Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Stage Robust Optimization with Decision Dependent Uncertainty

The type of decision dependent uncertainties (DDUs) imposes a great challenge in decision making, while existing methodologies are not suﬃcient to support many real practices. In this paper, we present a systematic study to handle this challenge in two-stage robust optimization (RO). Our main contributions include three sophisticated variants of column-and-constraint generation method to exactly compute DDU-based two-stage RO. By a novel application of core concepts of linear programming, we provide rigorous analyses on their computational behaviors. Interestingly, in terms of the iteration complexity of those algorithms, DDU-based two-stage RO is not more demanding than its decision independent uncertainty (DIU) based counterpart. It is worth highlighting a counterintuitive discovery that converting a DIU set into a DDU set by making use of “deep knowledge” and then computing the resulting DDU-based formulation may lead to a signiﬁcant improvement. Indeed, as shown in this paper, in addition to capturing the actual dependence existing in the real world, DDU is a powerful and ﬂexible tool to represent and leverage analytical properties or simply domain expertise to achieve a strong solution capacity. So, we believe it will open a new direction to solve large-scale DIU- or DDU-based RO. Other important results include basic structural properties for two-stage RO, an approximation scheme to deal with mixed integer recourse, and a couple of enhancement techniques for the developed algorithms, as well as an organized numerical study to help us appreciate all algorithms and enhancement techniques’ computational performances.
A general approach for running Python codes in OpenFOAM using an embedded pybind11 Python interpreter

As the overlap between traditional computational mechanics and machine learning grows, there is an increasing demand for straight-forward approaches to interface Python-based procedures with C++-based OpenFOAM. This article introduces one such general methodology, allowing the execution of Python code directly within an OpenFOAM solver without the need for Python code translation. The proposed approach is based on the lightweight library pybind11, where OpenFOAM data is transferred to an embedded Python interpreter for manipulation, and results are returned as needed. Following a review of related approaches, the article describes the approach, with a particular focus on data transfer between Python and OpenFOAM, executing Python scripts and functions, and practical details about the implementation in OpenFOAM. Three complementary test cases are presented to highlight the functionality and demonstrate the effect of different data transfer approaches: a Python-based velocity profile boundary condition; a Python-based solver for prototyping; and a machine learning mechanical constitutive law class for solids4foam which performs field calculations.
Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture

Person name capture from human speech is a difficult task in human-machine conversations. In this paper, we propose a novel approach to capture the person names from the caller utterances in response to the prompt"say and spell your first/last name". Inspired from work on spell correction, disfluency removal and text normalization, we propose a lightweight Seq-2-Seq system which generates a name spell from a varying user input. Our proposed method outperforms the strong baseline which is based on LM-driven rule-based approach.
OLxPBench: Real-time, Semantically Consistent, and Domain-specific are Essential in Benchmarking, Designing, and Implementing HTAP Systems

As real-time analysis on the fresh data become in-creasingly compelling, more organizations deploy Hybrid Trans-actional/Analytical Processing (HTAP) systems to support real-time queries on data recently generated by online transaction processing. This paper argues that real-time queries, semantically consistent schema, and domain-specific workloads are essential in benchmarking, designing, and implementing HTAP systems. However, most state-of-the-art and state-of-the-practice bench-marks ignore those critical factors. Hence, at best, they are incommensurable and, at worst, misleading in benchmarking, designing, and implementing HTAP systems. This paper presents OLxPBench, a composite HTAP benchmark suite. OLxPBench proposes: (1) the abstraction of a hybrid transaction, performing a real-time query in-between an online transaction, to model widely-observed behavior pattern - making a quick decision while consulting real-time analysis; (2) a semantically consistent schema to express the relationships between OLTP and OLAP schema; (3) the combination of domain-specific and general benchmarks to characterize diverse application scenarios with varying resource demands. Our evaluations justify the three design decisions of OLxPBench and pinpoint the bottlenecks of two mainstream distributed HTAP DBMSs. International Open Benchmark Council (Bench Council) sets up the OLxP-Bench homepage at https://www.benchcouncil. org/olxpbench/. Its source code is available from https://github.com/BenchCouncil/olxpbench.git.
Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation

Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models. It is high time that we enhance the robustness of speech processing models to obtain good performance when encountering speech distortions while not hurting the original performance on clean speech. In this work, we propose to improve the robustness of speech processing models by domain adversarial training (DAT). We conducted experiments based on the SUPERB framework on five different speech processing tasks. In case we do not always have knowledge of the distortion types for speech data, we analyzed the binary-domain and multi-domain settings, where the former treats all distorted speech as one domain, and the latter views different distortions as different domains. In contrast to supervised training methods, we obtained promising results in target domains where speech data is distorted with different distortions including new unseen distortions introduced during testing.
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data

In this work Time Series Classiﬁcation techniques are investigated, and especially their applicability in applications where there are signiﬁcant diﬀerences between the individuals where data is collected, and the individuals where the classiﬁcation is evaluated. Classiﬁcation methods are applied to a fault classiﬁcation case, where a key assumption is that data from a fault free reference case for each speciﬁc individual is available. For the investigated application, wave propagation cause almost chaotic changes of a measured pressure signal, and physical modeling is diﬃcult. Direct application of One-Nearest-Neighbor Dynamic Time Warping, a common technique for this kind of problem, and other machine learning techniques are shown to fail for this case and new methods to improve the situation are presented. By using relative features describing the diﬀerence from the reference case rather than the absolute time series, improvements are made compared to state-of-the-art time series classiﬁcation algorithms.
Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots. However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.
Online Motion Style Transfer for Interactive Character Control

Motion style transfer is highly desired for motion generation systems for gaming. Compared to its offline counterpart, the research on online motion style transfer under interactive control is limited. In this work, we propose an end-to-end neural network that can generate motions with different styles and transfer motion styles in real-time under user control. Our approach eliminates the use of handcrafted phase features, and could be easily trained and directly deployed in game systems. In the experiment part, we evaluate our approach from three aspects that are essential for industrial game design: accuracy, flexibility, and variety, and our model performs a satisfying result.
Crash and Citation Records of Young Drivers with Skid Avoidance Training

In this study, 234 recent graduates from a basic driver education course in Montgomery County, Maryland, participated in a skid avoidance and vehicle control course. Traffic crash and citation rates of these participants over the next two years of driving were analyzed and compared to demographically similar subjects without skid training. Results were unclear as to whether the skid avoidance course had an effect on crash rates. This report also discusses the design of effective driver training programs.
Education for Sustainability: Assessing Pathways to the Future

In June 1992, the UN Conference on Environment and Development will be held in Brazil. A follow up to the World Commission on Environment and Development, it will mark the twentieth anniversary of the first UN conference on the environment, held in Stockholm in 1972. Promoting environmental education is one of 23 objectives for the conference.
Assembling the Start-up Brand: A Process Framework for Understanding Strategic Communication Challenges

ABSTRACT The proliferation of start-ups and their contribution to the global economy is well-documented as is their high failure rate, a paradox attributable in large part to the inadequate attention start-ups ascribe to establishing a strong brand. Premised on the idea that brand-building is an active process of meaning-making and an assemblage of layers of professional and material activities, our study offers an empirically grounded approach to start-up branding. Based on an inductive analysis of interviews with co-founders of 15 start-up and scale-up brands, we propose a process framework of start-up branding that highlights the strategic communication processes by which entrepreneurs bring their products to market. Through this contribution, we make clear that entrepreneurial brand building is not a one-size-fits all process and that there is no linear pathway. While the framework is not predictive of success, it is indicative of a new formulation of how strategic communication can be used to understand and evaluate brand processes within entrepreneurial organizations.
Toward a general theory of expatriates’ cross-cultural adjustment

Abstract This study employs a dynamic rather than a static and contextual rather than cultural conceptual framework for expatriate managers’ cross-cultural adjustment. It applies theories of learning-unlearning and of role behavior to explain what expatriates’ values, attitudes and behavior are being adjusted, how they are being adjusted and why they are being adjusted. A dynamic and contextual conceptualization of the process of cross-cultural adjustment refines theory by presenting adjustment as a continuous rather than as sporadic process; by providing a framework for identifying and prioritizing sources of the “push” and “pull” to adjust; and by generalizing theory of cross-cultural adjustment to include sojourners other than expatriates. Data were collected using an intensive literature search of publications in the English language. The study’s conceptual framework has the potential to enhance the development of a general theory of cross-cultural adjustment and to provide organizations with knowledge of how to define, measure and manage expatriate-managers’ adjustment, and, therefore, the ability to develop and employ tools to assist in the selection, training, performance management and evaluation and compensation of expatriates.
Analogies in Entrepreneurial Communication and Strategic Communication: Definition, Delimitation of Research Programs and Future Research

ABSTRACT This article aims to build a better understanding of entrepreneurial communication and strategic communication. The term entrepreneurial is finding its way into the discussion of communication science in general and strategic communication in particular, for example, through the consideration of startups. So far, the term entrepreneurial communication remains vague, is hardly defined and is not systematically distinguished from strategic communication. By applying an analogies lens in the context of problematizing, differences between the terms entrepreneurial and strategic in entrepreneurial communication and strategic communication are explored based on selected given definitions. As a result, three unifying dimensions – development stage, mode, and logic – are developed to highlight fundamental differences between the two terms. To create potential for a future creation of entrepreneurial communication as research program, and to elaborate on future potential for strategic communication in relation to this, central theoretical approaches in entrepreneurship research and their potential to research entrepreneurial communication are explored.
THE DEVELOPMENT OF A LOW COST MOTION ANALYSIS SYSTEM: CEKAK VISUAL 3D V1.0

To date, there is lack of biomechanical characterisation on defensive technique (martial arts) due to the unavailability of appropriate motion tracking system that can be used to characterise the technique in quasi-training environment. Therefore, this paper presents the development of a novel low cost motion analysis system, Cekak Visual 3D v1.0 which is capable to track dual martial art practitioner's skeleton motion in a single frame view using integration of Matlab GUIs and Microsoft Kinect. The accuracy and precision of the coordinate data recorded by the system was tested to ensure the quality of the system. The systems perform the tracking motion with a single Kinect which is a combination of various sensors (RGB and depth sensor) thus makes it capable in providing three dimensional coordinate data. The analysis reveals that Visual Cekak 3D v1.0 resulted lower percentage error with high internal consistency (Cronbach Alpha, α = 0.904). The proposed marker-less system is capable to track and store dual skeleton data in a single session tracking. This capability makes the systems suitable in providing quasi-training and natural setting environment for any martial arts biomechanics investigation. Therefore, we believe that the system provide new concept development for basic research in martial arts biomechanics.
Phylogenetic Analysis of the Dominant Non-biting Midge Forcipomyia bikanni (Diptera: Ceratopogonidae) around West Lake, Hangzhou, China

Abstract The nonbiting midges are tiny insects with a wide distribution around West Lake, China. In this study, the seasonal dynamics of the local dominant midge species was investigated. Phylogenetic trees of this species also were analyzed to discover its evolutionary history. Ultraviolet (UV) light traps were used for surveillance around West Lake in the period from April 2017 to November 2018. External morphological identification was performed using a stereomicroscope. Two genes of adult female midges that encoded the cytochrome oxidase subunit I (COI) and internal transcribed spacer 1 (ITS-1) were sequenced and aligned online using GenBank nucleotide database and the Molecular Evolutionary Genetics Analysis (MEGA) software 10.1. 7. According to the field surveillance in 2017–2018, Forcipomyia bikanni Chan and LeRoux (Diptera: Ceratopogonidae) is the representative dominant species of West Lake, with its activity peaking in August and October. The COI and ITS-1 gene sequences of F. bikanni (XH01-5) were 523 bp and 315 bp, respectively. The final phylogenetic tree of the COI implied that F. bikanni (XH01-5) has 97.9% significant similarity with conspecific F. bikanni (LC015045) from the adjacent Fujian Province, China (E-value ≤ 0.1%). Furthermore, the ITS-1 sequences with 98% likelihood showed the highest linkage between F. bikanni (XH01-5) and F. townsvillensis (Taylor) (HM775502-HM775504) with different branches. Thus, the final unrooted phylogenetic trees of COI and ITS-1 are essential tools for reconstructing the evolutionary histories of nonbiting midges.
Trap Type Affects Catches of Bark and Woodboring Beetles in a Southern Pine Stand

Abstract In 2012, we tested the relative efficacy of four commercial types of insect traps (panel; standard multiple-funnel; modified multiple-funnel; and sea, land, and air Malaise [SLAM]) for capturing bark and woodboring beetles (Coleoptera) in a pine stand in northcentral Georgia. All traps were baited with ethanol, α-pinene, ipsenol, and ipsdienol lures. The SLAM trap outperformed the panel trap for diversity and abundance of Cerambycidae. Mean catches of Asemum striatum (L.) in SLAM traps were greater than those in all other traps. SLAM traps caught more Acanthocinus obsoletus (LeConte) and Xylotrechus sagittatus (Germar) than standard multiple-funnel funnel and panel traps. The greatest numbers of Monochamus titillator (F.) were in SLAM and modified multiple-funnel traps. In contrast, SLAM traps were inferior to all other trap types in trapping bark beetles (Curculionidae: Scolytinae). More Dendroctonus terebrans (Olivier), Hylastes porculus Erichson, and Hylastes salebrosus Eichhoff were captured in panel traps than the other types of traps. Catches of Ips avulsus (Eichhoff), Ips calligraphus (Germar), Orthotomicus caelatus (Eichhoff), and Pityophthorus spp. were the same in panel, standard multiple-funnel, and modified multiple-funnel traps. Our data suggest that combinations of trap types should be considered in maximizing the effectiveness of detection programs for pine bark and woodboring beetles.
One Type Context is Not Enough: Global Context-Aware Neural Machine Translation

How to effectively model global context has been a critical challenge for document-level neural machine translation (NMT). Both preceding and global context have been carefully explored in the sequence-to-sequence (seq2seq) framework. However, previous studies generally map global context into one vector, which is not enough to well represent the entire document since this largely ignores the hierarchy between sentences and words within. In this paper, we propose to model global context for source language from both sentence-level and word-level. Specifically at sentence-level, we extract useful global context for current sentence, while at word-level, we compute global context against words within current sentence. On the basis, both kinds of global context can be appropriately fused before being incorporated into the state-of-the-art seq2seq model, i.e., Transformer. Detailed experimentation on various document-level translation tasks shows that both global context at sentence-level and word-level significantly improve translation performance. More encouraging, both kinds of global context are complementary. This leads to more improvement when both kinds of global context are used.
Survey on the Objectives of Recommender Systems: Measures, Solutions, Evaluation Methodology, and New Perspectives

Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field.
MaPHeA: A Framework for Lightweight Memory Hierarchy-aware Profile-guided Heap Allocation

Hardware performance monitoring units (PMUs) are a standard feature in modern microprocessors, providing a rich set of microarchitectural event samplers. Recently, numerous profile-guided optimization (PGO) frameworks have exploited them to feature much lower profiling overhead compared to conventional instrumentation-based frameworks. However, existing PGO frameworks mainly focus on optimizing the layout of binaries; they overlook rich information provided by the PMU about data access behaviors over the memory hierarchy. Thus, we propose MaPHeA, a lightweight Memory hierarchy-aware Profile-guided Heap Allocation framework applicable to both HPC and embedded systems. MaPHeA guides and applies the optimized allocation of dynamically allocated heap objects with very low profiling overhead and without additional user intervention to improve application performance. To demonstrate the effectiveness of MaPHeA, we apply it to optimizing heap object allocation in an emerging DRAM-NVM heterogeneous memory system (HMS), selective huge-page utilization, and controlling the cacheability of the objects with the low temporal locality. In an HMS, by identifying and placing frequently accessed heap objects to the fast DRAM region, MaPHeA improves the performance of memory-intensive graph-processing and Redis workloads by 56.0% on average over the default configuration that uses DRAM as a hardware-managed cache of slow NVM. By identifying large heap objects that cause frequent TLB misses and allocating them to huge pages, MaPHeA increases the performance of the read and update operations of Redis by 10.6% over the transparent huge-page implementation of Linux. Also, by distinguishing the objects that cause cache pollution due to their low temporal locality and applying write-combining to them, MaPHeA improves the performance of STREAM and RADIX workloads by 20.0% on average over the system without cacheability control.