LLukas22/scidocs huggingface dataset:
EFFECT OF FEEDING YEAST CULTURE FROM DIFFERENT SOURCES ON THE PERFORMANCE OF LACTATING HOLSTEIN COWS IN SAUDI ARABIA

One hundred-fifty lactating, multiparous cow at post-peak of lactation were used to examine the effect of dietary yeast supplementation on milk production, milk composition and ruminal fermentation. The cows were randomly allocated to three groups of fifty cows each: a control group fed on a basal diet without yeast supplementation and two groups fed on basal diets supplemented with one of two commercial sources of yeast cultures, given at the rates of 15 g/head/d (YC1) and 50 g/head/d (YC2), respectively, as per manufacturers' recommendation. Daily milk production was recorded for all cows, while milk samples were taken randomly from ten cows per group for two consecutive days at two-week intervals for chemical analysis of the milk. Rumen fluids were also analyzed for ammonia nitrogen and volatile fatty acids. The results indicated that cows consuming diets supplemented with yeast culture tended to decrease their dry matter intake and to increase their milk yield. Cows fed YC2 supplemented diet produced more milk and 4% fat corrected milk than those fed either YC1-supplemented diet or the control. The highest milk fat percentage was obtained in cows fed YC2 supplemented diet while the highest percentages of protein, lactose, total solids and solids not fat were recorded in cows fed YC1. Rumen ammonia nitrogen concentration decreased significantly after yeast culture supplementation. Molar proportion of volatile fatty acids did not change significantly with yeast supplementation. (Asian-Aust. J. Anim. Sci. 2002. Vol 15, No. 3 : 352-356)
CDM protection design for CMOS applications using RC-triggered rail clamps

A new ESD protection design method is introduced that optimizes the CDM circuit response of an RC-triggered rail clamp network for I/O protection. The design is verified by VF-TLP and CDM test data of I/O test rings with two different output driver options.
Early laparoscopy as a routine procedure in the management of acute abdominal pain: a review of 1,320 patients

Acute abdominal pain is a common cause for presentation to the emergency room and hospital admission. Many of these patients will undergo exploration for suspected appendicitis, but in 20–35% of cases a normal appendix is found. Because of the limited access provided by the gridiron incision, a definitive diagnosis may not be found. Other patients may be treated conservatively and discharged, only to return with recurrent pain or more definitive symptoms of pathology. In patients with acute abdominal pain, early laparoscopy is an accurate means of both making a definitive diagnosis and avoiding a delay in the diagnosis. We performed a retrospective analysis of 1,320 consecutive patients with acute abdominal pain over a 62-month period. All patients underwent diagnostic laparoscopy within 48 h of admission. We evaluated the initial clinical diagnosis, the laparoscopic diagnosis, and the subsequent outcome in this group of patients. Individuals with abdominal trauma were excluded from the study, and all patients were >12 years of age. A definitive diagnosis was made in 90% of patients after diagnostic laparoscopy. Laparoscopy changed the clinical diagnosis in 30% of cases. (83%) of patients underwent a laparoscopic operation for management of their condition at the time of diagnosis. In 92 patients (7%), conversion to laparotomy was required to manage their condition. Peritonitis was present in 180 patients; of 110 of them had appendicitis. Twelve patients developed complications related to the diagnostic laparoscopy or the laparoscopic operation, and there was one postoperative death due to a perforated gastric malignancy. Mean operating time was 30 min (range, 17–90) Early diagnostic laparoscopy and treatment results in the accurate, prompt, and efficient management of acute abdominal pain. This technique reduces the rate of unnecessary laparotomy and right iliac fossa gridiron incisions and increases the diagnostic accuracy in these patients. This treatment method is feasible where facilities are available to accommodate the workload and there are practitioners with the requisite expertise.
Detecting authorship deception: a supervised machine learning approach using author writeprints

We describe a new supervised machine learning approach for detecting authorship deception, a specific type of authorship attribution task particularly relevant for cybercrime forensic investigations, and demonstrate its validity on two case studies drawn from realistic online data sets. The core of our approach involves identifying uncharacteristic behavior for an author, based on a writeprint extracted from unstructured text samples of the author’s writing. The writeprints used here involve stylometric features and content features derived from topic models, an unsupervised approach for identifying relevant keywords that relate to the content areas of a document. One innovation of our approach is to transform the writeprint feature values into a representation that individually balances characteristic and uncharacteristic traits of an author, and we subsequently apply a Sparse Multinomial Logistic Regression classifier to this novel representation. Our method yields high accuracy for authorship deception detection on the two case studies, confirming its utility. .................................................................................................................................................................................
A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.
Convolutional neural network architectures for predicting DNA–protein binding

MOTIVATION
Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.


RESULTS
We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology.


AVAILABILITY AND IMPLEMENTATION
All the models analyzed are available at http://cnn.csail.mit.edu


CONTACT
gifford@mit.edu


SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Stabilizers of Classes of Representable Matroids

Let M be a class of matroids representable over a field F. A matroid N # M stabilizes M if, for any 3-connected matroid M # M, an F-representation of M is uniquely determined by a representation of any one of its N-minors. One of the main theorems of this paper proves that if M is minor-closed and closed under duals, and N is 3-connected, then to show that N is a stabilizer it suffices to check 3-connected matroids in M that are single-element extensions or coextensions of N, or are obtained by a single-element extension followed by a single-element coextension. This result is used to prove that a 3-connected quaternary matroid with no U3, 6 -minor has at most (q&2)(q&3) inequivalent representations over the finite field GF(q). New proofs of theorems bounding the number of inequivalent representations of certain classes of matroids are given. The theorem on stabilizers is a consequence of results on 3-connected matroids. It is shown that if N is a 3-connected minor of the 3-connected matroid M, and |E(M)&E(N)| 3, then either there is a pair of elements x, y # E(M) such that the simplifications of M x, M y, and M x, y are all 3-connected with N-minors or the cosimplifications of M"x, M"y, and M"x, y are all 3-connected with N-minors, or it is possible to perform a 2&Y or Y&2 exchange to obtain a matroid with one of the above properties. 1999
Neural Network with Binary Activations for Efficient Neuromorphic Computing Weier Wan

In this paper, we proposed techniques to train and deploy a multi-layer neural network using softmax loss for binary activations to best capitalize on its energy efficiency. These techniques include using the gradient of tanh function to approximate gradient of discrete binary threshold function during the backpropagation of training, and using a stochastic multi-sampling approach to convert high-precision input into a set of binary inputs. We used a three-layer fully connected neural network and experiment the techniques on CIFAR-10 and MNIST datasets. With the proposed strategy, we are able to train and deploy a model with binary activations with no or little loss of performance compared to a model with high-precision neurons.
An SSL Back-End Forwarding Scheme in Cluster-Based Web Servers

State-of-the-art cluster-based data centers consisting of three tiers (Web server, application server, and database server) are being used to host complex Web services such as e-commerce applications. The application server handles dynamic and sensitive Web contents that need protection from eavesdropping, tampering, and forgery. Although the secure sockets layer (SSL) is the most popular protocol to provide a secure channel between a client and a cluster-based network server, its high overhead degrades the server performance considerably and, thus, affects the server scalability. Therefore, improving the performance of SSL-enabled network servers is critical for designing scalable and high-performance data centers. In this paper, we examine the impact of SSL offering and SSL-session-aware distribution in cluster-based network servers. We propose a back-end forwarding scheme, called ssl_with_bf, that employs a low-overhead user-level communication mechanism like virtual interface architecture (VIA) to achieve a good load balance among server nodes. We compare three distribution models for network servers, round robin (RR), ssl_with_session, and ssl_with_bf, through simulation. The experimental results with 16-node and 32-node cluster configurations show that, although the session reuse of ssl_with_session is critical to improve the performance of application servers, the proposed back-end forwarding scheme can further enhance the performance due to better load balancing. The ssl_with_bf scheme can minimize the average latency by about 40 percent and improve throughput across a variety of workloads.
An Improved Direct AC–AC Converter for Voltage Sag Mitigation

Dynamic voltage restorer (DVR) is a definitive solution toward compensation of voltage sag with phase jump. Conventional DVR topologies, however, have dc links and two-stage power conversions. This increases its size, cost, and associated losses. Therefore, topologies without the dc link, mitigating sag by utilizing direct ac-ac converters, are preferable over the conventional ones. As no storage device is employed, compensation by these topologies is limited only by the voltages at the point of common coupling that is feeding the converters. In this paper, a direct-ac-ac-converter-based topology fed with line voltages is proposed. The arrangement provides increased range of compensation in terms of magnitude and phase angle correction. Detailed simulations have been carried out in MATLAB to compare the capability of the proposed topology with other similar topologies. Experimental results are presented to validate the capability of the topology to compensate various sags.
The integrative role of the project management office in the front end of innovation

This research addresses management control in the front end of innovation projects. We conceptualize and analyze PMOs more broadly than just as a specialized project-focused organizational unit. Building on theories of management control, organization design, and innovation front end literature, we assess the role of PMO as an integrative arrangement. The empirical material is derived from four companies. The results show a variety of management control mechanisms that can be considered as integrative organizational arrangements. Such organizational arrangements can be considered as an alternative to a non-existent PMO, or to complement a (non-existent) PMO's tasks. The paper also contrasts prior literature by emphasizing the desirability of a highly organic or embedded matrix structure in the organization. Finally, we propose that the development path of the management approach proceeds by first emphasizing diagnostic and boundary systems (with mechanistic management approaches) followed by intensive use of interactive and belief systems (with value-based management approaches). The major contribution of this paper is in the organizational and managerial mechanisms of a firm that is managing multiple innovation projects. This research also expands upon the existing PMO research to include a broader management control approach for managing projects in companies. © 2011 Elsevier Ltd. and IPMA. All rights reserved.
Applying spatial modelling tools to assess groundwater contamination risk from agricultural origin

In this lecture, recent advances for modelling of agrochemical transport at the regional scale will be presented
Palaeomagnetism of the Puerto Vallarta intrusive complex and the accretion of the Guerrero terrain, Mexico

Abstract The batholith exposed around Puerto Vallarta is part of the Guerrero tectonostratigraphic terrain, presumably accreted to Mexico during the Laramide orogenic phase. Fourteen sites on this batholith were studied palaeomagnetically and geochemically. Nine sites define a paleopole at 76.6°N, 181.8°E, with α 95 = 6.6°, coinciding with the reference pole for stable North America. It is proposed that no important movements of the region relative to North America have occurred since emplacement of the intrusive rocks ∼ 80 Ma ago. This would mean that the Guerrero terrain was accreted to the Mexican continental margin before that time. Geochemical data support this interpretation, characterizing the rocks as volcanic-arc granites intruded at an active continental margin.
Inverse Kinematics Solution for Robotic Manipulators Using a CUDA-Based Parallel Genetic Algorithm

Inverse kinematics is one of the most basic problems that needs to be solved when using robot manipulators in a work environment. A closed-form solution is heavily dependent on the geometry of the manipulator. A solution may not be possible for certain robots. On the other hand, there may be an infinite number of solutions, as is the case of highly redundant manipulators. We propose a Genetic Algorithm (GA) to approximate a solution to the inverse kinematics problem for both the position and orientation. This algorithm can be applied to different kinds of manipulators. Since typical GAs may take a considerable time to find a solution, a parallel implementation of the same algorithm (PGA) was developed for its execution on a CUDA-based architecture. A computational model of a PUMA 500 robot was used as a test subject for the GA. Results show that the parallel implementation of the algorithm was able to reduce the execution time of the serial GA significantly while also obtaining the solution within the specified margin of error.
Integrated model-based systems engineering (MBSE) applied to the Simulation of a CubeSat mission

Small satellite missions are becoming increasingly complex as scientists and engineers propose to utilize them to accomplish more ambitious science and technology goals. Small satellites such as CubeSats are challenging to design because they have limited resources, coupled subsystems, and must operate in dynamic environments. Model Based Systems Engineering (MBSE) is a key practice to advance systems engineering that can benefit CubeSat missions. MBSE creates a system model that helps integrate other discipline specific engineering models and simulations. The system level model is initiated at the start of a project and evolves throughout development. It provides a cohesive and consistent source of system requirements, design, analysis, and verification. This paper describes an integrated, executable MBSE representation of the Radio Aurora Explorer (RAX) CubeSat mission. The purpose of the RAX mission is to study the formation of magnetic field-aligned electron density irregularities in the Earth's ionosphere, which are known to disrupt tracking and communication between Earth and orbiting spacecraft. The RAX CubeSat model describes the configuration and properties for various systems and subsystems, and is capable of executing behavior and parametric models for analyzing subsystem functions and states of the spacecraft. It is comprised of a SysML model created with MagicDraw®, a set of analytical models developed in MATLAB®, and a high fidelity space system simulation model created in STK®. ModelCenter was used to integrate the analytical and simulation models. The integrated analyses were linked to the SysML model using MBSE Analyzer, a bridge between SysML tools and ModelCenter. Behavioral models were executed for a representative RAX mission to study energy state and data collection capabilities. This work was undertaken to demonstrate the power, scalability, and utility of MBSE tools and methods that are available to help meet the challenge of designing spacecraft missions of ever-increasing complexity. The RAX CubeSat model will be made available to the academic community for further study and potential extension for more complex missions.
Investigating the feasibility and acceptability of a cognitive behavioural suicide prevention therapy for people in acute psychiatric wards (the ‘INSITE’ trial): study protocol for a randomised controlled trial

BACKGROUND
Suicide is a major cause of preventable death, and suicidal behaviour is prevalent in acute psychiatric wards. People admitted to acute psychiatric wards often experience repeated episodes of suicidal behaviour, causing great distress and heavy use of NHS services. There is little research investigating effective psychological treatments for suicidal patients in inpatient settings although previous research has found support for psychological therapies which specifically target suicidal behaviour. This paper describes the protocol of a single blind RCT to investigate the acceptability and feasibility of a cognitive behavioural intervention targeting suicidality (CBSP) for suicidal people in acute psychiatric wards.


METHODS/DESIGN
A single blind RCT comparing treatment as usual (TAU) to TAU plus Cognitive Behavioural Suicide Prevention (CBSP) therapy (TAU + CBSP). Sixty participants (aged 18-65 years) who are suicidal, or have been within the past 3 months, will be recruited from NHS trusts in the North West of England. Our primary objective is to determine whether CBSP is feasible, acceptable and efficacious when compared to patients who receive TAU alone. Secondary aims are the impact of CBSP on suicidal thinking, behaviours, functioning, quality of life, service use and psychological factors associated with suicide. Assessments take place at baseline, 6 weeks and 6 months (end of treatment). The analysis will report on the feasibility and acceptability of CBSP. Qualitative data from staff and service users will inform feasibility and acceptability data.


DISCUSSION
Psychiatric inpatients are a high-risk group and the use of psychological therapies in these settings is rare and requires evaluation. This study is essential to investigate the unique contextual challenges involved in delivering psychological therapy to suicidal inpatients and to identify any necessary modifications required within inpatient settings. The findings will inform a larger, definitive trial.


TRIAL REGISTRATION
15 March 2012, PB-PG-1111-26026, NIHR ISRCTN17890126 .
Discrete Equations and Software Reliability Growth Models

We propose a criterion to determine a more appropriate software reliability growth model in the early development phase. The criterion with a discrete software reliability growth model determines the absolute worth of a model because the discrete software reliability growth model perfectly reproduces the parameter estimates when the data are used as an exact solution of the equation. The values of the proposed criterion of a discrete software reliability growth model are smaller than those of another software reliability growth model in all periods during the test phase with actual data sets. The discrete software reliability growth models are described with difference equations which have exact solutions. The models yield accurate parameter estimates in spite of a small amount of input data in an actual software testing. Therefore, the criterion and discrete software reliability growth models enable us to predict in the early development phase when software can be released.