Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic Tools and Selective Breeding in Molluscs

The production of most farmed molluscs, including mussels, oysters, scallops, abalone, and clams, is heavily dependent on natural seed from the plankton. Closing the lifecycle of species in hatcheries can secure independence from wild stocks and enables long-term genetic improvement of broodstock through selective breeding. Genomic techniques have the potential to revolutionize hatchery-based selective breeding by improving our understanding of the characteristics of mollusc genetics that can pose a challenge for intensive aquaculture and by providing a new suite of tools for genetic improvement. Here we review characteristics of the life history and genetics of molluscs including high fecundity, self-fertilization, high genetic diversity, genetic load, high incidence of deleterious mutations and segregation distortion, and critically assess their impact on the design and effectiveness of selective breeding strategies. A survey of the results of current breeding programs in the literature show that selective breeding with inbreeding control is likely the best strategy for genetic improvement of most molluscs, and on average growth rate can be improved by 10% per generation and disease resistance by 15% per generation across the major farmed species by implementing individual or family-based selection. Rapid advances in sequencing technology have resulted in a wealth of genomic resources for key species with the potential to greatly improve hatchery-based selective breeding of molluscs. In this review, we catalog the range of genomic resources currently available for molluscs of aquaculture interest and discuss the bottlenecks, including lack of high-quality reference genomes and the relatively high cost of genotyping, as well as opportunities for applying genomics-based selection.
CT findings of adnexal torsion: A matched case-control study

Objective The purpose of our study was to assess computed tomographic (CT) findings of adnexal torsion through a matched case-control analysis. Materials and methods This retrospective, single-institution case-control study included 43 women with adnexal torsion and 43 age- and ovarian mass-matched control women. CT images were evaluated independently by two readers for the following: prominent peripheral follicles, uterine deviation, thickened pedicles, a whirl sign, and a navel sign. Comparisons of CT findings were performed using the Chi square test and receiver operating characteristic (ROC) curves were obtained to assess the diagnostic performance. Differences between the areas under the ROC curves (AUCs) were compared by using a Delong test. Results The CT findings significant for adnexal torsion were uterine deviation toward the side of the affected ovary (P = < .01 for reader 1 and P = .02 for reader 2) and thickened pedicles with ancillary findings including a whirl sign, a navel sign, and uterine deviation facing thickened pedicles (P < .01 for both readers). Thickened pedicles with ancillary findings had the highest diagnostic accuracy, as measured with ROC curves (AUC, 0.86 in reader 1 and 0.85 in reader 2). Combining uterine deviation toward the side of the affected ovary with thickened pedicles with ancillary findings did not increase the performance relative to that of thickened pedicles with ancillary findings alone. Conclusions Thickened pedicles with ancillary findings including a whirl sign, a navel sign, and uterine deviation facing thickened pedicles could be helpful for the diagnosis of adnexal torsion.
Improving inference of the dynamic biological network underlying aging via network propagation

Gene expression (GE) data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.) However, GE analyses ignore physical interactions between gene products, i.e., proteins. Since proteins function by interacting with each other, and since biological networks (BNs) capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP) to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific network, even though cellular processes are dynamic. A dynamic process of our interest is aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related) network. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP.
On Computing the Resultant of Generic Bivariate Polynomials

An algorithm is presented for computing the resultant of two generic bivariate polynomials over a field K. For such p and q K[x,y] both of degree d in x and n in y , the algorithm computes the resultant with respect to y using (n2 - 1/ømega d) 1+o(1) arithmetic operations in K, where two n x n matrices are multiplied using O(nømega) operations. Previous algorithms required time (n2 d) 1+o(1). The resultant is the determinant of the Sylvester matrix S(x) of p and q , which is an n x n Toeplitz-like polynomial matrix of degree~ d . We use a blocking technique and exploit the structure of S(x) for reducing the determinant computation to the computation of a matrix fraction description R(x)Q(x)-1 of an m x m submatrix of the inverse S(x)-1, where młl n. We rely on fast algorithms for handling dense polynomial matrices: the fraction description is obtained from an x -adic expansion via matrix fraction reconstruction, and the resultant as the determinant of the denominator matrix. We also describe some extensions of the approach to the computation of generic Gröbner bases and of characteristic polynomials of generic structured matrices and in univariate quotient algebras.
Privacy-Preserving Multi-Period Demand Response: A Game Theoretic Approach

We study a multi-period demand response problem in the smart grid with multiple companies and their consumers. We model the interactions by a Stackelberg game, where companies are the leaders and consumers are the followers. It is shown that this game has a unique equilibrium at which the companies set prices to maximize their revenues while the consumers respond accordingly to maximize their utilities subject to their local constraints. Billing minimization is achieved as an outcome of our method. Closed-form expressions are provided for the strategies of all players. Based on these solutions, a power allocation game has been formulated, and which is shown to admit a unique pure-strategy Nash equilibrium, for which closed-form expressions are provided. For privacy, we provide a distributed algorithm for the computation of all strategies. We study the asymptotic behavior of equilibrium strategies when the numbers of periods and consumers grow. We find an appropriate company-to-user ratio for the large population regime. Furthermore, it is shown, both analytically and numerically, that the multi-period scheme, compared with the single-period one, provides more incentives for energy consumers to participate in demand response. We have also carried out case studies on real life data to demonstrate the benefits of our approach, including billing savings of up to 30\%.
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes

Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the latter suffers from intractable computation and can lead to overfitting. Taking the instantaneous stationarity idea, we construct a non-stationary GP with the stationarity parameter individually set at each location. Then we utilize the non-parametric mixture model to reduce the effective number of unique parameters. Different from a simple mixture of independent GPs, the mixture in stationarity allows the components to be spatial correlated, leading to improved prediction efficiency. Theoretical properties are examined and a linearly scalable algorithm is provided. The application is shown through several simulated scenarios as well as the massive spatiotemporally correlated temperature data.
Effects of Dance Interventions on Aspects of the Participants' Self: A Systematic Review

Background: Theoretical and empirical studies indicate that dance can strengthen the participants' self. The aim of the systematic review is to give an overview of studies investigating the effects of dance interventions on aspects of the self (e.g., self-concept/-esteem). Research questions are: (a) What is the evidence of the reported effects on different aspects of the self in children/adolescents and in adults? (b) Which study approaches and designs are used and what characterizes the interventions? (c) What are the qualitative facets of the implemented studies and what are issues for future research? Methods: We searched online databases for English and German journal articles with the following main inclusion criteria: (i) Intervention study (qualitative and quantitative approaches) (ii) Investigation of aspects of the self (iii) Dance as intervention content. Two reviewers independently screened studies for eligibility using the PRISMA guidelines and assessed the methodological quality of the included studies. Results: Out of 24 included studies, 11 investigate a sample of children/adolescents and 13 an adult sample. The review showed that dance interventions can have positive effects on aspects of the participants' self. The review of studies with qualitative methodologies suggests: children/adolescents benefit in body-related perceptions, self-trust, self-esteem, self-expression and perception of dance-abilities; adults benefit in self-expression, self-efficacy, self-/body-awareness, self-development and self-confidence. Studies with quantitative methodologies report improvement especially for body-related perceptions in both populations. Contradictory results exist concerning self-esteem/-efficacy. The evaluated studies show a heterogeneous nature of populations, intervention contents, timeframes, outcomes, research methods and study quality. Evidence for each of the aspects is still poor due to the small number of studies on each construct, inconsistent findings or methodological shortcomings. Conclusions: This review indicates that dance may be a valuable approach to strengthen aspects of the self. However, as evidence for the different aspects of the self is still poor, further studies with high quality are required (e.g., large samples, active control group). Research considering the complexity and specificity of dance interventions in the design and reporting (e.g., choice of outcomes, presentation of intervention details) seem to be particularly suitable to capture the effects of dance considering its holistic nature.
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github. io/deployable.
TýrFS: Increasing Small Files Access Performance with Dynamic Metadata Replication

Small files are known to pose major performance challenges for file systems. Yet, such workloads are increasingly common in a number of Big Data Analytics workflows or large-scale HPC simulations. These challenges are mainly caused by the common architecture of most state-of-the-art file systems needing one or multiple metadata requests before being able to read from a file. Small input file size causes the overhead of this metadata management to gain relative importance as the size of each file decreases. In this paper we propose a set of techniques leveraging consistent hashing and dynamic metadata replication to significantly reduce this metadata overhead. We implement such techniques inside a new file system named TýrFS, built as a thin layer above the Týr object store. We prove that TýrFS increases small file access performance up to one order of magnitude compared to other state-of-the-art file systems, while only causing a minimal impact on file write throughput.
Time Series Classification with HIVE-COTE

A recent experimental evaluation assessed 19 time series classification (TSC) algorithms and found that one was significantly more accurate than all others: the Flat Collective of Transformation-based Ensembles (Flat-COTE). Flat-COTE is an ensemble that combines 35 classifiers over four data representations. However, while comprehensive, the evaluation did not consider deep learning approaches. Convolutional neural networks (CNN) have seen a surge in popularity and are now state of the art in many fields and raises the question of whether CNNs could be equally transformative for TSC. We implement a benchmark CNN for TSC using a common structure and use results from a TSC-specific CNN from the literature. We compare both to Flat-COTE and find that the collective is significantly more accurate than both CNNs. These results are impressive, but Flat-COTE is not without deficiencies. We significantly improve the collective by proposing a new hierarchical structure with probabilistic voting, defining and including two novel ensemble classifiers built in existing feature spaces, and adding further modules to represent two additional transformation domains. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE), encapsulates classifiers built on five data representations. We demonstrate that HIVE-COTE is significantly more accurate than Flat-COTE (and all other TSC algorithms that we are aware of) over 100 resamples of 85 TSC problems and is the new state of the art for TSC. Further analysis is included through the introduction and evaluation of 3 new case studies and extensive experimentation on 1,000 simulated datasets of 5 different types.
Research on Risk Evaluation of Enterprise Human Capital Investment With Interval-Valued Bipolar 2-Tuple Linguistic Information

In this paper, we investigate the multiple attribute decision-making (MADM) problems for risk evaluation of enterprise human capital investment with interval-valued bipolar 2-tuple linguistic numbers (IVB2TLNs). Then, we propose some arithmetic and geometric aggregation operators with IVB2TLNs and analyze the characteristics of these operators. Furthermore, we present methods that apply these operators to solve the MADM problems with IVB2TLNs. Finally, a real-world example for the risk evaluation of enterprise human capital investment is used to illustrate the effectiveness of the proposed methodology.
Longest Common Substring Made Fully Dynamic

In the longest common substring (LCS) problem, we are given two strings $S$ and $T$, each of length at most $n$, and we are asked to find a longest string occurring as a fragment of both $S$ and $T$. This is a classical and well-studied problem in computer science with a known $\mathcal{O}(n)$-time solution. In the fully dynamic version of the problem, edit operations are allowed in either of the two strings, and we are asked to report an LCS after each such operation. We present the first solution to this problem that requires sublinear time per edit operation. In particular, we show how to return an LCS in $\tilde{\mathcal{O}}(n^{2/3})$ time (or $\tilde{\mathcal{O}}(\sqrt{n})$ time if edits are allowed in only one of the two strings) after each operation using $\tilde{\mathcal{O}}(n)$ space. This line of research was recently initiated by the authors [SPIRE 2017] in a somewhat restricted dynamic variant. An $\tilde{\mathcal{O}}(n)$-sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in $\tilde{\mathcal{O}}(1)$ time was presented. At CPM 2018, three papers studied analogously restricted dynamic variants of problems on strings. We show that our techniques can be used to obtain fully dynamic algorithms for several classical problems on strings, namely, computing the longest repeat, the longest palindrome and the longest Lyndon substring of a string. The only previously known sublinear-time dynamic algorithms for problems on strings were obtained for maintaining a dynamic collection of strings for comparison queries and for pattern matching with the most recent advances made by Gawrychowski et al. [SODA 2018] and by Clifford et al. [STACS 2018].
Recommendation With Social Roles

Social recommender is an active research area. Most previous social recommenders adopt existing social networks to augment recommendations which are based on user preferences. In this contribution, we propose to simultaneously infer the social influence network and the user preferences in a matrix factorization framework. Furthermore, we assume that the influence strength is dependent on the social roles of users. We present an incremental clustering algorithm to detect dynamic social roles. Comprehensive experiments on real data sets demonstrate the efficiency and effectiveness of our model to generate precise recommendations.
Expected Free Energy Formalizes Conflict Underlying Defense in Freudian Psychoanalysis

Freud's core interest in the psyche was the dynamic unconscious: that part of the psyche which is unconscious due to conflict (Freud, 1923/1961). Over the course of his career, Freud variously described conflict as an opposition to the discharge of activation (Freud, 1950), opposition to psychic activity due to the release of unpleasure (Freud, 1990/1991), opposition between the primary principle and the reality principle (Freud, 1911/1963), structural conflict between id, ego, and superego (Freud, 1923/1961), and ambivalence (Freud, 1912/1963). Besides this difficulty of the shifting description of conflict, an underlying question remained the specific shared terrain in which emotions, thoughts, intentions or wishes could come into conflict with one another (the neuronal homolog of conflict), and most especially how they may exist as quantities in opposition within that terrain. Friston's free-energy principle (FEP henceforth) connected to the work of Friston (Friston et al., 2006; Friston, 2010) has provided the potential for a powerful unifying theory in psychology, neuroscience, and related fields that has been shown to have tremendous consilience with psychoanalytic concepts (Hopkins, 2012). Hopkins (2016), drawing on a formulation by Hobson et al. (2014), suggests that conflict may be potentially quantifiable as free energy from a FEP perspective. More recently, work by Friston et al. (2017a) has framed the selection of action as a gradient descent of expected free energy under different policies of action. From this perspective, the article describes how conflict could potentially be formalized as a situation where opposing action policies have similar expected free energy, for example between actions driven by competing basic prototype emotion systems as described by Panksepp (1998). This conflict state may be avoided in the future through updating the relative precision of a particular set of prior beliefs about outcomes: this has the result of tending to favor one of the policies of action over others in future instances, a situation analogous to defense. Through acting as a constraint on the further development of the person, the defensive operation can become entrenched, and resistant to alteration. The implications that this formalization has for psychoanalysis is explored.
IEC 61850 and XMPP Communication Based Energy Management in Microgrids Considering Electric Vehicles

Electric vehicles (EVs) can act as flexible resources in grid due to their bidirectional power transfer capabilities. In microgrids, the bidirectional power transfer capability of EVs through proper scheduling can be utilized to improve reliability, security, and quality of supply. Optimal scheduling of EVs is generally managed and controlled through the energy management system of microgrid. Since microgrid energy management (EM)-based scheduling of EVs is data driven, an effective communication between different actors of EM is required. This paper presents a IEC 61850 communication-based EM in microgrids with integrated EVs. Furthermore, augmentation of existing IEC 61850-90-8 logical nodes of EV and its related equipment with new data objects to include information exchanges for discharging operation of EVs have been proposed. Finally, in this paper, XMPP-based communication approach and its mapping to the service models for EM problem has been demonstrated.
GreyFiber: A System for Providing Flexible Access to Wide-Area Connectivity

Access to fiber-optic connectivity in the Internet is traditionally offered either via lit circuits or dark fiber. Economic (capex vs. opex) and operational considerations (latency, capacity) dictate the choice between these two offerings, but neither may effectively address the specific needs of modern-day enterprises or service providers over a range of use scenarios. In this paper, we describe a new approach for fiber-optic connectivity in the Internet that we call GreyFiber. The core idea of GreyFiber is to offer flexible access to fiber-optic paths between end points (e.g., datacenters or colocation facilities) over a range of timescales. We identify and discuss operational issues and systems challenges that need to be addressed to make GreyFiber a viable and realistic option for offering flexible access to infrastructure (similar to cloud computing). We investigate the efficacy of GreyFiber with a prototype implementation deployed in the GENI and CloudLab testbeds. Our scaling experiments show that 50 circuits can be provisioned within a minute. We also show that backup paths can be provisioned 28 times faster than an OSPF-based solution during failure/maintenance events. Our experiments also examine GreyFiber overhead demands and show that the time spent in circuit creation is dependent on the network infrastructure, indicating avenues for future improvements.