Miracle-dz/UniFAR-FTUs huggingface dataset:
An application of convolutional neural network in street image classification: the case study of london

Street frontage quality is an important element in urban design as it contributes to the interest, social life and success of public spaces. To collect the data needed to evaluate street frontage quality at the city or regional level using traditional survey method is both costly and time consuming. As a result, this research proposes a pipeline that uses convolutional neural network to classify the frontage of a street image through the case study of Greater London. A novelty of the research is it uses both Google streetview images and 3D-model generated streetview images for the classification. The benefit of this approach is that it can provide a framework to test different urban parameters to help evaluate future urban design projects. The research finds encouraging results in classifying urban frontage quality using deep learning models. This research also finds that augmenting the baseline model with images produced from a 3D-model can improve slightly the accuracy of the results. However these results should be taken as preliminary, where we acknowledge several limitations such as the lack of adversarial analysis, labeled data, or parameter tuning. Despite these limitations, the results of the proof-of-concept study is positive and carries great potential in the application of urban data analytics.
The measurement of utilization in health care programs.

H~J EALTH CARE plans have enormous potenIItial for contributing to an understanding of the factors influencing the patterns of use of health care facilities and manpower, as well as of the relation between the patterns of delivery of care and the health of a population. Data in the files of health care plans can provide considerable insight into such questions as: What is the relative impact of different forms of organization, such as group practice and solo practice, on the utilization of hospitals? What is the relationship between the method of delivery of health services and the pattern of utilization? What is the quality of care provided in the health care plan? Are some types of plans more acceptable to consumers and providers than others? What impact does the use of allied health manpower have on cost? One of the keys to unlocking this great potential lies in the design of the reporting system for the health care plan. The reporting system must serve several purposes. It must be designed to foster good patient care. It must provide management with the informational tools it needs to maintain an efficient operation and control costs. It should foster research into the factors influencing the use of health care facilities and manpower and into the relationship between the patterns of health care and the health of the population. These several purposes are not mutually exclusive; rather they are complementary. This paper presents the concepts and principles that should guide the development of the reporting system if it is to fulfill these several functions. Instead of approaching the subject in the abstract, the framework to be used is a set of minimal tables prepared by an ad hoc group that grew out of a conference held in the spring of 1970. This conference, supported by the National Center for Health Services Research and Development, Health Services and Mental Health Administration, brought together representatives from six medical schools that were providing, or planning to provide, comprehensive health services (1). To the degree that these schools shared a commonality of purpose, the representatives believed that they should b. able to compile information that would permit a comparison of utilization of services and costs. The Guidelines for Producing Uniform Data for Health Care Plans (2) prepared by the ad hoc working group contain a minimal set of tables. These tables are suggested for use by any health care plan serving a definable population, Dr. Densen is director of the Harvard Center for Community Health and Medical Care, Harvard University School of Public Health, Boston, Mass. The paper on which this article is based was presented by Dr. Densen at the Health Care Evaluation Seminars, McMaster University, Hamilton, Ont. ,, Canada, in May 1971. Tearsheet requests to Dr. Paul M. Densen, Harvard Center for Community Health and Medical Care, 643 Huntington Ave., Boston, Mass. 02115.
The Kinetics of PDZ Domain-Ligand Interactions and Implications for the Binding Mechanism*

PDZ domains are protein adapter modules present in a few hundred human proteins. They play important roles in scaffolding and signal transduction. PDZ domains usually bind to the C termini of their target proteins. To assess the binding mechanism of this interaction we have performed the first in-solution kinetic study for PDZ domains and peptides corresponding to target ligands. Both PDZ3 from postsynaptic density protein 95 and PDZ2 from protein tyrosine phosphatase L1 bind their respective target peptides through an apparent A + B → A·B mechanism without rate-limiting conformational changes. But a mutant with a fluorescent probe (Trp) outside of the binding pocket suggests that slight changes in the structure take place upon binding in protein tyrosine phosphatase-L1 PDZ2. For PDZ3 from postsynaptic density protein 95 the pH dependence of the binding reaction is consistent with a one-step mechanism with one titratable group. The salt dependence of the interaction shows that the formation of electrostatic interactions is rate-limiting for the association reaction but not for dissociation of the complex.
Mathematical modeling of hydrodinamic processes in the rotors of flow micro-hydropower plants

The paper is dedicated to the study of hydrodynamic effects in the rotors of flow micro-hydropower plants. There was argued the geometry of hydrodynamic profiles of blades efficient in terms of energy conversion efficiency based on which original concepts of hydrodynamic rotors were developed. Using the CAD models of the proposed rotors: there were performed complex CFD simulations of the transient fluid flow through the rotors and near the blades in order to determine the influence of the constructive and kinematic parameters on the power characteristics and hydrodynamic performance factor of the rotors used in micro-hydropower plants; the fluid flow phenomenon was analyzed in the boundary layer and there were identified technical solutions for control and minimization of its negative impact on the energy conversion of the micro-hydropower plants.
Identification and Management of Albuminuria in the Primary Care Setting

Albuminuria is an important risk marker for adverse cardiovascular (CV) and renal outcomes and mortality. The relationship between albuminuria and risk is continuous and linear, like that of blood pressure and cardiovascular risk. Evidence now supports increased risk even at levels traditionally considered within normal limits. In high‐risk patients, routine annual screening can detect changes in urine albumin excretion and improve the timely identification of albuminuria, and therefore should be considered in patients with diabetes, hypertension, and chronic kidney disease. Preferred simple screening methods appropriate for use in the primary care setting include microalbumin‐specific dipsticks and urinary albumin:creatinine ratio determination (from a spot urine sample). Cornerstones of albuminuria treatment include risk factor management, ongoing monitoring, and, in patients with hypertension, chronic kidney disease, or diabetes, the use of renin‐angiotensin‐aldosterone system (RAAS)–blocking agents. Both angiotensin‐converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) have demonstrated utility in this regard; data from studies of direct renin inhibition are promising. The combined use of an ACE inhibitor and ARB was once considered a viable option for the treatment of albuminuria; however, results of the Ongoing Telmisartan Alone and in Combination With Ramipril Global Endpoint Trial (ONTARGET) raised important questions regarding the benefits and limitations of dual RAAS blockade. Ongoing studies should provide important insight into the effects of this approach on renal outcomes. J Clin Hypertens (Greenwich). 2011;13:438–449. ©2011 Wiley Periodicals, Inc.
Nomenclature for factors of the HLA system, update September 2011.

The following sequences have been submitted to the Nomenclature Committee since the August 2011 nomenclature update and, following agreed policy, have been assigned official allele designations (1). Full details of all sequences will be published in a forthcoming report. Below are listed the newly assigned sequences (Table 1) and confirmations of previously reported sequences (Table 2). The accession number of each sequence is given and these can be used to retrieve the sequence files from the EMBL, GenBank or DDBJ data libraries. Although accession numbers have been assigned by the data libraries and most sequences are already available, there is still the possibility that an author may not yet have allowed the sequence to be released; in such a case you will have to contact the submitting author directly.
Multi-agent Deep Reinforcement Learning for Autonomous Driving

This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R2 that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agent policies. Our approach extends the traditional deep reinforcement learning algorithm by making use of stochastic policies during execution time and stationary policies for homogenous agents during training. We also use a residual neural network as the Q-value function approximator. The approach is shown to generalize multi-agent policies to new environments, and across varying numbers of agents. We also demonstrate how transfer learning can be applied to learning policies for large groups of agents in order to decrease convergence time. We conclude with other potential applications and extensions for future work.
Leveraging anatomical information to improve transfer learning in brain–computer interfaces

Objective. Brain–computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.
Simplified Fair Scheduling and Antenna Selection Algorithms for Multiuser MIMO Orthogonal Space-Division Multiplexing Downlink

We consider the downlink of a multiuser multiple-input multiple-output (MIMO) system, where the base station and the mobile receivers are equipped with multiple antennas. We propose simplified algorithms for channel-aware multiuser scheduling in conjunction with receive antenna selection for two downlink multiuser orthogonal space-division multiplexing techniques: block diagonalization and successive optimization. The algorithms greedily maximize the weighted sum rate. The algorithms add the best user at a time from the set of users that are not selected yet to the set of selected users until the desired number of users has been selected. To apply the proportional fairness criterion, simplified user scheduling metrics are proposed for block diagonalization and successive optimization. Two receive antenna selection algorithms are also proposed, which further enhance the power gain of the equivalent single-user channel after orthogonal precoding by selecting a subset of the receive antennas that contributes the most toward the total power gain of the channel. A user grouping technique is used to further lower the complexity of the selection algorithms. We compare various multiuser MIMO scheduling strategies that are applied to block diagonalization and successive optimization transmission techniques through simulation. Simulation results demonstrate the effectiveness of the proposed algorithms in ensuring throughput fairness among users. Results also show that when the number of users is large, the proposed scheduling algorithms perform close to the exhaustive search algorithms and previously proposed greedy scheduling algorithms, but with much lower complexity.
Detecting Denial of Service Attack Using Principal Component Analysis with Random Forest Classifier

--Nowadays, computer network systems plays gradually an important role in our society and economy. It became a targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason that computer security has become an essential concern for network administrators. In this paper, an exploration of anomaly detection method has been presented. The proposed system uses a statistical method called principal component analysis to filter the attributes and random forest classifier is used to detect various attack present in Denial of Service using NSL-KDD dataset. The principal component Analysis filters attributes drastically to improve classification performance. Regarding to the task of intrusion detection a new method of random forest classifier is used to improve accuracy. Experimental result shows that the proposed method can achieve high detection rate than other existing machine learning techniques. This approach is dynamic in the sense that the model is updated based on the variations of its input. Our experiments revealed relevant results that can effectively be used to classify Denial of Service attacks. malathi.arunachalam@yahoo.com Keyword--Intrusion Detection, Principal component analysis, Random Forest, NSL-KDD dataset
Components of Short-Term Memory and Their Relation to Language Processing

Verbal working memory consists of separable capacities for the retention of phonological and semantic information. Within the phonological domain, there are independent capacities for retaining input-phonological codes and output-phonological codes. The input-phonological capacity does not appear to be critical for language comprehension but is involved in verbatim repetition and long-term learning of new words. The semantic capacity is critical for both comprehension and production and for the learning of new semantic information. Different neural structures appear to underlie these capacities, with a left-parietal region involved in input-phonological retention and a left-frontal region involved in semantic retention.
Strategic Alignment and Misalignment of Knowledge Management Systems: A Social Representation Perspective

To derive more business value from existing organizational knowledge, many organizations seek to rely on strategically aligned knowledge management systems (KMS). However, as documented in prior studies, they often underestimate the challenges about social interactions and users' perceptions in response to new information systems. Based on an interpretive case study, this paper examines the implementation of a KMS to show how social representations of four groups of users resulted in the misalignment of the KMS with the organizational strategy. The social representation lens allows us to interpret strategic alignment in terms of dynamic processes of anchoring and objectification that aid individuals and groups to make sense of KMS initiatives. The groups studied developed different cognitive views of the KMS that ultimately led to a strategic misalignment. The key implication is that social interactions within and among groups shape KMS alignment with organizational strategy, thus elucidating the nature of system use.
Transcranial Doppler ultrasound in children with stroke and cerebrovascular disorders

Purpose of review A major advantage of transcranial Doppler (TCD) ultrasound is its ability to measure cerebral hemodynamics noninvasively at the patient's bedside. This article summarizes the basic physics and variables used during TCD, recent pediatric data published on the use of TCD in stroke and cerebrovascular disorders and how it may impact diagnosis and management, and some issues to be resolved so that TCD can be put into clinical practice. Recent findings In sickle cell disease in children, TCD is the gold standard stroke prediction tool. Recent data suggest that TCD may provide important information in ischemic stroke because of other childhood arteriopathies such as moyamoya syndrome, transient or focal cerebral arteriopathy, and genetic/syndromic causes. TCD may also detect cerebral emboli and diagnose right-to-left atrial cardiac shunts in children with cryptogenic stroke and transient ischemic attack. Summary There are many potential clinical applications for TCD in pediatric stroke and cerebrovascular disorders. Additional research in children is needed to determine whether TCD can identify markers of increased stroke risk, elucidate underlying stroke mechanisms, influence the choice of additional testing and treatment, and ultimately impact patient outcomes.
Complete Genome Sequence of an Acinetobacter Strain Harboring the NDM-1 Gene

ABSTRACT The NDM-1 gene is a significant public health concern. Acinetobacter is one of the most prevalent opportunistic pathogens causing recent nosocomial infections with NDM-1, and drug-resistant strains pose serious threats to public health worldwide. Herein, we present the genomic sequence of Acinetobacter calcoaceticus subsp. anitratus XM1570, a multidrug-resistant isolate that carries the blaNDM-1 gene.
Low noise coupling techniques for multi-phase oscillators

This paper presents a capacitive-coupling technique for low phase noise multi-phase oscillators. The proposed coupling techniques can improve the phase noise performance while maintain good phase accuracy over wide frequency range. A 4.3-5.3 GHz two-phase VCO prototype is implemented to demonstrate the effectiveness of the proposed technique. It achieves a measured phase noise of -120 to -124.04dBc /Hz @ 1MHz offset and a measured phase error of 0.23-0.91° across a 1GHz tuning range.
An Apprenticeship with the CWIC Corpus: a Tool for Learner Writers in Italian

This paper discusses the composition and application in teaching of the Contemporary written Italian corpus (CWIC), created at Griffith University as a resource for Italian students at intermediate level. Since 1998, CWIC has been used in our second-year and third-year courses, primarily as a reference tool while writing. Our rationale in introducing CWIC drew particularly on the idea that learners armed with a corpus can act as researchers who discover rules about language for themselves, by formulating and testing hypotheses with reference to authentic examples of word use in context, as outlined by Johns (1991b). We conducted a gradual and guided apprenticeship in corpus use over a whole semester, intended to prepare the students sufficiently to work independently with CWIC when a web-based version comes online. We then sought to evaluate the effectiveness of the students’ investigations and identify the difficulties they encountered when working without a teacher. Although the students were very enthusiastic about using CWIC, our analysis of their investigations showed they were less successful than we had anticipated. In particular, it highlighted two types of problems – in managing a hypothesis-testing process and in observation of data – that significantly reduced their effectiveness in the ‘learner as researcher’ role. We are seeking to address these difficulties by changing the objectives and priorities of our training. A key element in our response is to introduce students to CWIC with an ‘observe and borrow’ mentality first, before progressing to an ‘observe and derive rules’ approach such as we had originally envisaged would be the norm.
Progress and Regress of Time Dependent Data and Application in Bank Branch

To evaluate each decision making unit having time dependent inputs and outputs data, a new method has been developed and reported here. This method uses the Malmquist productivity index, and is a very simple function based on Cubic Spline function to determine the progress and regress of that unit. To show the capability of this developed method, the data of 9 branches of a commercial bank has been used, evaluated, and reported.
CommAnalyzer: Automated Estimation of Communication Cost on HPC Clusters Using Sequential Code

MPI+X is the de facto standard for programming applications on HPC clusters. e performance and scalability on such systems is limited by the communication cost on dierent number of processes and compute nodes. erefore, the current communication analysis tools play a critical role in the design and development of HPC applications. However, these tools require the availability of the MPI implementation, which might not exist in the early stage of the development process due to the parallel programming eort and time. is paper presents CommAnalyzer, an automated tool for communication model generation from a sequential code. CommAnalyzer uses novel compiler analysis techniques and graph algorithms to capture the inherent communication characteristics of sequential applications, and to estimate their communication cost on HPC systems. e experiments with real-world, regular and irregular scientic applications demonstrate the utility of CommAnalyzer in estimating the communication cost on HPC clusters with more than 95% accuracy on average.