Miracle-dz/UniFAR-FTUs huggingface dataset:
CN-Celeb: A Challenging Chinese Speaker Recognition Dataset

Recently, researchers set an ambitious goal of conducting speaker recognition in unconstrained conditions where the variations on ambient, channel and emotion could be arbitrary. However, most publicly available datasets are collected under constrained environments, i.e., with little noise and limited channel variation. These datasets tend to deliver over-optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.In this paper, we present CN-Celeb, a large-scale speaker recognition dataset collected ‘in the wild’. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. Experiments conducted with two state-of-the-art speaker recognition approaches (i-vector and x-vector) show that the performance on CN-Celeb is far inferior to the one obtained on Vox-Celeb, a widely used speaker recognition dataset. This result demonstrates that in real-life conditions, the performance of existing techniques might be much worse than it was thought. Our database is free for researchers and can be downloaded from http://project.cslt. org.
When Choosing Plausible Alternatives, Clever Hans can be Clever

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT’s and RoBERTa’s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT’s and RoBERTa’s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.
Review: Ordinary Differential Equations For Deep Learning

To better understand and improve the behavior of neural networks, a recent line of works bridged the connection between ordinary differential equations (ODEs) and deep neural networks (DNNs). The connections are made in two folds: (1) View DNN as ODE discretization; (2) View the training of DNN as solving an optimal control problem. The former connection motivates people either to design neural architectures based on ODE discretization schemes or to replace DNN by a continuous model characterized by ODEs. Several works demonstrated distinct advantages of using a continuous model instead of traditional DNN in some specific applications. The latter connection is inspiring. Based on Pontryagin's maximum principle, which is popular in the optimal control literature, some developed new optimization methods for training neural networks and some developed algorithms to train the infinite-deep continuous model with low memory-cost. This paper is organized as follows: In Section 2, the relation between neural architecture and ODE discretization is introduced. Some architectures are not motivated by ODE, but they are later found to be associated with some specific discretization schemes. Some architectures are designed based on ODE discretization and expected to achieve some special properties. Section 3 formulates the optimization problem where a traditional neural network is replaced by a continuous model (ODE). The formulated optimization problem is an optimal control problem. Therefore, two different types of controls will also be discussed in this section. In Section 4, we will discuss how we can utilize the optimization methods that are popular in optimal control literature to help the training of machine learning problems. Finally, two applications of using a continuous model will be shown in Section 5 and 6 to demonstrate some of its advantages over traditional neural networks.
Linear-quadratic control for a class of stochastic Volterra equations: Solvability and approximation

We provide an exhaustive treatment of Linear-Quadratic control problems for a class of stochastic Volterra equations of convolution type, whose kernels are Laplace transforms of certain signed matrix measures which are not necessarily finite. These equations are in general neither Markovian nor semimartingales, and include the fractional Brownian motion with Hurst index smaller than $1/2$ as a special case. We establish the correspondence of the initial problem with a possibly infinite dimensional Markovian one in a Banach space, which allows us to identify the Markovian controlled state variables. Using a refined martingale verification argument combined with a squares completion technique, we prove that the value function is of linear quadratic form in these state variables with a linear optimal feedback control, depending on non-standard Banach space valued Riccati equations. Furthermore, we show that the value function of the stochastic Volterra optimization problem can be approximated by that of conventional finite dimensional Markovian Linear--Quadratic problems, which is of crucial importance for numerical implementation.
Serious sport tourism and event travel careers

This article seeks to advance theory pertaining to serious sport tourism, through the application of serious leisure and ego-involvement theory and the analysis of a survey of participants in the TransRockies Challenge mountain-bike event. Participants were questioned postevent about their motives, involvement in their sport, event-related travel, and destination and event preferences. Analysis revealed that most respondents were highly involved in competitive mountain biking, and were primarily motivated by self development through meeting a challenge. Many respondents also participated in a portfolio of other competitive sport events that provided similar personal rewards. Results suggest that many serious sport tourists develop travel careers centered on competitive events. A hypothetical framework for assessing six dimensions of event travel career trajectories is developed, leading to consideration of practical management implications and research needs.
WEIZZ: automatic grey-box fuzzing for structured binary formats

Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, we propose a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We identify dependencies between input bytes and comparison instructions, and use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. Our technique can perform comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation WEIZZ revealed 16 unknown bugs in widely used programs.
Connecting ontological (in)securities and generation through the everyday and emotional geopolitics of Falkland Islanders*

Abstract Debates about the security of British Overseas Territories (OTs) like the Falkland Islands are typically framed through the discourses of formal and practical geopolitics in ways that overlook the perspectives of their citizens. This paper focuses on the voices of two generations of citizens from the Falkland Islands, born before and after the 1982 war, to show how they perceive geopolitics and (in)security in different ways. It uses these empirical insights to show how theorisations of ontological (in)security might become more sensitive to the lived experiences of diverse generational groups within states and OTs like the Falklands. The paper reflects on the complex experiences of citizens living in a postcolonial OT that still relies heavily on the UK Government and electorate for assurances of security, in the face of diplomatic pressure from Argentina. While Islander youth reflected on how their views about geopolitics and security might be considered marginal, relative to those who directly experienced geopolitical events in the Falklands during the second half of the twentieth century, the paper illustrates the multiple ways they can act as agents of (in)security.
The impact of elevation, topography and snow load damage of trees on the position of the actual timberline on the fells in central Finnish Lapland

This study examines the impact of selected environmental variables on the position of the limit of continuous forest (actual timberline) on three fells situated in central Finnish Lapland, namely Aakenustunturi, Yllastunturi and Pyhatunturi. The factors studied are elevation, topography and snow load damage to coniferous trees. The potential for ascending timberlines in the region is discussed. The limit of continuous forest in the study area is hardly ever composed of a single species but rather features alternating occurrences of spruce, pine and mountain birch. The average altitudinal position of the actual timberline is highest on the southern and western slopes (366–428 m a.s.l.), while the lowest altitudes are recorded on the northern and eastern slopes (336–403 m a.s.l.). Prevalence of block fields and slope gradient are the most significant and visible factors controlling the position of the actual timberline. On the studied fells, from 10% to more than 50% of the total length of the actual timberline is controlled by block fields. The lowest occurrence of the limit of continuous forest, at 270 m a.s. l. , results from an extremely high proportion of block fields and steep slopes. Snow patches and delayed snow melting also hamper the upward advancement of the forest. Continuous forest reaches its highest altitudes at 440 m a.s. l. on slopes with gentle inclination and land cover other than block fields, and 460 m a.s. l. in sheltered furrows. Average snow load damage to spruce and pine at the actual timberline varies in the ranges 15–58% and 30–76%, respectively. Snow load damages on trees undoubtedly impede the advance of the actual timberline to higher elevation in all exposures. Seedling density (number/ha) on south-facing slopes near the actual timberline is ca. 5 times greater than on the north-facing slopes. Recent regeneration and seedling establishment is almost absent at the treeline near the fell tops. Results suggest that there is potential for actual timberline advance on south-facing slopes with gentle inclination and missing block fields. It is uncertain, however, whether natural regeneration will take place on felltops with few or no trees.
Clinical Depression and Affect Recognition with EmoAudioNet

Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval. Understanding human's specific and basic emotions and reacting accordingly can improve HCI. Besides, giving machines skills to understand human's emotions when interacting with other humans can help humans with a socio-affective intelligence. In this paper, we present a deep Neural Network-based architecture called EmoAudioNet which studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Two applications are performed using EmoAudioNet : automatic clinical depression recognition and continuous dimensional emotion recognition from speech. The extensive experiments showed that the proposed approach significantly outperforms the state-of-art approaches on RECOLA and DAIC-WOZ databases. The competitive results call for applying EmoAudioNet on others affects and emotions recognition from speech applications.
Deployment Architectures for Cyber-Physical Control Systems

We consider the problem of how to deploy a controller to a (networked) cyber-physical system (CPS). Con-trolling a CPS is an involved task, and synthesizing a controller to respect sensing, actuation, and communication constraints is only part of the challenge. In addition to controller synthesis, one should also consider how the controller will work in the CPS. Put another way, the cyber layer and its interaction with the physical layer need to be taken into account.In this work, we aim to bridge the gap between theoretical controller synthesis and practical CPS deployment. We adopt the system level synthesis (SLS) framework to synthesize a state-feedback controller and provide a deployment architecture for the standard SLS controller. Furthermore, we derive a new controller realization for open-loop stable systems and introduce four different architectures for deployment, ranging from fully centralized to fully distributed. Finally, we compare the trade-offs among them in terms of robustness, memory, computation, and communication overhead.
Sequential/Spatial, a Survey of Interactive Information Retrieval Methods for Controlled Experimentation and Evaluation

This survey presents studies that investigated non-spatial (sequential) and spatial information retrieval systems in parallel during a battery of information-seeking tasks with respect to user navigational behaviors, incidental learning, retrieval performance, cognitive abilities & load, direct manipulation of 2D & 3D interfaces, and satisfaction. I consider how information theory has contributed to the concepts of foraging, sense-making, exploration, and how the applied areas of interactive information retrieval (IIR) and cognitive/behavioral psychology have implemented these concepts into architecture, interface design, experimental design, user study, and evaluation methodology.
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition

In recent years, Deep Learning (DL) models are becoming important due to their demonstrated success at overcoming complex learning problems. DL models have been applied effectively for different Natural Language Processing (NLP) tasks such as part-of-Speech (PoS) tagging and Machine Translation (MT). Disease Named Entity Recognition (Disease-NER) is a crucial task which aims at extracting disease Named Entities (NEs) from text. In this paper, a DL model for Disease-NER using dictionary information is proposed and evaluated on National Center for Biotechnology Information (NCBI) disease corpus and BC5CDR dataset. Word embeddings trained over general domain texts as well as biomedical texts have been used to represent input to the proposed model. This study also compares two different Segment Representation (SR) schemes, namely IOB2 and IOBES for Disease-NER. The results illustrate that using dictionary information, pre-trained word embeddings, character embeddings and CRF with global score improves the performance of Disease-NER system.
Developing a knowledge base for educational leadership and management in East Asia

The role of school leadership in educational reform has reached the status of a truism, and led to major changes in school leader recruitment, selection, training and appraisal. While similar policy trends are evident in East Asia, the empirical knowledge base underlying these measures is distorted and lacking in validation. This paper begins by offering evidence in support of this assertion. Then it examines the process of knowledge production as it has unfolded in Western academic contexts. Finally, it assesses the context in East Asia and offers recommendations for accelerating the development of a regionally valid knowledge base.
Time-evolution of magnetic field in hot nuclear matter with fluctuating topological charge

The time-evolution of the magnetic field in hot homogeneous nuclear matter has two qualitatively different stages separated by the sphaleron transition time $\tau_c$. At early times the axial chemical potential and the corresponding chiral conductivity $\sigma_\chi$ are slow functions of time. The soft chiral modes $k<\sigma_\chi$ of the magnetic field grow exponentially with time, which is known as the chiral instability. At later times $\sigma_\chi$ fluctuates due to the sphaleron transitions and can be regarded as a random process. It is argued that the average magnetic field is exponentially damped at later times. The time-evolution of the average magnetic energy is more complicated and depends on the electrical conductivity of the chiral matter but does not depend on chirality. It exhibits instability only if the matter is a poor electrical conductor, such as the quark-gluon plasma near the critical temperature. The precise conditions for the instability and the growth rate of the unstable modes are derived.
Learning Optimal Search Algorithms from Data

Classical algorithm design is geared towards worst case instances and fails to exploit structure that may be present in typical instances. Is it possible to learn this structure from examples and exploit it algorithmically? We study this question in the simplest algorithmic context -- search for a cheap solution within an unstructured space. This setting captures, for example, search for a short path to drive to work when only some routes may ever be relevant to consider, or shopping online when there may only be a handful of stores that offer the best prices. We propose a framework for learning optimal search algorithms from data that captures the tradeoff between the cost of the solution and the time to find it. We consider a setting with $n$ alternatives each having an unknown cost that can be revealed one at a time. Given sample access to the distribution of the costs, our goal is to learn an algorithm that minimizes the expected sum of the cost of the chosen alternative and the total time to find it. Algorithms for this problem fall into three different classes, non-adaptive which always query a fixed set of alternatives, partially-adaptive that query alternatives in a fixed order until they decide to stop and fully-adaptive that choose the next query based on the costs they've seen. While approximately optimal fully-adaptive strategies cannot be learned efficiently, our main result is that it is possible to learn a partially-adaptive strategy that approximates the best non-adaptive and partially-adaptive strategies efficiently both in terms of samples and computation. We extend our results to settings where multiple alternatives must be chosen and study the case where any $k$ alternatives are feasible and the case where the alternatives must form a matroid base e.g. picking a minimum cost spanning tree.
A Merge-Split Proposal for Reversible Monte Carlo Markov Chain Sampling of Redistricting Plans

We describe a Markov chain on redistricting plans that makes relatively global moves. The chain is designed to be usable as the proposal in a Markov Chain Monte Carlo (MCMC) algorithm. Sampling the space of plans amounts to dividing a graph into a partition with a specified number elements which each correspond to a different district. The partitions satisfy a collection of hard constraints and the measure may be weighted with regard to a number of other criteria. When these constraints and criteria are chosen to align well with classical legal redistricting criteria, the algorithm can be used to generate a collection of non-partisan, neutral plans. This collection of plans can serve as a baseline against which a particular plan of interest is compared. If a given plan has different racial or partisan qualities than what is typical of the collection plans, the given plan may have been gerrymandered and is labeled as an outlier.
iConflicts of Utilization and Sustainable Production Forest Management n Forest Management Unit of Meranti South Sumatera in Ja

Utilization conflicts among users threatening the sustainability of forest management. This study is to determine the intensity of conflict and the sustainability of forest management by integrating the convergent in FMU Meranti parallel mixed method and Rapid Land Tenure Assessment. Data was collected through interviews with key informants, literatures, maps, documents, and histories studies; as well as focus groups discussions in eight villages around the . results showed that the people's bundle of right, the interaction and social capital, FMU Meranti The dispute against the coorporation's financial modal. Generally, the interaction and social capital elements are owned by the local community, while the financial modal is owned by the permit holder and the local elite. Based on the power analysis on each object, the power of both community and permit holders are equally strong. The conflict intensity is subdivided into three conflict categories appear and latent conflicts , i.e.: open (31%), (44%), (25%). The special area (wilayah tertentu) open are . and access area highly prioritized, that need conflict resolution The existence of the environmental damages, unclear ownership rights, lack of stakeholder supporting and , non-optimal institutional performance lead the sustainable forest management to bad category (40.6%).
A MODIFIED MICROSTRIP-LINE TYPED PHOTONIC BAND-GAP (PBG) STRUCTURE THROUGH COMBINATION OF INDUCTIVE AND CAPACITIVE ELEMENTS FOR S-PARAMETER ENHANCEMENT

Microwave Photonic Bandgap (PBG) structures are conventionally realized by cascading periodic distributive elements. In microstrip-line circuits, such PBG properties can be obtained by placing periodic perforated holes on the ground plane along the direction of propagation. Nevertheless, the spectral properties may be improved by additionally introducing an inductive element in conjunction to the perforated holes on the PBG circuit. This inductive element is realized by tapering the main strip on the sections corresponding to the perforated holes. Through comparison of the scattering (S) parameters, the PBG structure with combined inductive and capacitive elements is superior to both with either purely inductive or capacitive elements.
Organizational response through innovative HRM and re-design: a comparative study from France and India

This paper explores some of the major developments in cross-national, comparative studies of multi-national enterprises (MNEs) through qualitative research in human resource management (HRM) and then highlights the need for more such studies. With exploratory case study methodology, this study tries to understand the role of innovative HRM practices during organizational re-design in France and India. It highlights the necessity of context and builds on with the results of four longitudinal case studies in two industries, namely cement and automobile, in France and India, which attempted to operate in a competitive business environment. The results emphasize that given the context, amidst the backdrop of liberalization, a crucial challenge facing organizations is their ability to respond and manage re-designing their organizations while embracing integrative and innovative human resource (HR) mechanisms that cement these changes. The research setting of two countries helps to dig deeper into the mechanisms that shape innovative HR policies and practices. The article proposes and contributes to the contingency theory and to cross-national, comparative MNE literature.