
Graph-based Approximate NN Search: A Revisit

Nearest neighbor search plays a fundamental role in many disciplines such as multimedia information retrieval, data-mining, and machine learning. The graph-based search approaches show supe-rior performance over other types of approaches in recent studies. In this paper, the graph-based NN search is revisited. We optimize two key components in the approach, namely the search procedure and the graph that supports the search. For the graph construction, a two-stage graph diversification scheme is proposed, which makes a good trade-off between the efficiency and reachability for the search procedure that builds upon it. Moreover, the proposed diversification scheme allows the search procedure to decide dynamically how many nodes should be visited in one node’s neighborhood. By this way, the computing power of the devices is fully utilized when the search is carried out under different circumstances. Furthermore, two NN search procedures are designed respectively for small and large batch queries on the GPU. The optimized NN search, when being supported by the two-stage diversified graph, outperforms all the state-of-the-art approaches on both the CPU and the GPU across all the considered large-scale datasets.
Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI

As research and industry moves towards large-scale models capable of numerous downstream tasks, the complexity of understanding multi-modal datasets that give nuance to models rapidly increases. A clear and thorough understanding of a dataset’s origins, development, intent, ethical considerations and evolution becomes a necessary step for the responsible and informed deployment of models, especially those in people-facing contexts and high-risk domains. However, the burden of this understanding often falls on the intelligibility, conciseness, and comprehensiveness of the documentation. It requires consistency and comparability across the documentation of all datasets involved, and as such documentation must be treated as a user-centric product in and of itself. In this paper, we propose Data Cards for fostering transparent, purposeful and human-centered documentation of datasets within the practical contexts of industry and research. Data Cards are structured summaries of essential facts about various aspects of ML datasets needed by stakeholders across a dataset’s lifecycle for responsible AI development. These summaries provide explanations of processes and rationales that shape the data and consequently the models—such as upstream sources, data collection and annotation methods; training and evaluation methods, intended use; or decisions affecting model performance. We also present frameworks that ground Data Cards in real-world utility and human-centricity. Using two case studies, we report on desirable characteristics that support adoption across domains, organizational structures, and audience groups. Finally, we present lessons learned from deploying over 20 Data Cards.x
Cored dark matter halos in the cosmic neutrino background

We study the impact of the interaction between DM and the cosmic neutrino background on the evolution of galactic dark matter halos. The energy transfer from the cosmic background neutrinos to galactic dark matter can heat the center of the galaxy and make it cored. The core formation is efficient for the small galaxies such as the satellite galaxies of the Milky Way and we can put a conservative constraint on the non-relativistic elastic scattering cross section as σχν ≲ 10-31 cm2 for 0.1 keV dark matter.
A study of real-world data races in Golang

The concurrent programming literature is rich with tools and techniques for data race detection. Less, however, has been known about real-world, industry-scale deployment, experience, and insights about data races. Golang (Go for short) is a modern programming language that makes concurrency a first-class citizen. Go offers both message passing and shared memory for communicating among concurrent threads. Go is gaining popularity in modern microservice-based systems. Data races in Go stand in the face of its emerging popularity. In this paper, using our industrial codebase as an example, we demonstrate that Go developers embrace concurrency and show how the abundance of concurrency alongside language idioms and nuances make Go programs highly susceptible to data races. Google’s Go distribution ships with a built-in dynamic data race detector based on ThreadSanitizer. However, dynamic race detectors pose scalability and flakiness challenges; we discuss various software engineering trade-offs to make this detector work effectively at scale. We have deployed this detector in Uber’s 46 million lines of Go codebase hosting 2100 distinct microservices, found over 2000 data races, and fixed over 1000 data races, spanning 790 distinct code patches submitted by 210 unique developers over a six-month period. Based on a detailed investigation of these data race patterns in Go, we make seven high-level observations relating to the complex interplay between the Go language paradigm and data races.
Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI

As an emerging interaction paradigm, physiological computing is increasingly being used to both measure and feed back information about our internal psychophysiological states. While most applications of physiological computing are designed for individual use, recent research has explored how biofeedback can be socially shared between multiple users to augment human-human communication. Reflecting on the empirical progress in this area of study, this paper presents a systematic review of 64 studies to characterize the interaction contexts and effects of social biofeedback systems. Our findings highlight the importance of physio-temporal and social contextual factors surrounding physiological data sharing as well as how it can promote social-emotional competences on three different levels: intrapersonal, interpersonal, and task-focused. We also present the Social Biofeedback Interactions framework to articulate the current physiological-social interaction space. We use this to frame our discussion of the implications and ethical considerations for future research and design of social biofeedback interfaces.
A single speaker is almost all you need for automatic speech recognition

We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems. Through ex-tensive experiments, we show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems on a target language using only one target-language speaker during model training. We managed to close the gap between ASR models trained with synthesized versus human speech compared to other works that use many speakers. Finally, we show that it is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language.
Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information

We show how inter-asset dependence information derived from market prices of options can lead to improved model-free price bounds for multi-asset derivatives. Depending on the type of the traded option, we either extract correlation information or we derive restrictions on the set of admissible copulas that capture the inter-asset dependencies. To compute the resultant price bounds for some multi-asset options of interest, we apply a modified martingale optimal transport approach. Several examples based on simulated and real market data illustrate the improvement of the obtained price bounds and thus provide evidence for the relevance and tractability of our approach.
Synthesizing Attack-Aware Control and Active Sensing Strategies Under Reactive Sensor Attacks

We consider the probabilistic planning problem for a defender (P1) who can jointly query the sensors and take control actions to reach a set of goal states while being aware of possible sensor attacks by an adversary (P2) who has perfect observations. To synthesize a provably-correct, attack-aware joint control and active sensing strategy for P1, we construct a stochastic game on graph with augmented states that include the actual game state (known only to the attacker), the belief of the defender about the game state (constructed by the attacker based on his knowledge of the defender’s observations). We present an algorithm to compute a belief-based, randomized strategy for P1 to satisfy the reachability objective with probability one, under the worst-case sensor attacks carried out by an informed P2. We prove the correctness of the algorithm and illustrate it using an example.
The Cardinal Complexity of Comparison-based Online Algorithms

We consider ordinal online problems, i.e., those tasks that only depend on the pairwise comparisons between elements in the input. E.g., the secretary problem and the game of googol. The natural approach to these tasks is to use ordinal online algorithms that at each step only consider relative ranking among the arrived elements, without looking at the numerical values of the input. We formally study the question of how cardinal algorithms (that can use numerical values of the input) can improve upon ordinal algorithms. We give a universal construction of the input distribution for any ordinal online problem, such that the advantage of the cardinal algorithms over the ordinal algorithms is at most 1 + ε for arbitrary small ε > 0. However, the value range of the input elements in this construction is huge: O
How Can We Develop Explainable Systems? Insights from a Literature Review and an Interview Study

Quality aspects such as ethics, fairness, and transparency have been proven to be essential for trustworthy software systems. Explainability has been identified not only as a means to achieve all these three aspects in systems, but also as a way to foster users’ sentiments of trust. Despite this, research has only marginally focused on the activities and practices to develop explainable systems. To close this gap, we recommend six core activities and associated practices for the development of explainable systems based on the results of a literature review and an interview study. First, we identified and summarized activities and corresponding practices in the literature. To complement these findings, we conducted interviews with 19 industry professionals who provided recommendations for the development process of explainable systems and reviewed the activities and practices based on their expertise and knowledge. We compared and combined the findings of the interviews and the literature review to recommend the activities and assess their applicability in industry. Our findings demonstrate that the activities and practices are not only feasible, but can also be integrated in different development processes.
A Deep Learning Approach for Digital ColorReconstruction of Lenticular Films

We propose the first accurate digitization and color reconstruction process for historical lenticular film that is robust to artifacts. Lenticular films emerged in the 1920s and were one of the first technologies that permitted to capture full color information in motion. The technology leverages an RGB filter and cylindrical lenticules embossed on the film surface to encode the color in the horizontal spatial dimension of the image. To project the pictures the encoding process was reversed using an appropriate analog device. In this work, we introduce an automated, fully digital pipeline to process the scan of lenticular films and colorize the image. Our method merges deep learning with a model-based approach in order to maximize the performance while making sure that the reconstructed colored images truthfully match the encoded color information. Our model employs different strategies to achieve an effective color reconstruction, in particular (i) we use data augmentation to create a robust lenticule segmentation network, (ii) we fit the lenticules raster prediction to obtain a precise vectorial lenticule localization, and (iii) we train a colorization network that predicts interpolation coefficients in order to obtain a truthful colorization. We validate the proposed method on a lenticular film dataset and compare it to other approaches. Since no colored groundtruth is available as reference, we conduct a user study to validate our method in a subjective manner. The results of the study show that the proposed method is largely preferred with respect to other existing and baseline methods.
SISTEM KEMUDI SEMI OTOMATIS DENGAN GYROSCOPE PADA MOBILE ROBOT BERSTRUKTUR ORIGAMI

Robot terkenal dengan harga yang mahal dan tingkat kesulitan pembuatan yang tinggi. Hal ini terjadi dikarenakan robot membutuhkan banyak part dan skill. Salah satu metode membangun struktur robot yang mudah dan murah adalah dengan menggunakan prinsip kirigami atau origami. Kirigami adalah cabang dari origami yang dalam prosesnya melibatkan proses pemotongan. Origami memungkinkan membangun obyek 3D dari lembaran 2D. Mobile robot untuk keperluan inspeksi terkadang harus menjalankan misi yang membuatnya tidak dapat kembali (oneway mission), sehingga harga yang murah dan mudah sangat diharapkan. Dengan menggunakan prinsip origami tingkat presisi robot akan menurun dikarenakan sifat kelenturan bahan. Robot tersebut telah berhasil direalisasikan. Dalam penelitian ini dikembangkan sistem kemudi semi otomatis sehingga robot lebih mudah dikendalikan dengan menjaga arah gerak robot. Percobaan dilakukan dengan mengubah salah satu roda robot dengan diameter yang berbeda. Sistem dengan metode yang diusulkan secara signifikan dapat menjaga arah gerak daripada sistem open loop
Distributed Hybrid Current Sharing Control for Inverters in Parallel Operation

A novel distributed control scheme for inverters in parallel operation is proposed, where the synchronization control and current sharing control are decoupled by local feedback, the former is realized in digital manner, and the latter is implemented by digital and analog circuits. All the inverter modules are completely electrically-isolated, the instantaneous current sharing is realized, and the hot-swap of any module is permissible. The control characteristics of the root-mean-square voltage regulator of each inverter module in parallel operation are researched, as well as the coupling effect between root-mean-square voltage regulator and current sharing performance. Its bad influence on current sharing is compensated by applying the circulating current to adjust the amplitude of the sinusoidal voltage reference signal, improving the current sharing performance and output voltage precision of the parallel system effectively. Theoretic analysis and experimental results are presented to demonstrate the validity and feasibility of the proposed control scheme.
Purchasing Massive Prestige Brands: the Exploration of Consumers' Value Perceptions

The objective of the research was to analyze the internal aspects of consumers in perceiving the massive prestige brands including conspicuousness, need for uniqueness, extended-self, hedonism, exploration, value consciousness, and vanity. The research used quantitative approach by conduct-ing a survey on respondents who bought massive prestige brands. Conspicuousness consumption and Need for Uniqueness had an effect on purchase of massive prestige brands positively and significantly. Extended-self had an effect on purchase of massive prestige brands positively but not significantly. Hedonism, Exploration, and Vanity had an effect on purchase of massive prestige brands positively and significantly. Value consciousness had an effect on purchase of massive pres tige brands negatively and significantly.
Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing

Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds. ”
Career Calling and Task Performance: The Moderating Role of Job Demand

The Work as a Calling Theory (WCT) predicts that career calling fosters job performance. A quantitative summary of previous work supports this prediction and shows that the relation between calling and job performance is moderate in size (ρ = .29, K = 11, N = 2286). Yet, the environmental conditions that modulate this relation are completely unknown. According to an interactionist perspective, we argue that calling may predict performance only when job demand is low. Results of a multisource study on salesmen and managers dyads (N= 965) partially supported this prediction. We observed that highly demanding work environments, characterized by pressure to perform, high workload, and unachievable deadlines, suppress the positive relation between calling and self-reported performance. Job demand directly impairs performance and suppresses the positive effect of career calling. Theoretical and practical implications of these results are discussed.
Impact of teacher encouragement on children’s consumption and non-eating behaviour in a Wisconsin elementary school vegetable snack programme

Objective: Schools participating in the US Department of Agriculture (USDA) Fresh Fruit and Vegetable Programme (FFVP) serve more fruit than vegetables and tend not to implement activities designed to increase consumption. This research examines the impact of teacher encouragement on consumption of raw vegetables served through a non-FFVP elementary school snack programme. Method: Twelve teachers and 217 children in Grades K–3 were randomly assigned to one of three conditions. One condition served as the control group (no encouragement), while the other two conditions represented intervention groups (moderate and high encouragement). Three vegetables were served eight times each with teachers in both intervention conditions providing encouragement for the first six servings of each vegetable. All encouragement was discontinued during the final two servings of each vegetable. Average consumption and non-eating behaviour were analysed using differences in means, ordinary least squares and logistic regression analysis. Results: Moderate and high teacher encouragement significantly increased children’s consumption of vegetables compared to the control group. Both encouragement conditions also significantly reduced the likelihood of non-eating behaviour. The positive encouragement impacts were found to persist in the short term even when teacher encouragement had ceased, especially for the high encouragement condition. Conclusion: This study provides evidence that relatively low-cost teacher encouragement can decrease non-eating behaviour and increase children’s consumption of vegetable snacks served in elementary schools. This suggests that schools participating in the FFVP could see benefits from serving more vegetables and implementing encouragement activities. Moreover, these results could be informative to non-FFVP schools in implementing their own vegetable snack programme.
A Review of Privacy Decision-making Mechanisms in Online Social Networks

Personal information of online social networks (OSNs) is governed by the privacy policies chosen by users besides OSN’s policies. Users make these decisions using privacy mechanisms, but privacy problems and regrets are daily reported. This article reviews current privacy mechanisms and solutions. For this, we analyze all the sub-decisions and elements of online communication involved in the privacy decision-making process. However, the differences in users’ motivations and the disclosure of too sensitive information (among others) can lead to loss of privacy. In this work, we identify requirements such as automation, preference-centered, relationship-based, and multi-party privacy mechanisms, which have been more researched. But also other requirements (recently emerged), such as privacy preservation with risk metrics, explainability, and ephemeral messages. We explore all the advances made in the literature, and we have seen that most of these have been focused on matching the users’ preferences with their decision (which is not appropriate, because users cannot evaluate all of the potential privacy scenarios) instead of assessing privacy risk metrics, adaptation, and explainability. Therefore, we have identified open challenges, such as metrics for assessing privacy risks, explainable solutions for users, ephemeral communication solutions, and the application of these requirements to the multi-party privacy scenario.
A Survey on Gaps between Mean-Variance Approach and Exponential Growth Rate Approach for Portfolio Optimization

Portfolio optimization can be roughly categorized as the mean-variance approach and the exponential growth rate approach based on different theoretical foundations, trading logics, optimization objectives, and methodologies. The former and the latter are often used in long-term and short-term portfolio optimizations, respectively. Although the mean-variance approach could be applied to short-term portfolio optimization, the performance may not be satisfactory (same with the exponential growth rate approach to the long-term portfolio optimization). This survey mainly explores the gaps between these two approaches, and investigates what common ideas or mechanisms are beneficial. Besides, the evaluating framework of this field and some unsolved problems are also discussed.