Miracle-dz/UniFAR-FTUs huggingface dataset:
Overexpression of a bHLH1 Transcription Factor of Pyrus ussuriensis Confers Enhanced Cold Tolerance and Increases Expression of Stress-Responsive Genes

The basic helix-loop-helix (bHLH) transcription factors are involved in arrays of physiological and biochemical processes. However, knowledge concerning the functions of bHLHs in cold tolerance remains poorly understood. In this study, a PubHLH1 gene isolated from Pyrus ussuriensis was characterized for its function in cold tolerance. PubHLH1 was upregulated by cold, salt, and dehydration, with the greatest induction under cold conditions. PubHLH1 had the transactivational activity and localized in the nucleus. Ectopic expression of PubHLH1 in transgenic tobacco conferred enhanced tolerance to cold stress. The transgenic lines had higher survival rates, higher chlorophyll, higher proline contents, lower electrolyte leakages and MDA when compared with wild type (WT). In addition, transcript levels of eight genes associated with ROS scavenging, regulation, and stress defense were higher in the transgenic plants relative to the WT under the chilling stress. Taken together, these results demonstrated that PubHLH1 played a key role in cold tolerance and, at least in part, contributed to activation of stress-responsive genes.
Estimating minimum streamflow from measurements at ungauged sites in regions with streamflow‐gauging networks

Estimation of low flows in rivers continues to be a vexing problem despite advances in statistical and process‐based hydrological models. We develop a method to estimate minimum streamflow at seasonal to annual timescales from measured streamflow based on regional similarity in the deviations of daily streamflow from minimum streamflow for a period of interest. The method is applied to 1,019 gauged sites in the Western United States for June to December 2015. The gauges were clustered into six regions with distinct timing and magnitude of low flows. A gamma distribution was fit each day to the deviations in specific discharge (daily streamflow divided by drainage area) from minimum specific discharge for gauges in each region. The Kolmogorov–Smirnov test identified days when the gamma distribution was adequate to represent the distribution of deviations in a region. The performance of the gamma distribution was evaluated at gauges by comparing daily estimates of minimum streamflow with estimates from area‐based regression relations for minimum streamflow. Each region had at least 8 days during the period when streamflow measurements would provide better estimates than the regional regression equation, but the number of such days varied by region depending on aridity and homogeneity of streamflow within the region. Synoptic streamflow measurements at ungauged sites have value for estimating minimum streamflow and improving the spatial resolution of hydrological model in regions with streamflow‐gauging networks.
Personalized Pricing and Quality Customization

We embed the principal–agent model in a model of spatial differentiation with correlated consumer preferences to investigate the competitive implications of personalized pricing and quality allocation (PPQ), whereby duopoly firms charge different prices and offer different qualities to different consumers, based on their willingness to pay. Our model sheds light on the equilibrium product-line pricing and quality schedules offered by firms, given that none, one, or both firms implement PPQ. The adoption of PPQ has three effects in our model: it enables firms to extract higher rents from loyal customers, intensifies price competition for nonloyal customers, and eliminates cannibalization from customer self-selection. Contrary to prior literature on one-to-one marketing and price discrimination, we show that even symmetric firms can avoid the well-known Prisoner's Dilemma problem when they engage in personalized pricing and quality customization. When both firms have PPQ, consumer surplus is nonmonotonic in valuations such that some low-valuation consumers get higher surplus than high-valuation consumers. The adoption of PPQ can reduce information asymmetry, and therefore sellers offer higher-quality products after the adoption of PPQ. Overall, we find that while the simultaneous adoption of PPQ generally improves total social welfare and firm profits, it decreases total consumer surplus.
An accelerated randomized Kaczmarz method via low-rank approximation

The Kaczmarz method for finding the solution to an overdetermined consistent system of linear equation Ax=b(A∈Rm×n) is an iterative algorithm that has found many applications ranging from computer tomography to digital signal processing. Recently, Strohmer and Vershynin proposed randomized Kaczmarz, and proved its exponential convergence. In this paper, motivated by idea of precondition, we present a modified version of the randomized Kaczmarz method where an orthogonal matrix was multiplied to both sides of the equation Ax=b, and the orthogonal matrix is obtained by low-rank approximation. Our approach fits the problem when m is huge and m≫n. Theoretically, we improve the convergence rate of the randomized Kaczmarz method. The numerical results show that our approach is faster than the standard randomized Kaczmarz.
THE BUSINESS PERSPECTIVE OF CLOUD COMPUTING: ACTORS, ROLES, AND VALUE NETWORKS

With the rise of a ubiquitous provision of computing resources over the past years, cloud computing has been established as a prominent research topic. Many researchers, however, focus exclusively on the technical aspects of cloud computing, thereby neglecting the business opportunities and potentials cloud computing can offer. Enabled through this technology, new market players and business value networks arise and break up the traditional value chain of service provision. The focus of this paper therefore lies on the business aspects of cloud computing. Its main contribution - besides elaborating on a comprehensive definition of cloud computing - is the compilation of cloud actors and their roles within a new cloud computing value network and a conceptual development of a generic value network of different actors in cloud computing. Extending the prevailing technical perspective of cloud computing, this paper shifts the focus from an exclusive technological perspective to a broader understanding of business opportunities and business value.
DDE: dynamic data structure excavation

Dynamic Datastructure Excavation (DDE) is a new approach to extract datastructures from C binaries without any need for debugging symbols. Unlike most existing tools, DDE uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory. Its results are much more accurate than those of previous methods.
Robust Reinforcement Learning

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both off-line learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H control, we consider a differential game in which a disturbing agent tries to make the worst possible disturbance while a control agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.
Enhanced Methodology and Tools for Exploring Domain-Specific Coarse-Grained FPGAs

This paper presents a new environment for the exploration of domain-specific coarse-grained FPGAs. An architecture description mechanism is used to define a coarse-grained architecture. A software flow is used to map a netlist on the defined architecture. The software flow not only maps the instances of a target netlist on their respective blocks in the architecture, but also refines the position of the blocks on the architecture. This environment can also be used to define and optimize a domain-specific architecture for a set of netlists to be mapped on it at mutually exclusive times. A set of DSP test-benches are used to show the effectiveness of various techniques used in this work.
MSKT-ORAM : Multi-server Kary Tree Oblivious RAM without Homomorphic Encryption

This paper proposes MSKT-ORAM, an efficient multiple server ORAM construction, to protect a client’s access pattern to outsourced data. MSKT-ORAM organizes each of the server storage as a k-ary tree and adopts XOR based PIR and a novel delayed eviction technique to optimize both the data query and data eviction process. MSKT-ORAM is proved to protect the data access pattern privacy at a failure probability of 2−80 when k ≥ 128. Meanwhile, given constant local storage, when N (i.e., the total number of outsourced data blocks) ranges from 2 to 2 and data block size B ≥ 20 KB, the communication cost of MSKT-ORAM is only 22 to 46 data blocks. Asymptotical analysis and detailed implementation comparisons are conducted to show that MSKT-ORAM achieves better communication, storage and access delay in practical scenario over the compared state-of-the-art ORAM schemes.
COMBINING p-VALUES : A DEFINITIVE PROCESS 4

summary In the Bayes approach the model-data summary is arguably the observed likelihood function; in the frequentist approach it is arguably a p-value function assessing a least squares or maximum-likelihood departure; and in the higher-order likelihood approach it is the observed likelihood together with a canonical reparameterization. For likelihood the obvious method of combining is to add log-likelihoods from independent sources: this is in the nature of likelihood itself and is also an implicit Bayes imperative as only likelihood is used in the Bayesian argument. For the familiar frequentist approach the combining of p-values is often ad hoc: we discuss first a Fisher proposal and then offer a likelihood based alternative. For the higher order likelihood approach the combining begins with the standard summary, which is likelihood plus a canonical reparameterization: we develop the appropriate higher order combining procedure. For the p-value summary, Fisher (1973) proposed a quick and easy method for combining p-values from independent investigations: multiply them together and use chi-square tables. The proposal received criticism that it did not address power and other conventional criteria, but Fisher had assumed quite clearly that such related information was unavailable. We use first order likelihood theory to derive a simple modification: the p-values are converted to likelihood values and the likelihood values to observed likelihood functions and these in turn to a new composite p-value. Higher order likelihood offers further refinement: use the standard summary involving the log-likelihood (θ) and the canonical reparameterization ϕ(θ); the combining from independent investigations then amounts to adding the observed log-likelihoods i(θ) and weighting and adding the reparameterizations ϕi(θ). We develop this information combining procedure: add log-likelihoods and suitably weight and add canonical parameters. Some examples follow.
Using Connectionist Models to Evaluate Examinees' Response Patterns on

The purpose of this study is to describe how the attribute hierarchy method (AHM) can be applied to assessment engineering. The AHM is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive test specifications. These specifications identify the examinees’ attribute patterns which are used as output for the neural network. The purpose of the statistical pattern recognition analysis is to estimate the probability that an examinee possess specific attribute combinations based on their observed item response patterns. Two examples using student response data from a sample of algebra items on the SAT illustrate our pattern recognition approach.
Security function virtualization in software defined infrastructure

In this paper we present an approach to implement security as a Virtualized Network Function (VNF) that is implemented within a Software-Defined Infrastructure (SDI). We present a scalable, flexible, and seamless design for a Deep Packet Inspection (DPI) system for network intrusion detection and prevention. We discuss how our design introduces significant reductions in both capital and operational expenses (CAPEX and OPEX). As proof of concept, we describe an implementation for a modular security solution that uses the SAVI SDI testbed to first detect and then block an attack or to re-direct it to a honey-pot for further analysis. We discuss our testing methodology and provide measurement results for the test cases where an application faces various security attacks.
Reliable Measures for Aligning Japanese-English News Articles and Sentences

We have aligned Japanese and English news articles and sentences to make a large parallel corpus. We first used a method based on cross-language information retrieval (CLIR) to align the Japanese and English articles and then used a method based on dynamic programming (DP) matching to align the Japanese and English sentences in these articles. However, the results included many incorrect alignments. To remove these, we propose two measures (scores) that evaluate the validity of alignments. The measure for article alignment uses similarities in sentences aligned by DP matching and that for sentence alignment uses similarities in articles aligned by CLIR. They enhance each other to improve the accuracy of alignment. Using these measures, we have successfully constructed a large-scale article and sentence alignment corpus available to the public.
Lateral motion of beta receptors in membranes of cultured liver cells.

We have studied the lateral mobility and distribution of beta receptors on Chang human liver cells by fluorescence photobleaching recovery and video intensification microscopy. The beta receptors were labeled with the fluorescent antagonist 7-(2-allylphenoxy)-2,2-dimethyl-6-hydroxy-1-(4-nitrobenzo-2-oxa-1,3-diazolyl)-1 ,4-diazaheptane (Alp-NBD). Sixty to 75% of the staining was specific (displaceable by unlabeled antagonists). Most of the antagonist-occupied beta receptors were immobile, because only 15-25% of their fluorescence recovered on the experimental time scale at 23 degrees C. This immobility correlates with the clustered distribution of Alp-NBD--beta-receptor complexes at 4 degrees C and 37 degrees C. The beta receptors appear to be aggregated prior to antagonist binding, because visible patches were observed immediately after labeling for 30 sec at 4 degrees C. Preincubation at 37 degrees C with (--)-isoproterenol, a beta agonist, prior to Alp-NBD labeling induced a time-dependent release of the beta receptors to a more homogeneous distribution and increased the mobile fraction to 70-80% (lateral diffusion coefficient = 1.4 X 10(-9) cm2/sec at 23 degrees C). This is not due to an effect on membrane fluidity, because the diffusion coefficient of a lipid probe was not altered. The time course of agonist-induced beta-receptor mobilization correlates with receptor loss and adenylate cyclase desensitization but is much slower than adenylate cyclase activation. This indicates that adenylate cyclase activation by beta receptors does not require macroscopic lateral mobility of the majority of the beta receptors.
A supply voltage-dependent variation aware reliability evaluation model

With the continuous scaling of CMOS VLSI technology well into the nano-meter regime, and the increasing demand for ultra low power/low voltage circuits and systems, reliability is becoming an extra design optimisation goal in addition to size, performance, and energy. In this paper, a supply voltage (Vdd-) dependent, transistor threshold voltage variation aware propagation delay estimation model and a comprehensive statistical model to evaluate the reliability of the VLSI circuits is proposed. This accurate Vdd-dependent reliability evaluation model can be applied in the process of reliability driven multi-objective optimisation, which deals with tradeoffs between reliability, area, performance and energy. The experimental results show that the average estimation error is within 3% when compared to Monte-Carlo SPICE simulation while saving runtime by at least 100 times for generic benchmark circuits.
Dihedral Butterfly Digraph and Its Cayley Graph Representation

In this paper, we present a new extension of the butterfly digraph, which is known as one of the topologies used for interconnection networks. The butterfly digraph was previously generalized from binary to d-ary. We define a new digraph by adding a signed label to each vertex of the d-ary butterfly digraph. We call this digraph the dihedral butterfly digraph and study its properties. Furthermore, we show that this digraph can be represented as a Cayley graph. It is well known that a butterfly digraph can be represented as a Cayley graph on the wreath product of two cyclic groups [1]. We prove that a dihedral butterfly digraph can be represented as a Cayley graph in two ways.
Betweenness centrality in dense random geometric networks

Random geometric networks are mathematical structures consisting of a set of nodes placed randomly within a bounded set V ⊆ ℝd mutually coupled with a probability dependent on their Euclidean separation, and are the classic model used within the expanding field of ad hoc wireless networks. In order to rank the importance of the network's communicating nodes, we consider the well established `betweenness' centrality measure (quantifying how often a node is on a shortest path of links between any pair of nodes), providing an analytic treatment of betweenness within a random graph model by deriving a closed form expression for the expected betweenness of a node placed within a dense random geometric network formed inside a disk of radius R. We confirm this with numerical simulations, and discuss the importance of the formula for mitigating the `boundary effect' connectivity phenomenon, for cluster head node election protocol design and for detecting the location of a network's `vulnerability backbone'.
On a class of 2-surface observables in general relativity

The boundary conditions for canonical vacuum general relativity are investigated at the quasi-local level. It is shown that fixing the area element on the 2-surface (rather than the induced 2-metric) is enough to have a well-defined constraint algebra, and a well-defined Poisson algebra of basic Hamiltonians parametrized by shifts that are tangent to and divergence free on . The evolution equations preserve these boundary conditions, and the value of the basic Hamiltonians gives 2+2-covariant, gauge-invariant 2-surface observables. The meaning of these observables is also discussed.
Coronary Artery Spatial Distribution of Acute Myocardial Infarction Occlusions

Background—Acute coronary occlusions leading to ST-segment elevation myocardial infarctions (STEMIs) are due primarily to rupture of atherosclerotic plaques. Present “vulnerable plaque” detection technology focuses on identifying individual plaques with no clear therapeutic plan beyond conventional risk factor reduction. We developed a spatial map of the distribution of acute coronary occlusions to test our hypothesis that plaque ruptures do not occur uniformly throughout the coronary tree. Methods and Results—We analyzed 208 consecutive patients who presented to the Brigham and Women’s Hospital with STEMI and mapped the location of the acute coronary occlusion. These occlusions were not uniformly distributed throughout each of the major epicardial coronary arteries but tended to cluster within the proximal third of each of the vessels (right coronary artery, P =0.001; left anterior descending artery, P =0.003; left circumflex artery, P =0.001). Furthermore, Poisson regression showed that for each 10-mm increase in distance from the ostium, the risk of an acute coronary occlusion was significantly decreased by 13% in the right coronary artery, 30% in the left anterior descending artery, and 26% in the left circumflex artery. Conclusions—Acute coronary occlusions leading to STEMI tend to cluster in predictable “hot spots” within the proximal third of the coronary arteries. Identification of these high-risk zones for acute coronary occlusions will lead to future advances in vulnerable plaque detection technology and potentially locally directed preventive strategies.
An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising

In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique ( DelL\BBR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelL\BBR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number ( ~ 12) of electrodes is available.
Using Extended Multidimensional Pattern Relation for Multidimensional On-line Mining

Although incremental data mining and online mining approaches are rather efficient, they usually can not flexibly obtain association rules or patterns from portions of data, diversely consider problems at different aspects, and provide on-line decision supports for users. In the past, we thus developed the multidimensional on-line mining approach to select and integrate related mining information for diverse mining requests. It uses the multidimensional pattern relation to structurally and systematically store the additional context information and mining information for each inserted block of data. It may, however, get loose upper-bound supports of candidate itemsets and thus cause datasets to be frequently re-processed for new mining requests. In this paper, we attempt to apply the concept of negative border to enlarge the mining information in the multidimensional pattern relation to help get tight upper-bound supports of candidate itemsets, and thus reduce the number of candidate itemsets to be considered. Based on the extended multidimensional pattern relation, a corresponding online mining approach is proposed.