Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-based adaptive skin segmentation for hand and head detection

This paper addresses a problem of skin segmentation for sign language recognition purposes. The main contributions of the presented research is an adaptive skin model enhanced with a blob analysis algorithm which significantly improves skin detection effectiveness. Adaptive skin detector utilizes a statistical skin color model updated dynamically based on a face region defined by eye positions. Face geometry is used for eye detection in luminance channel prior to the model adaptation. Color-based skin detectors classify every pixel separately which results in high false positives for background pixels which color is similar to human skin. The proposed blob analysis technique verifies detected skin regions by taking into account pixel topology. The experiments for ECU database showed that with the proposed approach false positive rate was reduced from 15.6% to 6% compared with a statistical model in RGB. Increased precision of skin detection makes it applicable to gesture recognition purposes.
Pastwatch: A Distributed Version Control System

Pastwatch is a version control system that acts like a traditional client-server system when users are connected to the network; users can see each other's changes immediately after the changes are committed. When a user is not connected, Pastwatch also allows users to read revisions from the repository, commit new revisions and share modifications directly between users, all without access to the central repository. In contrast, most existing version control systems require connectivity to a centralized server in order to read or update the repository. Each Pastwatch user's host keeps its own writable replica of the repository, including historical revisions. Users can synchronize their local replicas with each other or with one or more servers. Synchronization must handle inconsistency between replicas because users may commit concurrent and conflicting changes to their local replicas. Pastwatch represents its repository as a "revtree" data structure which tracks the relationships among these conflicting changes, including any reconciliation. The revtree also ensures that the replicas eventually converge to identical images after sufficient synchronization. We have implemented Pastwatch and evaluate it in a setting distributed over North America. We have been using it actively for more than a year. We show that the system is scalable beyond 190 users per project and that commit and update operations only take 2-4 seconds. Currently, five users and six different projects regularly use the system; they find that the system is easy to use and that the system's replication has masked several network and storage failures.
Association of heat shock protein 27 (HSP27) gene variants with physiological parameters and milk parameters in Hariana cattle

The present study was conducted on 50 Hariana cattle to analyse association of HSP 27 gene with vital parameters and of milk production parameters. Genomic DNA was extracted from whole blood of a total of 50 lactating Hariana cattle. PCR amplification yielded products of 631bp for HSP 27 gene. Amplicons were subjected to direct sequencing and multiple sequence alignment using ClustalW revealed one nucleotide sequence variation in HSP 27 gene. In HSP 27 gene G225A was polymorphic with three genotypes AA, AB and BB in our resource population. Allele and genotype frequencies of HSP 27 gene were calculated using POPGENE software package. Genotype AA, BB and AB had frequencies 0.64, 0.28 and 0.08, respectively, whereas, allele frequency for A and B allele were 0.78 and 0.22, respectively. Effect of SNP variants on physiological parameters and milk parameters were analysed using least squares model with the help of SAS statistical software. Effect of genotypes on physiological function were found significant ( p <0.05) for RR and HTC values. Least squares mean value for genotype AA, AB and BB were 67.85, 65.92 and 67.33 for PR, 101.40 o F, 101.46 o F and 101.11 o F for RT, 24.00, 22.10 and 27.19 for RR, 1.69, 1.75 and 2.11 for HTC respectively. Hariana cows with genotype AA had better thermotolerance than the other cows having genotypes AB and BB due to low HTC values. However, effect of genotypes on milk parameters was found to be non-significant.
HUA ENHANCER3 reveals a role for a cyclin-dependent protein kinase in the specification of floral organ identity in Arabidopsis

In plants, organs are generated post-embryonically from highly organized structures known as meristems. Cell division in the meristem is closely integrated with cell fate specification and organ formation. The presence of multiple cyclin-dependent kinases (CDKs) and their partner cyclins in plants and other multicellular organisms probably reflects the complexity of cell cycle regulation within developmental contexts. The Arabidopsis genome encodes at least eight CDKs and 30 cyclins. However, no mutants in any CDKs have been reported, and the function of the great majority of these genes in plant development is unknown. We show that HUA ENHANCER3 (HEN3), which encodes CDKE, a homolog of mammalian CDK8, is required for the specification of stamen and carpel identities and for the proper termination of stem cells in the floral meristem. Therefore, CDK8 plays a role in cell differentiation in a multicellular organism.
A user-centric model of voting intention from Social Media

Social Media contain a multitude of user opinions which can be used to predict realworld phenomena in many domains including politics, finance and health. Most existing methods treat these problems as linear regression, learning to relate word frequencies and other simple features to a known response variable (e.g., voting intention polls or financial indicators). These techniques require very careful filtering of the input texts, as most Social Media posts are irrelevant to the task. In this paper, we present a novel approach which performs high quality filtering automatically, through modelling not just words but also users, framed as a bilinear model with a sparse regulariser. We also consider the problem of modelling groups of related output variables, using a structured multi-task regularisation method. Our experiments on voting intention prediction demonstrate strong performance over large-scale input from Twitter on two distinct case studies, outperforming competitive baselines.
Repression Domains of Class II ERF Transcriptional Repressors Share an Essential Motif for Active Repression

We reported previously that three ERF transcription factors, tobacco ERF3 (NtERF3) and Arabidopsis AtERF3 and AtERF4, which are categorized as class II ERFs, are active repressors of transcription. To clarify the roles of these repressors in transcriptional regulation in plants, we attempted to identify the functional domains of the ERF repressor that mediates the repression of transcription. Analysis of the results of a series of deletions revealed that the C-terminal 35 amino acids of NtERF3 are sufficient to confer the capacity for repression of transcription on a heterologous DNA binding domain. This repression domain suppressed the intermolecular activities of other transcriptional activators. In addition, fusion of this repression domain to the VP16 activation domain completely inhibited the transactivation function of VP16. Comparison of amino acid sequences of class II ERF repressors revealed the conservation of the sequence motif L/FDLNL/F(x)P. This motif was essential for repression because mutations within the motif eliminated the capacity for repression. We designated this motif the ERF-associated amphiphilic repression (EAR) motif, and we identified this motif in a number of zinc-finger proteins from wheat, Arabidopsis, and petunia plants. These zinc finger proteins functioned as repressors, and their repression domains were identified as regions that contained an EAR motif.
A data stream outlier detection algorithm based on grid

The main aim of data stream outlier detection is to find the data stream outliers in rational time accurately. The existing outlier detection algorithms can find outliers in static data sets efficiently, but they are inapplicable for the dynamic data stream, and cannot find the abnormal data effectively. Due to the requirements of real-time detection, dynamic adjustment and the inapplicability of existing algorithms on data stream outlier detection, we propose a new data stream outlier detection algorithm, ODGrid, which can find the abnormal data in data stream in real time and adjust the detection results dynamically. According to the experiments on real datasets and synthetic datasets, ODGrid is superior to the existing data stream outlier detection algorithms, and it has good scalability to the dimensionality of data space.
ZipWeave: Towards efficient and reliable measurement based mobile coverage maps

The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these sub-regions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.
Review of general surgery 1985.

Nineteen eighty-five has been an interesting year in general surgery and I have picked out some of the highlights that I have found of special value. They reflect the present trend to critical review of patient management, scientifically conducted prospective trials and the application of modem technology to patient care. This year I review abdominal emergency surgery (including appendicitis, pancreatitis, peritonitis and abdominal trauma), gall stone surgery, diverticula, inflammatory disease and cancer of the large bowel and the urogenital tract (urinary stone, urothelial cancer, testicular disease and prostatectomy). I make no excuse for dealing extensively with breast cancer, where important progress is being made in assessing the value of screening, adjuvant cytotoxic therapy and conservative surgical treatment. Finally, I review some interesting problems in the diagnosis of pulmonary emboli, wound healing and wound closure. The great majority of the papers quoted have been published in 1985.
Up and Down States in Striatal Medium Spiny Neurons Simultaneously Recorded with Spontaneous Activity in Fast-Spiking Interneurons Studied in Cortex–Striatum–Substantia Nigra Organotypic Cultures

In vivo intracellular spontaneous activity in striatal medium spiny (MS) projection neurons is characterized by “up” and “down” states. How this type of activity relates to the neuronal activity of striatal fast-spiking (FS) interneurons was examined in the presence of nigral and cortical inputs using cortex–striatum–substantia nigra organotypic cultures grown for 45 ± 4 d. The nigrostriatal projection was confirmed by tyrosine hydroxylase immunoreactivity. Corticostriatal (CS) projection neurons, striatal MS neurons, and FS neurons were intracellularly recorded and morphologically and electrophysiologically characterized. Intracellular spontaneous activity in the cultures consisted of intermittent depolarized periods of 0.5–1 sec duration. Spontaneous depolarizations in MS neurons were restricted to a narrow membrane potential range (up state) during which they occasionally fired single spikes. These up states were completely blocked by the glutamate antagonist CNQX. In FS interneurons, depolarized periods were characterized by large membrane potential fluctuations that occupied a wide range between rest and spike threshold. Also, FS interneurons spontaneously fired at much higher rates than did MS neurons. Simultaneous intracellular recordings established that during spontaneous depolarizations MS neurons and FS interneurons displayed correlated subthreshold neuronal activity in the low frequency range. These results indicate that (1) the CS projection neurons, striatal MS neurons, and FS interneurons grown in cortex–striatum–substantia nigra organotypic cultures show morphological and electrophysiological characteristics similar to those seen in vivo; (2) striatal MS neurons but not FS interneurons show an up state; (3) striatal MS neurons and FS interneurons receive common, presumably cortical inputs in the low frequency range. Our results support the view that the cortex provides a feedforward inhibition of MS neuron activity during the up state via FS interneurons.
Data-efﬁcient Deep Learning for RGB-D Object Perception in Cluttered Bin Picking

—Deep learning methods often require large annotated data sets to estimate their high numbers of parameters, which is not practical for many robotic domains. One way to migitate this issue is to transfer features learned on large datasets to related tasks. In this work, we describe the perception system developed for the entry of team NimbRo Picking into the Amazon Picking Challenge 2016. Object detection and semantic Segmentation methods are adapted to the domain, including incorporation of depth measurements. To avoid the need for large training datasets, we make use of pretrained models whenever possible, e.g. CNNs pretrained on ImageNet, and the whole DenseCap captioning pipeline pretrained on the Visual Genome Dataset. Our system performed well at the APC 2016 and reached second and third places for the stow and pick tasks, respectively.
Image/video deblurring using a hybrid camera

We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar who introduced the hybrid camera idea for correcting global motion blur for a single still image. We broaden the scope of the problem to address spatially varying blur as well as video imagery. We also reformulate the correction process to use more information available in the hybrid camera system, as well as iteratively refine spatially varying motion extracted from the low-resolution high-speed camera. We demonstrate that our approach achieves superior results over existing work and can be extended to deblurring of moving objects.
Mark my words!: linguistic style accommodation in social media

The psycholinguistic theory of communication accommodation accounts for the general observation that participants in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its almost forty years of existence, this theory has been empirically supported exclusively through small-scale or controlled laboratory studies. Here we address this phenomenon in the context of Twitter conversations. Undoubtedly, this setting is unlike any other in which accommodation was observed and, thus, challenging to the theory. Its novelty comes not only from its size, but also from the non real-time nature of conversations, from the 140 character length restriction, from the wide variety of social relation types, and from a design that was initially not geared towards conversation at all. Given such constraints, it is not clear a priori whether accommodation is robust enough to occur given the constraints of this new environment. To investigate this, we develop a probabilistic framework that can model accommodation and measure its effects. We apply it to a large Twitter conversational dataset specifically developed for this task. This is the first time the hypothesis of linguistic style accommodation has been examined (and verified) in a large scale, real world setting. Furthermore, when investigating concepts such as stylistic influence and symmetry of accommodation, we discover a complexity of the phenomenon which was never observed before. We also explore the potential relation between stylistic influence and network features commonly associated with social status.
The Relative Ef ciency of Endogenous Proxies : Still Living with the Roll Critique

An asset pricing restriction that permits inference about the familiar CAPM despite the market return being unobservable is generalized to allow an observable proxy of the market return to be endogenously determined along with the individual asset returns the proxy is supposed to price. Making this allowance reduces the ef ciency of the proxy relative to the market return by upwards of 20%. Such a reduction is capable of reversing an inference about the validity of the CAPM theory under the aforementioned pricing restriction. Rendering this pricing restriction feasible empirically is a new method for estimating triangular systems given GARCH errors.
GREEN HRM AND GREEN COMPETITIVE ADVANTAGE IN HOTEL AND TOURISM INDUSTRY: A MEDIATED MODERATION MODEL USING ECO-INNOVATION AND GREEN PSYCHOLOGICAL CLIMATE

This study investigates the impact of green HRM (GHRM) on green competitive advantage (GCA), examining the mediating role of eco-innovation and the moderating role of green psychological climate (GPC). The study surveyed 472 fulltime employees in five-star hotels and travel agencies, developing and testing a five-hypothesis research model using PLS-SEM. Results revealed that GHRM positively impacts GCA and eco-innovation. Eco-innovation positively impacts GCA. In addition, GPC has a positive moderating influence on the relationship between GHRM and eco-innovation. Furthermore, Eco-innovation significantly mediates the relationship between GHRM and GCA.
From Biological Activity to Stereoselectivity: A Portrait of Molecular and Mechanistic Profiles of the Therapeutic Potential of G-1 and LNS8801 as GPER-1 Activator in the Treatment of Waldenström’s Macroglobulinemia

Therapeutic activation of G protein-coupled estrogen receptor 1 (GPER-1) shows promise for treating Waldenström's Macroglobulinemia (WM). Recently, the selective small-molecule agonist G-1 or its enantiomer LNS8801 were reported as potent GPER-1 pharmacological activators, however, the molecular events surrounding their chirality towards GPER-1 is still unexplored. This study aimed to explore the molecular events surrounding the chirality of the selective small-molecule agonists G-1 and LNS8801 towards GPER-1. Molecular docking and dynamics simulations revealed strong binding affinities and key amino acid residues involved in the activation process. The results revealed favourable binding affinities of -29.87kcal/mol, and -28.09kcal/mol for G-1 and LNS8801 towards GPER-1, respectively. Per-residue energy decomposition and time-dependent analysis proved that Arg253 and Arg254 are key amino acid residues in binding these activators towards GPER-1. The activators affected stability, flexibility, and structure of GPER-1. G-1 exhibited greater rigidity when bound to GPER-1 compared to LNS880. This study's findings illuminate chirality and the potential for optimizing the enantiomer ratio to enhance inhibitory effects. These results could also reveal the molecular specifics and binding mechanism of the G-1 and its enantiomer LNS8801 with GPER-1, laying the basis for the rational design of enhanced chiral molecules against WM.
Normalization by Evaluation for Martin-Lof Type Theory with Typed Equality Judgements

The decidability of equality is proved for Martin-Lof type theory with a universe a la Russell and typed beta-eta- equality judgements. A corollary of this result is that the constructor for dependent function types is injective, a property which is crucial for establishing the correctness of the type-checking algorithm. The decision procedure uses normalization by evaluation, an algorithm which first interprets terms in a domain with untyped semantic elements and then extracts normal forms. The correctness of this algorithm is established using a PER-model and a logical relation between syntax and semantics.
MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.
The Scene Category Classification for TV Series

Scene Category Classification is one of the key areas of research in Computer Vision. In this project, we use convolutional neural networks to classify a sequence of images, obtained from video clips of a famous TV Series, according to their scene category. We have programmed our model using a computationally efficient averaging technique on frames which gives us a higher accuracy than techniques which do not employ the use of neural networks.