snu-aidas/HVMap-dataset huggingface dataset:
A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability

A biopharmaceutics drug classification scheme for correlating in vitro drug product dissolution and in vivo bioavailability is proposed based on recognizing that drug dissolution and gastrointestinal permeability are the fundamental parameters controlling rate and extent of drug absorption. This analysis uses a transport model and human permeability results for estimating in vivo drug absorption to illustrate the primary importance of solubility and permeability on drug absorption. The fundamental parameters which define oral drug absorption in humans resulting from this analysis are discussed and used as a basis for this classification scheme. These Biopharmaceutic Drug Classes are defined as: Case 1. High solubility-high permeability drugs, Case 2. Low solubility-high permeability drugs, Case 3. High solubility-low permeability drugs, and Case 4. Low solubility-low permeability drugs. Based on this classification scheme, suggestions are made for setting standards for in vitro drug dissolution testing methodology which will correlate with the in vivo process. This methodology must be based on the physiological and physical chemical properties controlling drug absorption. This analysis points out conditions under which no in vitro-in vivo correlation may be expected e.g. rapidly dissolving low permeability drugs. Furthermore, it is suggested for example that for very rapidly dissolving high solubility drugs, e.g. 85% dissolution in less than 15 minutes, a simple one point dissolution test, is all that may be needed to insure bioavailability. For slowly dissolving drugs a dissolution profile is required with multiple time points in systems which would include low pH, physiological pH, and surfactants and the in vitro conditions should mimic the in vivo processes. This classification scheme provides a basis for establishing in vitro-in vivo correlations and for estimating the absorption of drugs based on the fundamental dissolution and permeability properties of physiologic importance.
An extra low-power 1Tbit/s bandwidth PLL/DLL-less eDRAM PHY using 0.3V low-swing IO for 2.5D CoWoS application

A 1Tbit/s bandwidth PHY is demonstrated through 2.5D CoWoS platform. Two chips: SOC and eDRAM have been fabricated in TSMC 40nm CMOS technology and stacked on another silicon interposer chip in 65nm technology. Total 1024 DQ bus operating in 1.1Gbit/s with Vmin=0.3V are proven in experimental results. A novel timing compensation mechanism is presented to achieve a low-power and small area eDRAM PHY that excludes PLL/DLL but retains good timing margin. Another data sampling alignment training approach is reserved to enhance timing robustness. A compact low-swing IO also achieves great power efficiency of 0.105mW/Gbps.
The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan

With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.
Efficient parallel translating embedding for knowledge graphs

Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.
Photogrammetric Multi-View Stereo and Imaging Network Design
Transcriptional reprogramming in yeast using dCas9 and combinatorial gRNA strategies

BACKGROUND
Transcriptional reprogramming is a fundamental process of living cells in order to adapt to environmental and endogenous cues. In order to allow flexible and timely control over gene expression without the interference of native gene expression machinery, a large number of studies have focused on developing synthetic biology tools for orthogonal control of transcription. Most recently, the nuclease-deficient Cas9 (dCas9) has emerged as a flexible tool for controlling activation and repression of target genes, by the simple RNA-guided positioning of dCas9 in the vicinity of the target gene transcription start site.


RESULTS
In this study we compared two different systems of dCas9-mediated transcriptional reprogramming, and applied them to genes controlling two biosynthetic pathways for biobased production of isoprenoids and triacylglycerols (TAGs) in baker's yeast Saccharomyces cerevisiae. By testing 101 guide-RNA (gRNA) structures on a total of 14 different yeast promoters, we identified the best-performing combinations based on reporter assays. Though a larger number of gRNA-promoter combinations do not perturb gene expression, some gRNAs support expression perturbations up to ~threefold. The best-performing gRNAs were used for single and multiplex reprogramming strategies for redirecting flux related to isoprenoid production and optimization of TAG profiles. From these studies, we identified both constitutive and inducible multiplex reprogramming strategies enabling significant changes in isoprenoid production and increases in TAG.


CONCLUSION
Taken together, we show similar performance for a constitutive and an inducible dCas9 approach, and identify multiplex gRNA designs that can significantly perturb isoprenoid production and TAG profiles in yeast without editing the genomic context of the target genes. We also identify a large number of gRNA positions in 14 native yeast target pomoters that do not affect expression, suggesting the need for further optimization of gRNA design tools and dCas9 engineering.
Detection of hiding in the least significant bit

In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.
Bootstrapping Biomedical Ontologies for Scientific Text using NELL

We describe an open information extraction system for biomedical text based on NELL (the NeverEnding Language Learner) [7], a system designed for extraction from Web text. NELL uses a coupled semi-supervised bootstrapping approach to learn new facts from text, given an initial ontology and a small number of “seeds” for each ontology category. In contrast to previous applications of NELL, in our task the initial ontology and seeds are automatically derived from existing resources. We show that NELL’s bootstrapping algorithm is susceptible to ambiguous seeds, which are frequent in the biomedical domain. Using NELL to extract facts from biomedical text quickly leads to semantic drift. To address this problem, we introduce a method for assessing seed quality, based on a larger corpus of data derived from the Web. In our method, seed quality is assessed at each iteration of the bootstrapping process. Experimental results show significant improvements over NELL’s original bootstrapping algorithm on two types of tasks: learning terms from biomedical categories, and named-entity recognition for biomedical entities using a learned lexicon.
Impact of IEEE 802.11n/ac PHY/MAC High Throughput Enhancements over Transport/Application Layer Protocols - A Survey

Since the inception of Wireless Local Area Networks (WLANs) in the year 1997, it has tremendously grown in the last few years. IEEE 802.11 is popularly known as WLAN. To provide the last mile wireless broadband connectivity to users, IEEE 802.11 is enriched with IEEE 802.11a, IEEE 802.11b and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac and IEEE 802.11ad are introduced with enhancements to the physical (PHY) layer and medium access control (MAC) sublayer to provide much higher data rates and thus these amendments are called High Throughput WLANs (HT-WLANs). In IEEE 802.11n, the data rate has increased up to 600 Mbps, whereas IEEE 802.11ac/ad is expected to support a maximum throughput of 1 to 7 Gbps over wireless media. For both standards, PHY is enhanced with multiple-input multiple-output (MIMO) antenna technologies, channel bonding, short guard intervals (SGI), enhanced modulation and coding schemes (MCS). At the same time, MAC layer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, existing studies reveal that although PHY and MAC enhancements promise to improve physical data rate significantly, they yield negative impact over upper layer protocols – mainly for reliable end-to-end transport/application layer protocols. As a consequence, a large number of schools have focused researches on HT-WLANs to improve the coordination among PHY/MAC and upper layer protocols and thus, boost up the performance benefit. In this survey, we discuss the impact of enhancements of PHY/MAC layer in HT-WLANs over transport/application layer protocols. Several measures have been reported in the literature that boost up data rate of WLANs and use aforesaid enhancements effectively for performance benefit of end-to-end protocols. We also point out limitations of existing researches and list down different open challenges that can be explored for the development of next generation HT-WLAN technologies. Keywords-IEEE 802.11n, IEEE 802.11ac, MIMO, MU-MIMO, Channel bonding, Short-guard interval (SGI), Frame aggregation, Block Acknowledgement, TCP/UDP Throughput.
Comparison among three analytical methods for knowledge communities group-decision analysis

Knowledge management can greatly facilitate an organization’s learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), ‘‘Technique for Order Preference by Similarity to an Ideal Solution’’ (TOPSIS) and ‘‘VlseKriterijumska Optimizacija I Kompromisno Resenje’’ (VIKOR), are based on an aggregating function representing ‘‘closeness to the ideal point’’. The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems, provide excellent results close to reality, and grant superior analysis. 2006 Elsevier Ltd. All rights reserved.
User Profile Based Research Paper Recommendation

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. [1] For a recommender system to be effective, it has to have a comprehensive and appropriately catalogued repository of resources. Also, it has to be accurate in understanding the need of the user, and basically has to correctly profile the user. Ideally, one needs to take into consideration the fact that a user’s preferences are constantly changing and that is often not done by recommender systems. To meet this need, we propose an interactive research paper recommender system that observes the various themes that occur in a researcher’s work over time and keeps learning the user’s preferences constantly based on the user’s feedback of the papers recommended to them. We use a topic model to take into account the themes occurring in the document. The query is modeled as a bag of topics, where topics represent latent generative clusters of relative words. The entire collection or research papers is also modeled this way. We then estimate the similarity between the query and each research paper using a bag-of-topics based similarity measure and find the best ones. To take into account the users preferences, we keep track of the papers which the user likes and augment the query with the topics which recur in a lot of the users preferred papers the next time recommendations are required. We also truncate the topics which the user seems to ignore.
Reflection from layered surfaces due to subsurface scattering

The reflection of light from most materials consists of two major terms: the specular and the diffuse. Specular reflection may be modeled from first principles by considering a rough surface consisting of perfect reflectors, or micro-facets. Diffuse reflection is generally considered to result from multiple scattering either from a rough surface or from within a layer near the surface. Accounting for diffuse reflection by Lambert’s Cosine Law, as is universally done in computer graphics, is not a physical theory based on first principles. This paper presents a model for subsurface scattering in layered surfaces in terms of one-dimensional linear transport theory. We derive explicit formulas for backscattering and transmission that can be directly incorporated in most rendering systems, and a general Monte Carlo method that is easily added to a ray tracer. This model is particularly appropriate for common layered materials appearing in nature, such as biological tissues (e.g. skin, leaves, etc.) or inorganic materials (e.g. snow, sand, paint, varnished or dusty surfaces). As an application of the model, we simulate the appearance of a face and a cluster of leaves from experimental data describing their layer properties. CR
Finding Groups in Data: An Introduction to Cluster Analysis

Description: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.
Types for Units-of-Measure: Theory and Practice

Units-of-measure are to science what types are to programming. In science and engineering, dimensional and unit consistency provides a first check on the correctness of an equation or formula, just as in programming the passing of a program by the type-checker eliminates one possible reason for failure. Units-of-measure errors can have catastrophic consequences, the most famous of which was the loss in September 1999 of NASA’s Mars Climate Orbiter probe, caused by a confusion between newtons (the SI unit of force) and lbf (poundforce, a unit of force sometimes used in the US). The report into the disaster made many recommendations [14]. Notably absent, though, was any suggestion that programming languages might assist in the prevention of such errors, either through static analysis tools, or through type-checking. Over the years, many people have suggested ways of extending programming languages with support for static checking of units-of-measure. It’s even possible to abuse the rich type systems of existing languages such as C++ and Haskell to achieve it, but at some cost in usability [18, 6]. More recently, Sun’s design for its Fortress programming language has included type system support for dimensions and units [2]. In this short course, we’ll look at the design supported by the F# programming language, the internals of its type system, and the theoretical basis for units safety. The tutorial splits into three parts:
Online Attendance Management System Using RFID with Object Counter

Educational institutions’ administrators in our country and the whole world are concerned about regularity of student attendance. Student overall academic performance is affected by it. The conventional method of taking attendance by calling names or signing on paper is very time consuming, and hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. A system that can automatically capture student’s attendance by flashing their student card at the RFID reader and save all the mentioned troubles was given by T.S.Lim et al. [1].The RFID system using web-based applications such as ASP.NET and IIS server to cater the recording and reporting of the students’ attendances was given by Abdul Aziz Mohammed et.al. [2]. Arulogun O. T. et al made an attempt to solve recurrent lecture attendance monitoring problem in developing countries using RFID technology [3]. Rajan Patel et al [4] presents the integration of ubiquitous computing systems into classroom for managing the students’ attendance using RFID technology. H.B. Hamid et al [5] gave a system that has been built using the web-based applications such as JSP, MySQL and Apache to cater the recording and reporting of the students’ attendances. NetBeans IDE 6.1 is used for developing the overall system. We have proposed the system in this paper using C#. Microsoft Visual Studio is used for the system designing. Also, the issue related to fake /false attendance through the RFID system has been addressed, we eliminate it by using a special object counter for the head count. Ankita Agrawal & Ashish Bansal 132