Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Capability and Business Strategy on the Performance

This research investigates the effect of dynamic capability and business strategy on the performance of accounting firms in an intensive competitive environment. Research findings suggest that dynamic capability resides in organizational process, position, and path, respectively. Training expenses, education of CPAs, and non-auditing services ratio are key issues fostering and enhancing dynamic capability. The theoretical result is further tested on empirical data of Accounting Firms in Taiwan. The result shows that dynamic capability and innovative strategy have significant and positive effects on performance. Besides, intensive competition influences organizational performance negatively.
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain

Contrastive Predictive Coding (CPC), based on predicting future segments of speech from past segments is emerging as a powerful algorithm for representation learning of speech signal. However, it still under-performs compared to other methods on unsupervised evaluation benchmarks. Here, we intro-duce WavAugment, a time-domain data augmentation library which we adapt and optimize for the specificities of CPC (raw waveform input, contrastive loss, past versus future structure). We find that applying augmentation only to the segments from which the CPC prediction is performed yields better results than applying it also to future segments from which the samples (both positive and negative) of the contrastive loss are drawn. After selecting the best combination of pitch modification, additive noise and reverberation on unsupervised metrics on LibriSpeech (with a gain of 18-22% relative on the ABX score), we apply this combination without any change to three new datasets in the Zero Resource Speech Benchmark 2017 and beat the state-of-the-art using out-of-domain training data. Finally, we show that the data-augmented pretrained features improve a downstream phone recognition task in the Libri-light semi-supervised setting (10 min, 1 h or 10 h of labelled data) reducing the PER by 15% relative.
Consistent Structured Prediction with Max-Min Margin Markov Networks

Max-margin methods for binary classification such as the support vector machine (SVM) have been extended to the structured prediction setting under the name of max-margin Markov networks ($M^3N$), or more generally structural SVMs. Unfortunately, these methods are statistically inconsistent when the relationship between inputs and labels is far from deterministic. We overcome such limitations by defining the learning problem in terms of a "max-min" margin formulation, naming the resulting method max-min margin Markov networks ($M^4N$). We prove consistency and finite sample generalization bounds for $M^4N$ and provide an explicit algorithm to compute the estimator. The algorithm achieves a generalization error of $O(1/\sqrt{n})$ for a total cost of $O(n)$ projection-oracle calls (which have at most the same cost as the max-oracle from $M^3N$). Experiments on multi-class classification, ordinal regression, sequence prediction and ranking demonstrate the effectiveness of the proposed method.
Laplacian Change Point Detection for Dynamic Graphs

Dynamic and temporal graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address two main challenges associated with this problem: I) how to compare graph snapshots across time, II) how to capture temporal dependencies. To solve the above challenges, we propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the Laplacian matrix of the graph structure at each snapshot to obtain low dimensional embeddings. LAD explicitly models short term and long term dependencies by applying two sliding windows. In synthetic experiments, LAD outperforms the state-of-the-art method. We also evaluate our method on three real dynamic networks: UCI message network, US senate co-sponsorship network and Canadian bill voting network. In all three datasets, we demonstrate that our method can more effectively identify anomalous time points according to significant real world events.
Study of galaxies on large-scale filaments in simulations

We use data from the Evolution and Assembly of GaLaxies in their Environment (EAGLE) cosmological simulation to study properties of galaxies in the cosmic web. Galaxies become more redder and form stars at a lower rate relative to their counterparts further away from the cylindrical axis of the large-scale filaments. These trends are particularly strong for galaxies with $M_*/M_{\odot}\lesssim10^{10}$. We also find that at distances $<0.5$ Mpc from the spine of the filaments, the median gas and stellar mass fraction in filament galaxies rises sharply with decreasing distance from the spine of the filament. These results, together with matching trends in the SFR/$M_*$ and the $g-r$ colour of filament galaxies suggest that (i) the intrafilamentary gas condenses into the filament galaxies thus fuelling star formation in them, and (ii) increased number density of galaxies closer to the central axis of the filament enhances the rate of gravitational interactions among filament galaxies closer to the spine.
Structure Adaptive Algorithms for Stochastic Bandits

We study reward maximisation in a wide class of structured stochastic multi-armed bandit problems, where the mean rewards of arms satisfy some given structural constraints, e.g. linear, unimodal, sparse, etc. Our aim is to develop methods that are flexible (in that they easily adapt to different structures), powerful (in that they perform well empirically and/or provably match instance-dependent lower bounds) and efficient in that the per-round computational burden is small. We develop asymptotically optimal algorithms from instance-dependent lower-bounds using iterative saddle-point solvers. Our approach generalises recent iterative methods for pure exploration to reward maximisation, where a major challenge arises from the estimation of the sub-optimality gaps and their reciprocals. Still we manage to achieve all the above desiderata. Notably, our technique avoids the computational cost of the full-blown saddle point oracle employed by previous work, while at the same time enabling finite-time regret bounds. Our experiments reveal that our method successfully leverages the structural assumptions, while its regret is at worst comparable to that of vanilla UCB.
change in lipids quality and fatty acids profile of two small pelagic fish: sardinella aurita and sardina pilchardus during canning process in olive oil and tomato sauce respectively

The effects of cooking and sterilisation steps on muscle lipid deterioration of Sardinella aurita and Sardina pilchardus canned in olive oil and tomato sauce respectively were investigated. Lipid contents of Sardinella aurita flesh were significantly affected (p < 0.05) by the canning process. However, lipid levels of Sardina pilchardus remained constant during processing. The peroxide value and thiobarbituric index increased slightly after the cooking step but changed significantly (p < 0.05) following canning. Both canned sardine and sardinella absorbed coating-oil during sterilisation inducing a higher oleic (C18:1 n-9) and linoleic (C18:2 n-6) acids content. Independently of coating-oil category, the eicosapentaenoic (C20:5 n-3) and docosahexaenoic (C22:6 n- 3) acids concentrations ranged from 3.00 to 6.24% and from 5.12% to 40.26% respectively. Although, lipids of Sardinella aurita and Sardina pilchardus were slightly affected by the canning process, and they remain a good source of ω3 and ω6 fatty acids.
Is Rust Used Safely by Software Developers?

Rust, an emerging programming language with explosive growth, provides a robust type system that enables programmers to write memory-safe and data-race free code. To allow access to a machine's hardware and to support low-level performance optimizations, a second language, Unsafe Rust, is embedded in Rust. It contains support for operations that are difficult to statically check, such as C-style pointers for access to arbitrary memory locations and mutable global variables. When a program uses these features, the compiler is unable to statically guarantee the safety properties Rust promotes. In this work, we perform a large-scale empirical study to explore how software developers are using Unsafe Rust in real-world Rust libraries and applications. Our results indicate that software engineers use the keyword unsafe in less than 30% of Rust libraries, but more than half cannot be entirely statically checked by the Rust compiler because of Unsafe Rust hidden somewhere in a library's call chain. We conclude that although the use of the keyword unsafe is limited, the propagation of unsafeness offers a challenge to the claim of Rust as a memory-safe language. Furthermore, we recommend changes to the Rust compiler and to the central Rust repository's interface to help Rust software developers be aware of when their Rust code is unsafe.
Convergence of weighted ergodic averages

Let $(X, \mathcal{A},\mu)$ be a probability space and let $T$ be a contraction on $L^2(\mu)$. We provide suitable conditions over sequences $(w_k)$, $(u_k)$ and $(A_k)$ in such a way that the weighted ergodic limit $\lim\limits_{N\rightarrow\infty}\frac{1}{A_N}\sum_{k=0}^{N-1} w_k T^{u_k}(f)=0$ $\mu$-a.e. for any function $f$ in $L^2(\mu)$. As a consequence of our main theorems, we also deal with the so-called one-sided weighted ergodic Hilbert transforms.
Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons

Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal “binding problem. ” We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.
Nutrient composition and metamorphosis success of Atlantic halibut (Hippoglossus hippoglossus, L.) larvae fed natural zooplankton or Artemia

Atlantic halibut larvae were fed docosohexanoic acid- (DHA) selco enriched Artemia (RH-cysts) or wild zooplankton in duplicate tanks from first-feeding and 60 days onward. The zooplankton were collected from a fertilized sea water pond and consisted mainly of different stages of Eurytemora affinis and Centropages hamatus. There were no differences in survival, or in growth during the first 45 days of feeding, between larvae fed the two prey items, but the larvae fed Artemia showed much higher incidence of malpigmentation and impaired eye migration than larvae fed zooplankton. The prey organisms contained similar amounts of dry matter and protein, but Artemia was higher in lipid and glycogen than the zooplankton. Larvae fed Artemia were higher in both glycogen and lipid than the zooplankton-fed larvae towards the end of the feeding period. There were large differences between the prey organisms in the concentrations of essential fatty acids (% of total fatty acids) which was reflected in the fatty acid composition of the larval body. It is concluded that the macronutrient composition of Artemia in the present study was probably within the optimal range for promotion of growth and survival in young Atlantic halibut. The concentration of n-3 HUFA, and especially DHA, is however, very much lower in enriched Artemia than in copepods, and may be one of the factors triggering developmental errors in Atlantic halibut.
Stable genetic transformation in cotton (Gossypium hirsutum L.) using marker genes

Article history: Received 24 October 2013 Revised form 29 October 2013 Accepted 3 November 2013 Available online 25 January 2014 Transformation of novel candidate gene for cotton fiber improvement is using a unique Agrobacterium Mediated Transformation approach. The detection of NPTII (kanamycin resistance) as a transgenic marker gene and 35SCaMV as promoter in cotton has been carried out. Transgenic cotton plants were also confirmed by using VirG gene of Agrobacterium. Genomic DNA from transgenic cotton samples were isolated using Fermentas DNA extraction kit. The purity of DNA samples was estimated using UV-Vis spectrophotometry at 260 nm and 280 nm. They gave an absorbance ratio (A260/A280) of 1.74-1.79 which indicated its purities. In order to identify the transgenic cotton, a 151bp fragment of the NPTII gene (kanamycin resistant gene), 210bp fragment of 35SCaMV promoter and 410bp of VirG gene of Agrobacterium were amplified using polymerase chain reaction (PCR). The results showed that both NPTII gene and 35SCaMV promoter are present in putative transgenic cotton plants but no amplification was found in untransformed plant. 2013 KnowledgesPublisher Ltd. All rights reserved.
Effects of Adding Ethanol Extract of Propolis and Cumin Essential Oil to Diet on the Performance, Blood Parameters, Immune Response and Carcass Traits of Broiler Chicks

This study was conducted to determine the effects of adding ethanol extract of propolis (EEP) and cumin essential oil (CEO) to diets of broiler chicks on the performance, blood parameters, immune response and carcass traits. A total of 240 day-old Ross-308 broiler chicks were randomly allocated to four treatments with six replicates and 10 birds in each replicate in a completely randomized design. Each of four iso-caloric and iso-nitrogenous corn-soybean-based experimental diets including control, diet with 0.2 g/kg EEP, diet with 0.8 g/kg CEO and diet with 0.2 EEP g/kg as well as 0.8 g/kg CEO were offered to the birds during a 6-week trial period. Body weight (BW), body weight gain (BWG), feed intake (FI) and feed conversion ratio (FCR) were determined throughout starter and grower periods (1-21 and 22-42 days of age, respectively). Plasma levels of albumin, cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL), triacylglyceride (TG), uric acid, glucose and leukocyte profile were determined on day 42 of age. At the end of the experimental period, carcass traits were also determined. Diet inclusion of EEP and / or CEO significantly improved BW, BWG and FCR. The birds fed diet containing EEP at 0.2 g/kg had significantly lower plasma concentration of glucose compared to the control group. The use of EEP or CEO significantly decreased plasma concentration of cholesterol compared to the control group. The use of EEP or CEO also increased plasma concentration of albumin compared to the control group. Diet inclusion of EEP and/or CEO did not significantly influence the antibody titters against Newcastles vaccine at 31 and 41 days of age, H/L ratio and also carcass characteristics. Results of this study showed that EEP and CEO can be used in diets of broiler chickens at 0.2 g/kgand 0.8 g/kg, respectively with positive effects on performance and blood parameters.
ESTIMATION OF CONTRIBUTIONS TO POPULATION GROWTH: A REVERSE-TIME CAPTURE???RECAPTURE APPROACH

We consider methods for estimating the relative contributions of different demographic components, and their associated vital rates, to population growth. We identify components of the population at time i (including a component for animals not in the population at i). For each such component we ask the following question: "What is the probability that an individual randomly selected from the population at time i + 1 was a member of this component at i? " The estimation methods for these probabilities (^Y) are based on capture-recapture studies of marked animal populations and use reverse-time modeling. We consider several different sampling situations and present example analyses for meadow voles, Microtus pennsylvanicus. The relationship between these yi parameters and elasticities (and other parameters based on projection matrix asymptotics) is noted and discussed. We conclude by suggesting that model-based asymptotics be viewed as demo- graphic theory and that direct estimation approaches be used to test this theory with data from sampled populations with marked animals.
Efficiency of partial 16S rRNA gene sequencing as molecular marker for phylogenetic study of cyanobacteria, with emphasis on some complex taxa

At present, the analysis of 16S rRNA gene sequences is the most commonly used molecular marker for phylogenetic studies of cyanobacteria. However, in many studies partial sequences is used. To evaluate the performance of this molecular marker, phylogenetic relationship of several taxa from this phylum, especially some intermixed taxa, was studied. We analyzed a data set consisting of three categories of cyanobacterial strains, traditionally classified in three orders, by morphological and phylogenetic analyses. The phylogenetic analyses were performed with an emphasis on partial 16S rRNA gene sequences (600 bp) and the phylogenetic relationships were assessed using Maximum Parsimony, Maximum Likelihood and Bayesian Inference. In morphometric study, numerical taxonomy was performed on several morphospecies, and cluster analysis was performed using SPSS software. Based on the findings of this study, unlike the morphological analysis which was useful in several taxonomic ranks, this molecular marker is recommended for use only in high taxonomic levels such as order and family, because, contrary to our expectations, using partial 16S rRNA gene sequencing in the lower taxonomic levels, even in the genus level, was not necessarily successful. Inefficiency of this molecular marker in taxonomy of some genera, especially intermixed taxa, was another finding of the present study, which represents the genetic similarity of these taxa. Acta Biol Szeged 61(1):59-68 (2017) KEy WoRdS
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accounts for correlations between individual objects. This allows the model to generate realistic scenes and videos from a physically-interpretable parameterization. Furthermore, we show that modeling the object correlation is necessary to learn to disentangle object positions and identity. We find that RELATE is also amenable to physically realistic scene editing and that it significantly outperforms prior art in object-centric scene generation in both synthetic (CLEVR, ShapeStacks) and real-world data (street traffic scenes). In addition, in contrast to state-of-the-art methods in object-centric generative modeling, RELATE also extends naturally to dynamic scenes and generates videos of high visual fidelity
Effect of replacing cod liver oil with soybean oil as dietary lipid on carcass composition, haematology and sensory properties of the Nile tilapia Oreochromis niloticus

A study was conducted to evaluate the effect of replacement of cod liver oil (CLO) by soybean oil (SO) on the carcass composition, haematology and sensory qualities of the flesh of the Nile tilapia Oreochromis niloticus. Four diets (30% cp) containing 6% of added CLO and/or SO were formulated. Diet 1 contained 6% of added CLO, Diet 2 had 4% CLO and 2% SO, Diet 3 had 2% CLO and 4% SO and Diet 4 had 6% SO. The diets were fed to triplicate groups of ten tilapia fingerlings (mean weight 10.06 ± 1.02 g) for 56 days. Results at the end of the trial showed that carcass quality of the Nile tilapia fed soybean oil diets was not affected. The viscerosomatic index (VSI) of fish fed experimental diets decreased with increasing percentage of soybean oil in the feed, but the hepatosomatic index (HSI) showed a trend that is the reverse of VSI. The gonadosomatic index (GSI) of fish showed variation between treatments, with the control diet having the highest value. The values of the blood parameters (i.e. Hct, Hb, Esr, Wbc, and Rbc) of fish fed different diets showed variability among treatments, but fish fed the control diet had lower values than those fed other treatments. There was no significant difference in most of the organoleptic properties of the flesh (general appearance, colour, taste, juiciness), except in the texture and aroma of fish fed Diets 1 and 4 on the one hand, and those fed Diets 2 and 3 on the other (P < 0.05). The results indicate that soybean oil can replace up to 100% of fish oil in diets for the Nile Tilapia O. niloticus, without adverse effect on organoleptic properties.
EXPERIMENTAL STUDY ON THE MRR, MACHINED DEPTH AND HOLE DIAMETER FOR SODA-LIME GLASS BY ELECTROCHEMICAL DISCHARGE MACHINING PROCESS WITH COPPER TOOL

The electrochemical discharge machining (ECDM) is an integrated machining process that can cut effectively with various non-conducting materials. In present research work, the copper tool was used as a cathode to machined Soda-lime glass workpiece material by fabricated ECDM setup. The Soda-lime glass material is having high brittleness and hardness properties. Taguchi L27 orthogonal array method was used for drilling experimentation on Soda-lime glass by ECDM. The Minitab software was utilized for analyzing the experimental output results. The average material removal rate (MRR), average hole diameter and average machined depth were considered as output responses and input parameters were electrolyte concentration, voltage, and tool rotation speed. The experimental and analytical results were showing that for average material removal rate, electrolyte concentration was the most dominating factor followed by voltage and tool electrode rotation. However, for average machined depth, the voltage was the most powerful parameter, then after electrolyte concentration and tool rotation. Though, for average hole diameter, tool rotation was the most powerful parameter followed by electrolyte concentration and voltage. The response surface methodology plot shows that the optimum values of input parameters were voltage of 75 V, electrolyte concentration of 11.21 %, and rotation of 50 r/min, to get the higher average material removal rate, maximum average machined depth and targeted nominal average hole diameter.