Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximation Algorithms for Stochastic Inventory Control Models

We consider stochastic control inventory models in which the goal is to coordinate a sequence of orders of a single commodity, aiming to supply stochastic demands over a discrete finite horizon with minimum expected overall ordering, holding and backlogging costs. In particular, we consider the periodic-review stochastic inventory control problem and the stochastic lot-sizing problem in the case where demands over time are correlated and non-stationary (time-dependent). In these cases, it is usually very hard to compute the optimal policy. We provide what we believe to be the first computationally efficient policies with constant worst-case performance guarantees. More specifically, we provide a general 2-approximation algorithm for the periodic-review stochastic inventory control problem and a 3-approximation algorithm for the stochastic lot-sizing problem. Our approach is based on several novel ideas: we present a new (marginal) cost accounting for stochastic inventory models; we use cost-balancing techniques; and we consider non base-stock (order-up-to) policies that are extremely easy to implement on-line. Our results are valid for all of the currently known approaches in the literature to model correlation and non-stationarity of demands over time.
Harmonic Proportional Bandwidth Allocation for Service Differentiation on Streaming Servers

Recent advance of real-time audio/video transcoding technology enables streaming servers to manage the constrained network-I/O bandwidth available to the Internet at a fine-grained level. In this article, we propose a bandwidth allocation mechanism for differentiated services (DiffServ) on the streaming servers. It aims to deliver high bit rate streams to high priority requests without overcompromising low priority requests. We formulate the bandwidth allocation problem as optimization of a harmonic utility function of the stream quality factors and derive the optimal streaming bit rates for requests of different classes under various server load conditions. We prove that the optimal allocation scheme, referred to harmonic proportional allocation, not only maximizes the system utility function, but also guarantees proportional sharing between classes with different pre-specified differentiation weights. We evaluate the harmonic allocation scheme via extensive simulations and compare it with an absolute DiffServ strategy and a proportionalshare strategy tailored from relative DiffServ in networking and operating systems. Simulation results show that the optimal allocation can meet the objective of relative DiffServ in both short and long timescales and greatly enhance the service availability and maintain low queueing delay when the streaming server load is high.
A representation for describing and analyzing concerns in source code

Existing approaches that are available to help software developers locate and manage the scattered implementation of concerns use a representation based on lines of source code. Because they do not explicitly express program structure, concern representations based on source code have inherent limitations when finding, describing and analyzing concerns. To address these problems, we are trying to find a concern representation that captures as close as possible the amount and precision level of information that software developers need to efficiently plan a change, and that supports queries that can provide this information. As a proposed solution, we introduced the Concern Graph representation that abstracts the implementation details of a concern and makes explicit the relationships between different parts of the concern. The Concern Graph abstraction has also been designed to allow an obvious and expensive mapping back to the corresponding source code. To investigate the practical tradeoffs related to this approach, we have built the Feature Exploration and Analysis Tool (FEAT) that allows a developer to navigate over an extracted model of a Java program, to build up the subset of the model that corresponds to a concern of interest, and to analyze the relationships of that concern to the code base.
Anti-retroviral drug hepatotoxicity and risk factors in HIV Patients with or without Hepatitis B and C : a review

Physicians are treating patients infected with human immunodeficiency virus and/or hepatitis B or hepatitis C in their practice more often. Long-term complications of this diseases are multifactorial and can be related to the virus itself or to adverse effects of antiretroviral therapy. The liver is the main organ for regulating the internal environment of a body. There is no way to compensate for the loss of a liver function. It has major influences on the flow of nutrients as well as controlling carbohydrate, protein and fat metabolism. Drugs are significant in causing liver injury. More than nine hundred drugs, herbs and toxins have been documented as being hepatotoxic in line with different risk factors. The incidences of severe hepatic injury vary among different study cohorts as well as the differences in risk factors. Patients with a co-infection of HIV and Hepatitis B or C are at a risk of getting liver injury from antiretroviral drugs because the co-infections accelerate liver injury that may lead to cirrhosis or hepatocellular carcinoma. The risk of liver damage for those with a monoinfection of HIV alone is lower than in co-infections. This review explores risk factors for hepatotoxicity, its hepatotoxic antiretroviral drugs and the mechanisms of toxicity. It is meant to highlight the hepatotoxic potential of different antiretroviral drugs currently in use by HIV infected individuals.
Is home monitoring of international normalised ratio safer than clinic-based monitoring?

A best evidence topic was written according to a structured protocol, to answer the question: 'In patients taking warfarin, is home self-monitoring of international normalized ratio (INR) safer than clinic-based testing in reducing bleeding, thrombotic events and death? ' Altogether, 268 papers were found using the reported search. Five papers represented the highest level of evidence to answer the clinical question (four systematic reviews with meta-analysis and one meta-analysis). The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. The principal outcomes of interest were death, major haemorrhage, major thromboembolism, and time (or percentage time) spent within the therapeutic range, compared between self-monitoring/self-management and conventional management. Self-monitoring/self-management was associated with a significantly reduced risk of all-cause mortality of 26-42%. All meta-analyses reported on major thromboembolism, finding significant reductions in risk of ~50%. One meta-analysis found a 35% reduction in the risk of major haemorrhage, with the other four studies finding no significant difference. Only one study found self-monitoring/self-management to be associated with a significantly greater proportion of time within range, with another finding no significant difference in either the percentage of therapeutic results or in the time within range. The remaining two could not combine data for meta-analysis owing to methodological heterogeneity. We conclude that self-monitoring/self-management appears to be safer than conventional management. It is associated with consistently lower rates of thromboembolism and may also be associated with reduced risk of bleeding and death. This supports the updated guidance from the American College of Chest Physicians, recommending self-management of INR for patients who are both competent and motivated.
Whole-Genome Sequences of Two Rare Human Group C Rotavirus Strains Isolated from Two Cases of Acute Gastroenteritis

ABSTRACT This is a report of the complete genomic sequences of two rare group C rotavirus strains RVC/SZ94/CHN/2011 and RVC/SZ272/CHN/2011, isolated from two cases of acute gastroenteritis in Shenzhen, southern China, in 2011. These two strains display a close genetic relationship to 2007 Chinese strain YNR001 and 2008 Japanese strain BK0830.
Structure-Guided Design of Selective Epac1 and Epac2 Agonists

The second messenger cAMP is known to augment glucose-induced insulin secretion. However, its downstream targets in pancreatic β-cells have not been unequivocally determined. Therefore, we designed cAMP analogues by a structure-guided approach that act as Epac2-selective agonists both in vitro and in vivo. These analogues activate Epac2 about two orders of magnitude more potently than cAMP. The high potency arises from increased affinity as well as increased maximal activation. Crystallographic studies demonstrate that this is due to unique interactions. At least one of the Epac2-specific agonists, Sp-8-BnT-cAMPS (S-220), enhances glucose-induced insulin secretion in human pancreatic cells. Selective targeting of Epac2 is thus proven possible and may be an option in diabetes treatment.
The Distribution of Program Sizes and Its Implications: An Eclipse Case Study

A large software system is often composed of many inter-related programs of different sizes. Using the public Eclipse dataset, we replicate our previous study on the distribution of program sizes. Our results confirm that the program sizes follow the lognormal distribution. We also investigate the implications of the program size distribution on size estimation and quality predication. We find that the nature of size distribution can be used to estimate the size of a large Java system. We also find that a small percentage of largest programs account for a large percentage of defects, and the number of defects across programs follows the Weibull distribution when the programs are ranked by their sizes. Our results show that the distribution of program sizes is an important property for understanding large and complex software systems.
Killer cell immunoglobulin‐like receptors (KIR) of the African‐origin sabaeus monkey: evidence for recombination events in the evolution of KIR

Killer cell immunoglobulin (Ig)‐like receptors (KIR) were characterized in the West African sabaeus monkey (Chlorocebus sabaeus) to elucidate the mechanism by which diversity evolves in this family of molecules. Complementary DNA encoding four forms of KIR molecules, including KIR3DL, KIR2DL4, KIR2DL5, and KIR3DH forms, were identified in two unrelated sabaeus monkeys. A novel hybrid form showing features found in both KIR2DL5 and KIR3DH was also identified. Both the KIR3DL and KIR3DH forms from the sabaeus monkey were considerably more polymorphic than any KIR form identified in great apes or humans. The polymorphic residues of the three Ig‐like domains were frequently located in structural loops, indicating that point mutations have occurred in these regions. The three Ig‐like domains of the KIR3D forms of six primate species were found to have different patterns of clustering in phylogenetic trees, suggesting that each Ig‐like domain has a distinct phylogenetic history. This variation in relationships suggests that repeated recombination events have occurred between the Ig‐like domains during the evolution of the KIR family in primates. Recombination between individual Ig‐like domains, in addition to point mutations, provides a mechanism for generating the diversity of the KIR genes.
On the Memorability of System-generated PINs: Can Chunking Help?

To ensure that users do not choose weak personal identification numbers (PINs), many banks give out systemgenerated random PINs. 4-digit is the most commonly used PIN length, but 6-digit system-generated PINs are also becoming popular. The increased security we get from using system-generated PINs, however, comes at the cost of memorability. And while banks are increasingly adopting systemgenerated PINs, the impact on memorability of such PINs has not been studied. We conducted a large-scale online user study with 9,114 participants to investigate the impact of increased PIN length on the memorability of PINs, and whether number chunking 1 techniques (breaking a single number into multiple smaller numbers) can be applied to improve memorability for larger PIN lengths. As one would expect, our study shows that system-generated 4-digit PINs outperform 6-, 7-, and 8-digit PINs in long-term memorability. Interestingly, however, we find that there is no statistically significant difference in memorability between 6-, 7-, and 8-digit PINs, indicating that 7-, and 8-digit PINs should also be considered when looking to increase PIN length to 6-digits from currently common length of 4-digits for improved security. By grouping all 6-, 7-, and 8-digit chunked PINs together, and comparing them against a group of all non-chunked PINs, we find that chunking, overall, improves memorability of system-generated PINs. To our surprise, however, none of the individual chunking policies (e.g., 0000-00-00) showed statistically significant improvement over their peer non� Part of this work was done while Dr. Huh and Dr. Bobba were at the University of Illinois. 1 Note that our notion of chunking differs from the traditional notion in that we do not chunk numbers into semantically meaningful pieces.
Retinal Input Regulates the Timing of Corticogeniculate Innervation

Neurons in layer VI of visual cortex represent one of the largest sources of nonretinal input to the dorsal lateral geniculate nucleus (dLGN) and play a major role in modulating the gain of thalamic signal transmission. However, little is known about how and when these descending projections arrive and make functional connections with dLGN cells. Here we used a transgenic mouse to visualize corticogeniculate projections to examine the timing of cortical innervation in dLGN. Corticogeniculate innervation occurred at postnatal ages and was delayed compared with the arrival of retinal afferents. Cortical fibers began to enter dLGN at postnatal day 3 (P3) to P4, a time when retinogeniculate innervation is complete. However, cortical projections did not fully innervate dLGN until eye opening (P12), well after the time when retinal inputs from the two eyes segregate to form nonoverlapping eye-specific domains. In vitro thalamic slice recordings revealed that newly arriving cortical axons form functional connections with dLGN cells. However, adult-like responses that exhibited paired pulse facilitation did not fully emerge until 2 weeks of age. Finally, surgical or genetic elimination of retinal input greatly accelerated the rate of corticogeniculate innervation, with axons invading between P2 and P3 and fully innervating dLGN by P8 to P10. However, recordings in genetically deafferented mice showed that corticogeniculate synapses continued to mature at the same rate as controls. These studies suggest that retinal and cortical innervation of dLGN is highly coordinated and that input from retina plays an important role in regulating the rate of corticogeniculate innervation.
A high energy-efficiency SAR ADC based on partial floating capacitor switching technique

This paper presents a new successive approximation register (SAR) analog-to-digital converter (ADC) with partial floating capacitor switching (PFCS) scheme for low-power data converters. By interchanging the switching order of the largest capacitor with the second largest one the switching energy consumption can be efficiently reduced. From the mathematical calculation result, the proposed technique achieves 96.11% less switching energy compared to the conventional approach. The presented 10-bit PFCS-based SAR ADC is implemented in a CMOS 0.18-μm 1P6M technology. The power consumption of the presented prototype is only 7.16-μW with a sampling rate of 1-MS/s at a supply voltage of 0.9-V and a 21.56-fF/conversion-step figure of merit is achieved.
Fine-Grained Recognition via Attribute-Guided Attentive Feature Aggregation

Fine-grained object recognition is challenging due to large intra-class variation and inter-class ambiguity. A good algorithm should be able to: 1) discover discriminative local details and 2) align and aggregate these local discriminative patch-level features in an effective way to facilitate object level classification. Towards this end, we propose a novel local feature discovery, discriminative alignment and aggregation framework, inspired by the recent success of deep recurrent attention model. First, we develop a novel attribute-guided attentive network to sequentially discover informative parts/regions, by seeking a good registration between attentive regions and predefined object attributes. This could be considered as a semantic guided salient region discovery and alignment network, which might be more robust than conventional attention model. Second, these discovered regions are actively and progressively fed into a recurrent neural network, to yield the object-level representation. This could be considered as a discriminant aggregation network and informative patch-level features are propagated and accumulated to the deeper nodes of the recurrent network for final classification. We extensively test our framework on two fine-grained image benchmarks and the results demonstrate the effectiveness of the proposed framework.
Doped Lead Fluoride Chloride Crystals for the HHCAL Detector Concept

We report an investigation on pure and doped PbFCl crystals. Optical and scintillation properties, such as emission and transmittance spectra, light output and decay kinetics, were measured at different temperatures. Three emission bands at 2.27, 2.85 and 3.39 eV are observed at 77 K. Thermal quenching effect is also observed. While a scintillation component peaked at 400 nm with 3 ns decay time is observed in all PbFCl samples, a secondary emission at 550 nm with 360 ns decay time is found in the K doped PbFCl samples. The origins of three emission bands and further development of PbFCl for the HHCAL detector concept are discussed.
On SUSY standard-like models from orbifolds of D = 6 Gepner orientifolds

As a further elaboration of the proposal of ref. [1] we address the construction of Standard-like models from configurations of stacks of orientifold planes and D-branes on an internal space with the structure (Gepner model)c = 6 × T2/N. As a first step, the construction of D = 6 Type II B orientifolds on Gepner points, in the diagonal invariant case and for both, odd and even, affine levels is discussed. We build up the explicit expressions for B-type boundary states and crosscaps and obtain the amplitudes among them. From such amplitudes we read the corresponding spectra and the tadpole cancellation equations. Further compactification on a T2 torus, by simultaneously orbifolding the Gepner and the torus internal sectors, is performed. The embedding of the orbifold action in the brane sector breaks the original gauge groups and leads to = 1 supersymmetric chiral spectra. Whenever even orbifold action on the torus is considered, new branes, with worldvolume transverse to torus coordinates, must be included. The detailed rules for obtaining the D = 4 model spectra and tadpole equations are shown. As an illustration we present a 3 generations Left-Right symmetric model that can be further broken to a MSSM model.
Efficient Mining of Temporal High Utility Itemsets from Data streams

itemsets are considered as the different values of individual items as utilities, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets) -Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To our best knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer temporal high transaction-weighted utilization 2-itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with limited memory space, less candidate itemsets and CPU I/O time. This meets the critical requirements on time and space efficiency for mining data streams. The experimental results show that THUI-Mine can discover the temporal high utility itemsets with higher performance and less candidate itemsets compared to other algorithms under various experimental conditions.
The Economics of Beer

Beer has been consumed across the globe for centuries and was the drink of choice in many ancient societies. Today it is the most important alcoholic drink worldwide, in terms of volume and value. The largest brewing companies have developed into global multinationals, and the beer market has enjoyed strong growth in emerging economies, but there has been a substantial decline of beer consumption in traditional markets and a shift to new products. There is close interaction between governments and markets in the beer industry. For centuries, taxes on beer or its raw materials have been a major source of tax revenue and governments have regulated the beer industry for reasons related to quality, health, and competition. This book is the first economic analysis of the beer market and brewing industry. The introduction provides an economic history of beer, from monasteries in the early Middle Ages to the recent 'microbrewery movement', whilst other chapters consider whether people drink more beer during recessions, the effect of television on local breweries, and what makes a country a 'beer drinking' nation. It comprises a comprehensive and unique set of economic research and analysis on the economics of beer and brewing and covers economic history and development, supply and demand, trade and investment, geography and scale economies, technology and innovation, health and nutrition, quantity and quality, industrial organization and competition, taxation and regulation, and regional beer market developments. Contributors to this volume - William James Adams, University of Michigan Abhimanyu Arora, University of Leuven Junfei Bai, Chinese Academy of Sciences Anjor Bhaskar, Independent Researcher, India Matt Boswell, Stanford University and the Chinese Academy of Sciences Liesbeth Colen, University of Leuven Koen Deconinck, University of Leuven Kenneth G. Elzinga, University of Virginia Don Freeman, Sam Houston State University Lisa M. George, City University of New York Jikun Huang, Chinese Academy of Sciences Jill J. McCluskey, Washington State University Bart Minten, International Food Policy Research Institute (IFPRI) John Nye, George Mason University Damiaan Persyn, University of Leuven Eline Poelmans, Hogeschool Universiteit Brussel Scott Rozelle, Stanford University Sanatan Shreay, Amgen Corporation Margaret Slade, Universities of British Columbia and Warwick Johan F.M. Swinnen, University of Leuven Anthony Swisher, Akin Gump Strauss Hauer & Feld LLP Carol H. Tremblay, Oregon State University Victor J. Tremblay, Oregon State University Richard Unger, University of British Columbia Kristine Van Herck, University of Leuven Frank van Tongeren, OECD Trade and Agriculture Directorate Thijs Vandemoortele, University of Leuven Anneleen Vandeplas, University of Leuven Stijn Vanormelingen, IESE Business School