Miracle-dz/UniFAR-FTUs huggingface dataset:
The development of spatial decision support system tool for marine spatial planning

ABSTRACT A major problem associated with marine spatial planning (MSP) involves the difficult and time-consuming practice of creating a scenario that encompasses complex datasets in near real time via the use of a simple spatial analysis method. Moreover, decision-makers require a reliable, user-friendly system to quickly and accessibly acquire accurate spatial planning information. The development of national spatial data infrastructure (NSDI), which links the spatial data of a nation’s many diverse institutions, may pave the way for the development of a tool that can better utilize spatial datasets, such as a spatial decision support system (SDSS). Thus, this project aimed to develop an SDSS for MSP and to evaluate the feasibility of its integration within the NSDI framework. The seaweed culture was selected as an example due to its economic and technological acceptance by traditional fishers. Additionally, a multi-criteria analysis was used to develop the tool. Furthermore, a feasibility evaluation of its implementation within the NSDI framework was conducted based on the Delphi method. The results of the assessment indicated that the SDSS can be incorporated into the NSDI framework by addressing the policy issue – one map policy, updating custodians’ decree and data, and improve the standard and protocol.
Depression and Obesity: Integrating the Role of Stress, Neuroendocrine Dysfunction and Inflammatory Pathways

Literature on depression and obesity describes the relevance of the hypothalamic pituitary adrenal axis dysfunction, sympathetic nervous system (SNS) activation, and inflammatory processes as well as the interaction of genetic and environmental factors. Recent investigation in obesity highlights the involvement of several regulation systems, particularly in white adipose tissue. The hypothalamic pituitary adrenal axis, gonadal, growth hormone, leptin, sympathetic nervous system and adrenergic, dopaminergic, and serotoninergic central pathways, all seem interconnected and involved in obesity. From another perspective, the role of psychosocial chronic stressors, determining poor mental and physical health, is well documented. Empirical data can support biologically conceivable theories describing how perceptions of the external social environment are transduced into cellular inflammation and depression. Although in neurobiological models of depression, stress responses are associated with neuroendocrine and neuro-inflammatory processes, concerning similar pathways to those described in obesity, an integrating model is still lacking. The aim of this mini-review is to offer a reflexion on the interplay between the neuroendocrine dysfunctions related to chronic stress and the nature of the shared biologic mechanisms in the pathophysiology of both clinical entities, depression and obesity. We highlight dysfunctional answers of mind body systems that are usually activated to promote regulation and adaptation. Stress response, as a mediator between different level phenomena, may undertake the role of a plausible link between psychological and biological determinants of disease. Depression and obesity are major public health issues, urging for new insights and novel interventions and this discussion points to the need of a more in-depth approach.
Best Arm Identification in Linear Bandits with Linear Dimension Dependency

We study the best arm identification problem in linear bandits, where the mean reward of each arm depends linearly on an unknown d-dimensional parameter vector θ, and the goal is to identify the arm with the largest expected reward. We first design and analyze a novel randomized θ estimator based on the solution to the convex relaxation of an optimal G-allocation experiment design problem. Using this estimator, we describe an algorithm whose sample complexity depends linearly on the dimension d, as well as an algorithm with sample complexity dependent on the reward gaps of the best d arms, matching the lower bound arising from the ordinary top-arm identification problem. We finally compare the empirical performance of our algorithms with other state-of-theart algorithms in terms of both sample complexity and computational time.
A mixed-method empirical study of Function-as-a-Service software development in industrial practice

Function-as-a-Service (FaaS) describes cloud computing services that make infrastructure components transparent to application developers, thus falling in the larger group of “serverless” computing models. When using FaaS offerings, such as AWS Lambda, developers provide atomic and short-running code for their functions, and FaaS providers execute and horizontally scale them on- demand . Currently, there is no systematic research on how developers use serverless, what types of applications lend themselves to this model, or what architectural styles and practices FaaS-based applications are based on. We present results from a mixed-method study, combining interviews with advanced practitioners, a systematic analysis of grey literature, and a Web-based survey. We find that successfully adopting FaaS requires a different mental model, where systems are primarily constructed by composing pre-existing services, with FaaS often acting as the “glue” that brings these services together. Tooling availability and maturity, especially related to testing and deployment, remains a major difficulty. Further, we find that current FaaS systems lack systematic support for function reuse, and abstractions and programming models for building non-trivial FaaS applications are limited . We conclude with a discussion of implications for FaaS providers, software developers, and researchers.
Dual-Mode Wake-Up Nodes for IoT Monitoring Applications: Measurements and Algorithms

Internet of Things (IoTs)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energy-efficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data- and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms which select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications.
Language Identification and Analysis of Code-Switched Social Media Text

In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset. In this regard, we build a new code-switched dataset for Hindi-English. To understand the code-switching patterns in these language pairs, we investigate different code-switching metrics. We find that the CRF model outperforms the neural network based models by a margin of 2-5 percentage points for Spanish-English and 3-5 percentage points for Hindi-English.
On the largest part size and its multiplicity of a random integer partition

Abstract Let λ be a partition of the positive integer n chosen uniformly at random among all such partitions. Let Ln = Ln(λ) and Mn = Mn(λ) be the largest part size and its multiplicity, respectively. For large n, we focus on a comparison between the partition statistics Ln and LnMn. In terms of convergence in distribution, we show that they behave in the same way. However, it turns out that the expectation of LnMn – Ln grows as fast as 12logn {1 \over 2}\log n . We obtain a precise asymptotic expansion for this expectation and conclude with an open problem arising from this study.
The Numerical Invariant Measure of Stochastic Differential Equations With Markovian Switching

The existence and uniqueness of the numerical invariant measure of the backward Euler-Maruyama method for stochastic differential equations with Markovian switching is yielded, and it is revealed that the numerical invariant measure converges to the underlying invariant measure in the Wasserstein metric. Under the polynomial growth condition of drift term the convergence rate is estimated. The global Lipschitz condition on the drift coefficients required by Bao et al., 2016 and Yuan et al., 2005 is released. Several examples and numerical experiments are given to verify our theory.
Versatile event correlation with algebraic effects

We present the first language design to uniformly express variants of n-way joins over asynchronous event streams from different domains, e.g., stream-relational algebra, event processing, reactive and concurrent programming. We model asynchronous reactive programs and joins in direct style, on top of algebraic effects and handlers. Effect handlers act as modular interpreters of event notifications, enabling fine-grained control abstractions and customizable event matching. Join variants can be considered as cartesian product computations with ”degenerate” control flow, such that unnecessary tuples are not materialized a priori. Based on this computational interpretation, we decompose joins into a generic, naive enumeration procedure of the cartesian product, plus variant-specific extensions, represented in terms of user-supplied effect handlers. Our microbenchmarks validate that this extensible design avoids needless materialization. Alongside a formal semantics for joining and prototypes in Koka and multicore OCaml, we contribute a systematic comparison of the covered domains and features.
Siphon: Expediting Inter-Datacenter Coflows in Wide-Area Data Analytics

It is increasingly common that large volumes of production data originate from geographically distributed datacenters. Processing such datasets with existing data parallel frameworks may suffer from significant slowdowns due to the much lower availability of inter-datacenter bandwidth. Thus, it is critical to optimize the delivery of inter-datacenter traffic, especially coflows that imply application-level semantics, to improve the performance of such geo-distributed applications. In this paper, we present Siphon, a building block integrated in existing data parallel frameworks (e.g., Apache Spark) to expedite their generated inter-datacenter coflows at runtime. Specifically, Siphon serves as a transport service that accelerates and schedules the inter-datacenter traffic with the awareness of workload-level dependencies and performance, while being completely transparent to analytics applications. Novel intra-coflow and inter-coflow scheduling and routing strategies have been designed and implemented in Siphon, based on a software-defined networking architecture. On our cloud-based testbeds, we have extensively evaluated Siphon's performance in accelerating coflows generated by a broad range of workloads. With a variety of Spark jobs, Siphon can reduce the completion time of a single coflow by up to 76%. With respect to the average coflow completion time, Siphon outperforms the state-of-the-art scheme by 10%.
Detail Highlighting using a Shadow Edge Histogram

In this paper we propose a simple and effective technique for setting up a conﬁguration of directional light sources to accentuate the prominent geometric features of complex objects by increasing the local shadow contrast near them. Practical applications of such a task are encountered among others in professional photography, and cinematography. The method itself, which is based on a voting mechanism, quickly produces consistent and view-independent results, with minimal user intervention. Categories and Subject Descriptors (according to ACM CCS) : I.3. 7 [Computer Graphics]: Three-Dimensional Graphics and Realism—shading, shadowing I.3. 4 [Computer Graphics]: Graphics Utilities—Graphics editors
A Two-Level Scheme for Quality Score Compression

Previous studies on quality score compression can be classified into two main lines: lossy schemes and lossless schemes. Lossy schemes enable a better management of computational resources. Thus, in practice, and for preliminary analyses, bioinformaticians may prefer to work with a lossy quality score representation. However, the original quality scores might be required for a deeper analysis of the data. Hence, it might be necessary to keep them; in addition to lossy compression this requires lossless compression as well. We developed a space-efficient hierarchical representation of quality scores, QScomp, which allows the users to work with lossy quality scores in routine analysis, without sacrificing the capability of reaching the original quality scores when further investigations are required. Each quality score is represented by a tuple through a novel decomposition. The first and second dimensions of these tuples are separately compressed such that the first-level compression is a lossy scheme. The compressed information of the second dimension allows the users to extract the original quality scores. Experiments on real data reveal that the downstream analysis with the lossy part-spending only 0.49 bits per quality score on average-shows a competitive performance, and that the total space usage with the inclusion of the compressed second dimension is comparable to the performance of competing lossless schemes.
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions on a Riemannian manifold. The present paper proposes a Riemannian stochastic recursive gradient algorithm (R-SRG), which does not require the inverse of retraction between two distant iterates on the manifold. Convergence analyses of R-SRG are performed on both retractionconvex and non-convex functions under computationally efficient retraction and vector transport operations. The key challenge is analysis of the influence of vector transport along the retraction curve. Numerical evaluations reveal that R-SRG competes well with state-of-the-art Riemannian batch and stochastic gradient algorithms.
Intratumoral Accumulation and Clonal Expansion May Not Be Decisive for Rejection of Allogeneic Tumors by CD8+ T-Lymphocytes

Background: The aim of this study was to analyze the spatial distribution and proliferation of adoptively transferred CD8+ T-lymphocytes sensitized against allogeneic tumors. Materials and Methods: Transgenic β-actin-luc mice that express luciferase were sensitized against allogeneic SL2 lymphoma. CD8+ T-lymphocytes from these mice were transferred to lymphocyte-deficient, recombination activating gene-deficient (Rag−/−) mice bearing SL2 tumors and were tracked using bioluminescence imaging. Results: Two out of six Rag−/− mice rejected their tumors. There were no apparent differences in spatial distribution and proliferative intensity of adoptively-transferred CD8+ T-lymphocytes between the two Rag−/− mice that rejected allogeneic SL2 tumors and the four Rag−/− mice that did not. Conclusion: The pattern of distribution in the mouse body and proliferative intensity of CD8+ T-lymphocytes do not seem to be decisive factors influencing allogeneic tumor rejection.
Agent-Based Simulation of Offender Mobility: Integrating Activity Nodes from Location-Based Social Networks

In recent years, simulation techniques have been applied to investigate the spatio-temporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with prevention strategies, and crime prediction purposes, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents an agent-based model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. To instantiate a realistic urban environment, we use open data to simulate the urban structure and location-based social networks data to represent activity nodes as proxy for human activity. 18 mobility strategies have been tested, combining search distance strategies (e.g. Le vy flight, inspired by insights in human dynamics literature) and destination selection strategies (enriched with Foursquare data). We analyze and compare the different mobility strategies, and show the impact of using activity nodes extracted from social networks to simulate offender mobility. This agent-based model provides a basis for comparing offender mobility in crime simulations by inferring offender mobility in urban areas from real world data.
A Spline Theory of Deep Networks

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-afﬁne spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. For instance, conditioned on the input signal, the output of a MASO DN can be written as a simple afﬁne transformation of the input. This implies that a DN constructs a set of signal-dependent, class-speciﬁc templates against which the signal is compared via a simple inner product; we explore the links to the classical theory of optimal classiﬁcation via matched ﬁlters and the effects of data memorization. Going further, we propose a simple penalty term that can be added to the cost function of any DN learning algorithm to force the templates to be orthogonal with each other; this leads to signif-icantly improved classiﬁcation performance and reduced overﬁtting with no change to the DN architecture. The spline partition of the input signal space opens up a new geometric avenue to study how DNs organize signals in a hierarchical fashion. As an application, we develop and validate a new distance metric for signals that quantiﬁes the difference between their partition encodings.
Richards-Campbell sleep questionnaire: psychometric properties of Chinese critically ill patients.

BACKGROUND Sleep abnormalities occur frequently in critically ill patients. Nurses are strategically placed, specifically in intensive care units, to promote sleep in such patients. Currently, an effective sleep assessment tool in Chinese is not available for intensive care settings. AIM This study aimed to assess the reliability and validity of the Chinese version of the Richards-Campbell Sleep Questionnaire (RCSQ-C). It also aimed to evaluate patient-nurse reliability and agreement of the RCSQ-C in the intensive care unit (ICU). METHODS We translated the original RCSQ into Chinese and then back-translated it into English to ensure its accuracy of translation. Internal consistency, discrimination validity and construct validity of the RCSQ-C were examined in 150 critically ill patients. The convergent validity of the RCSQ-C was evaluated in 44 of 150 critically ill patients, and data from the RCSQ-C were compared with those of the Chinese version of St Mary's Hospital Sleep Questionnaire (SMHSQ). Comparisons were also made between RCSQ-C scores obtained from patients and their nurses. RESULTS Cronbach's α of the RCSQ-C was 0.923; thus, it showed high reliability. The corrected item-total correlation coefficient was in the range of 0·680∼0·805, which showed that the items were homogeneous for evaluating sleep. The content validity was 0·84. One factor was extracted with a cumulative contribution rate of 76·597%. Confirmatory factor analysis showed that the original single-factor structure proposed by Richards adequately fit the data. The RCSQ-C could discriminate poor and good sleepers, which supported discriminant validity. There was a close correlation between the scores obtained from the RCSQ patient's version and those from the SMHSQ. The intraclass correlation coefficients of the patient and nurse ranged from 0·315 to 0·609. CONCLUSIONS The psychometric properties of the RCSQ-C suggest its utility in critically ill patients. Patient-nurse reliability on the RCSQ-C was "fair" to "substantial", with nurses tending to overestimate patients' perceived sleep quality. RELEVANCE TO CLINICAL PRACTICE If the validity of this questionnaire is supported in other ICU samples, RCSQ-C could be used as a routine evaluation instrument to distinguish good and poor sleepers and then direct nurses to form corresponding treatment plans to promote sleep.
Nuclear Magnetic Resonance Spectrum Inversion Based on the Residual Hybrid  $l_{1}$ / $l_{2}$  Norm

The residual <inline-formula> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> norm is commonly used to measure the fitting error for a nuclear magnetic resonance (NMR) spectrum inversion. However, the solution of the objective function with the residual <inline-formula> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> norm is sensitive to noise. When <inline-formula> <tex-math notation="LaTeX">$1 \le p < 2$ </tex-math></inline-formula>, the solution of the objective function with the residual <inline-formula> <tex-math notation="LaTeX">$l_{p}$ </tex-math></inline-formula> norm is less sensitive to noise. In this letter, we develop a method for NMR spectrum inversion based on the residual hybrid <inline-formula> <tex-math notation="LaTeX">$l_{1}/l_{2}$ </tex-math></inline-formula> norm, which behaves like <inline-formula> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> norm for small residuals and like <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> norm for large residuals. The numerical results of the 1-D <inline-formula> <tex-math notation="LaTeX">$T_{2}$ </tex-math></inline-formula> inversion and the 2-D <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$T_{2}$ </tex-math></inline-formula> inversion show that the residual hybrid <inline-formula> <tex-math notation="LaTeX">$l_{1}/l_{2}$ </tex-math></inline-formula> norm method provides an inversion result with a smaller porosity error and exhibits a better ability to distinguish the fluid spectrum peaks.
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.