Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosted Varying-Coefficient Regression Models for Product Demand Prediction

Estimating the aggregated market demand for a product in a dynamic market is critical to manufacturers and retailers. Motivated by the need for a statistical demand prediction model for laptop pricing at Hewlett-Packard, we have developed a novel boosting-based varying-coefficient regression model. The developed model uses regression trees as the base learner, and is generally applicable to varying-coefficient models with a large number of mixed-type varying-coefficient variables, which proves to be challenging for conventional nonparametric smoothing methods. The proposed method works well in both predicting the response and estimating the coefficient surface, based on a simulation study. Finally, we have applied this methodology to real-world mobile computer sales data, and demonstrated its superiority by comparing with elastic net- and kernel regression-based varying-coefficient model. Computer codes for boosted varying-coefficient regression are available online as supplementary materials.
Low pH-Induced Changes of Antioxidant Enzyme and ATPase Activities in the Roots of Rice (Oryza sativa L.) Seedlings

Soil acidification is the main problem in the current rice production. Here, the effects of low pH on the root growth, reactive oxygen species metabolism, plasma membrane functions, and the transcript levels of the related genes were investigated in rice seedlings (Oryza sativa L.) in a hydroponic system at pH 3.5, 4.5, and 5.5. There were two hybrid rice cultivars in this trial, including Yongyou 12 (YY12, a japonica hybrid) and Zhongzheyou 1 (ZZY1, an indica hybrid). Higher H+ activity markedly decreased root length, the proportion of fine roots, and dry matter production, but induced a significant accumulation of hydrogen peroxide (H2O2), and led to serious lipid peroxidation in the roots of the two varieties. The transcript levels of copper/zinc superoxide dismutase 1 (Cu/Zn SOD1), copper/zinc superoxide dismutase 2 (Cu/Zn SOD2), catalase A (CATA) and catalase B (CATB) genes in YY12 and ZZY1 roots were significantly down-regulated after low pH exposure for two weeks. Meanwhile, a significant decrease was observed in the expression of the P-type Ca2+-ATPases in roots at pH 3.5. The activities of antioxidant enzymes (SOD, CAT) and plasma membrane (PM) Ca2+-ATPase in the two varieties were dramatically inhibited by strong rhizosphere acidification. However, the expression levels of ascorbate peroxidase 1 (APX1) and PM H+-ATPase isoform 7 were up-regulated under H+ stress compared with the control. Significantly higher activities of APX and PM H+-ATPase could contribute to the adaptation of rice roots to low pH.
Vertical Aluminophosphate Molecular Sieve Crystals Grown at Inorganic-Organic Interfaces

Tubular aluminophosphate molecular sieve crystals were grown at an organic interface with their channels (7 angstroms in cross section) vertical to the substrate. To induce surface nucleation and oriented growth of AIPO4-5 crystals, organophosphonate layers cross-linked with Zr(IV) were assembled on a gold substrate and the modified substrate was immersed in a hydrothermal bath containing reagents for the synthesis of the molecular sieve. Reflection-absorption infrared studies demonstrated the stability of the phosphonate layers under these conditions. Drastic changes in the morphology of the surface-grown crystals from spherical agglomerates to vertical needles to thin tilted needles could be achieved by adjusting the water content of the synthesis bath. Nitrogen sorption in these structures on a piezoelectric device confirmed the presence of zeolitic microporosity.
The role of self-compassion in the well-being of self-identifying gay men

ABSTRACT The psychological literature has identified many benefits of self-compassion. In an effort to explore self-compassion as a potential resource for gay men, we explored relationships between self-compassion, well-being, self-esteem, pride (authentic and hubristic), attachment, and two minority-specific processes, outness and internalized heterosexism. A sample of predominantly White, self-identifying gay men (N = 139) completed an online survey consisting of demographic variables and questionnaires related to these constructs. Authentic pride, self-esteem, self-compassion, and outness were significantly positively related to well-being. Avoidant and anxious attachment styles and internalized heterosexism were significantly negatively related to well-being. No relationship was found between hubristic pride and self-compassion, or well-being. Multiple linear regression analysis revealed that the self-compassion components of self-kindness and isolation, the gay affirmation component of internalized heterosexism, outness, and the avoidance and anxiety components of attachment were significant predictors of general well-being. Results suggest that, in addition to developing an authentic sense of pride, self-compassion may be beneficial in cultivating well-being in gay men.
Towards Optimized Distribution Protection Design

Traditionally, distribution systems engineering has focused on simplified design criteria guided by individual utility practices and experience. These designs were not optimized but did satisfy specific regulatory goals for system performance. Two developments are invalidating this approach: deregulation of the industry and rapid improvements in technology. Deregulation forces utilities to more carefully consider the cost of providing reliable service and to be concerned with the customer cost of reliability. At the same time, microprocessor based devices have greatly expanded the capabilities of protection apparatus. Further, low cost high speed computers have rendered computer aided design tools feasible. In this paper, distribution feeder protection is formulated as a reliability problem. Distribution reliability indices appropriate for protection design are discussed. Indices which are impacted by protection design are identified. The authors propose a new approach to optimizing overall distribution system protection design.
Expanded Version of a Paper from the Sixth Working Conference on Dependable Computing for Critical Applications, Systematic Formal Veriication for Fault-tolerant Time-triggered Algorithms

Many critical real-time applications are implemented as time-triggered systems. We present a systematic way to derive a time-triggered implementation from a fault-tolerant algorithm speciied as a functional program. It is relatively easy to formally and mechanically verify correctness and fault-tolerance properties of algorithms expressed in this latter form. The functional program is next transformed into an untimed synchronous system, and then to a time-triggered implementation. The second step is independent of the algorithm concerned and we prove its correctness; the proof has also been formalized and mechanically checked with the PVS veriication system. This approach provides a methodology that can ease the formal speciication and assurance of critical fault-tolerant systems.
A Data Hiding Scheme with High Embedding Capacity Based on General Improving Exploiting Modification Direction Method

Data hiding is one of the most important strategies on the field of data security; the main purpose of data hiding is to disguise the secret information and put them behind a cover image to make them unnoticeable and to assure the secrecy as a stego-image. Besides safety, the quantity of data that can be hidden in a single cover image is also very important to the data encryption. In this paper, we propose two kinds of weight-changing evaluation of high capacity EMD data hiding strategies that formulas can be open to the public; one of them is using table-checking to change the weighting evaluation of high capacity EMD data hiding strategy, the other is using generalize of high capacity EMD data hiding; both strategies can improve the safety problem when opening the formula to the public.
Multiple mobile robots formation control and obstacle avoidance

The Leader/Follower formation control of mobile robots with uncertain dynamical models is investigated in this paper. A combined kinematic/torque control law is developed for the follower using backstepping in order to accommodate the dynamics both of the follower and the leader in contrast with kinematic-based formation controllers. Neural network is introduced to approximate the dynamics of the follower as well as its leader using online weight tuning. In order to allow the follower to navigate around obstacles while simultaneously tracking its leader, an obstacle avoidance scheme is introduced. It is shown using Lyapunov theory that the formation tracking errors for the follower are uniformly ultimately bounded, and numerical results are provided.
An adaptive fingerprint image segmentation algorithm based on multiple features

Fingerprint image segmentation heavily influences the performance of fingerprint verification systems. In recent years, some new methods have been introduced to the image segmenting processing in order to get better disposal results. And most of the proposed algorithms are based on threshold segmentation. In this paper, a novel approach is put forward to segment fingerprint images based on multiple features. It makes use of local and global features to determine block threshold adaptively without the experience. Meanwhile, the algorithm combines the point level segmentation with the block level segmentation in order that it could be capable of avoiding the appearance of the blocking effect of the foreground image edges at a low computational cost. The performance of the new algorithm is evaluated on FVC2004 database. Experiment results show that the proposed adaptive segmentation algorithm is effective and robust.
Nonalcoholic Fatty Liver: A Possible New Target for Type 2 Diabetes Prevention and Treatment

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder worldwide. Several lines of evidence have indicated a pathogenic role of insulin resistance, and a strong association with type 2 diabetes (T2MD) and metabolic syndrome. Importantly, NAFLD appears to enhance the risk for T2MD, as well as worsen glycemic control and cardiovascular disease in diabetic patients. In turn, T2MD may promote NAFLD progression. The opportunity to take into account NAFLD in T2MD prevention and care has stimulated several clinical studies in which antidiabetic drugs, such as metformin, thiazolidinediones, GLP-1 analogues and DPP-4 inhibitors have been evaluated in NAFLD patients. In this review, we provide an overview of preclinical and clinical evidences on the possible efficacy of antidiabetic drugs in NAFLD treatment. Overall, available data suggest that metformin has beneficial effects on body weight reduction and metabolic parameters, with uncertain effects on liver histology, while pioglitazone may improve liver histology. Few data, mostly preclinical, are available on DPP4 inhibitors and GLP-1 analogues. The heterogeneity of these studies and the small number of patients do not allow for firm conclusions about treatment guidelines, and further randomized, controlled studies are needed.
A feature selection method for malware detection

Due to the serious network security problems in recent years, a large number of malware features have been emerged, which leads to increasing time-complexity and space-consumption for malware detection systems. Moreover, irrelevant and redundant features may decrease the detection rate. Feature selection, as an important data mining phase and technology, can effectively reduce the redundant and irrelevant features in the original large feature space, thereby can increase the detection rate and reduce the false positive rate for malware detection model. This paper proposes a class driven correlation based on feature selection method, which can select corresponding features for different classes of data respectively. Then this method uses correlation based feature selection method to eliminating redundant features. Experimental results indicate that the approach can not only reduce the complexity of malware detection system, but also increase the detection rate as compared to other methods.
ROBUST BLOCK COORDINATE DESCENT May 8, 2015

In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill conditioned problems. We call the method Robust Coordinate Descent (RCD). At each iteration of RCD, a block of coordinates is sampled randomly, a quadratic model is formed about that block and the model is minimized approximately/inexactly to determine the search direction. An inexpensive line search is then employed to ensure a monotonic decrease in the objective function and acceptance of large step sizes. We prove global convergence of the RCD algorithm, and we also present several results on the local convergence of RCD for strongly convex functions. Finally, we present numerical results on large-scale problems to demonstrate the practical performance of the method.
HECKE CORRESPONDENCES AND FEYNMAN GRAPHS

We consider natural representations of the Connes-Kreimer Lie algebras on rooted trees/Feynman graphs arising from Hecke correspondences in the categories LRF ,LFG constructed by K. Kremnizer and the author. We thus obtain the insertion/elimination representations constructed by Connes-Kreimer as well as an isomorphic pair we term top-insertion/top-elimination. We also construct graded finite-dimensional sub/quotient representations of these arising from ”truncated” correspondences.
China's Monetary Policy in the Context of Financial Crisis

This paper focuses on China’s monetary policy in the context of financial crisis. Empirical Mode Decomposition (EMD) method is used to extract the influencing factors of M2 and analyze their effects. We construct a VAR model to calculate the effects of monetary policy in the context of financial crisis since September 2008, and compare with that in the Asian financial crisis. The results show that the expansionary monetary policies eliminate the negative effects of prior period monetary policies and influences of the financial crisis. These policies avoid a further decline in China's economy in time. The dramatically enhanced positive effects of monetary policy in the short-run may negatively impact economy by possible inflation expectations. The proposed subsequent monetary policy should be fine-tuned for inflation.
Positive forward rates in the maximum smoothness framework

In this paper we present a nonlinear dynamic programming algorithm for the computation of forward rates within the maximum smoothness framework. The algorithm implements the forward rate positivity constraint for a one-parametric family of smoothness measures and it handles price spreads in the constraining data set. We investigate the outcome of the algorithm using the Swedish Bond market showing examples where the absence of the positive constraint leads to negative interest rates. Furthermore we investigate the predictive accuracy of the algorithm as we move along the family of smoothness measures. Among other things we observe that the inclusion of spreads not only improves the smoothness of forward curves but also significantly reduces the predictive error.
Implications of activity-based costing/management for decision-making in order management

Activity-based costing and management is an accounting and management approach for determining accurate costs especially the overhead costs. Activity-Based Costing/Management (ABC/M) can overcome some of the limitations of traditional cost accounting for decision-making in order management. The combination of ABC in orders management decision-making models of can improve the quality of decision. Most of order management decision support models only consider material flow and capacity constraints and don’t consider the profitability factor. This paper proposes multi objective mixed integer programming model to could take into account profitability for managing order decisions effectively, subject to capacity constraints by using ABC. Illustrative example shows that the proposed model satisfies a favorite quantity of orders completely and accepts a selective number of orders partially by increasing the profitability and minimizing residual capacity simultaneously .
Myocardium segmentation using a priori knowledge of shape and a spatial relation

We propose a new method to segment the myocardium based on Active Shape Model - ASM and a spatial distance relation. The main idea is to take advantage from statistical a priori knowledge of the shape that exists in ASM, in order to model the shape of both structures to be segmented, and integrate a new a priori knowledge about the variation of a spatial distance relation between them. This knowledge is estimated during a training step, then, the obtained models are used to guide the segmentation process. The proposed method is applied to endocardial and epicardial contours segmentation in axial CT scan slices of the heart. The obtained results are encouraging and show the performance of the proposed method.
Formal Models for Analysing Dynamic Adaptation Behaviour in Real-Time Systems

Self-adaptive systems are able to autonomously adapt themselves to react to dynamic changes in their environment. They, thereby, provide suitable mechanisms to deal with uncertain environment settings, as is required in modern reactive systems, such as cyber-physical systems. However, design and analysis of adaptation logic is complex and error-prone. Thus, early design-time analysis of the adaptation logic is necessary, especially in safety-critical applications. In this paper, we cope with the problem of comprehensively analysing time-dependent self-adaptive systems. We consider rule-based adaptation as a generic mechanism to describe adaptation logic. We automatically extract formal timed models of the functional components from a SystemC system-level implementation. This ensures that analysis results on the models correspond to the actual running system. To analyse the adaptation behaviours, we embed the extracted functional models in a formal, generic, and abstract MAPE-K loop modelled with timed automata. We classify important adaptation properties and show how they can be generally verified on the resulting models together with an abstract model of the environment, which we assume to be given. To evaluate our approach, we analyse the widely used web-based information system Znn.com.
XRCC4's interaction with XLF is required for coding (but not signal) end joining

XRCC4 and XLF are structurally related proteins important for DNA Ligase IV function. XRCC4 forms a tight complex with DNA Ligase IV while XLF interacts directly with XRCC4. Both XRCC4 and XLF form homodimers that can polymerize as heterotypic filaments independently of DNA Ligase IV. Emerging structural and in vitro biochemical data suggest that XRCC4 and XLF together generate a filamentous structure that promotes bridging between DNA molecules. Here, we show that ablating XRCC4's affinity for XLF results in DNA repair deficits including a surprising deficit in VDJ coding, but not signal end joining. These data are consistent with a model whereby XRCC4/XLF complexes hold DNA ends together—stringently required for coding end joining, but dispensable for signal end joining. Finally, DNA-PK phosphorylation of XRCC4/XLF complexes disrupt DNA bridging in vitro, suggesting a regulatory role for DNA-PK's phosphorylation of XRCC4/XLF complexes.
An explicit statistical model of learning lexical segmentation using multiple cues

This paper presents an unsupervised and incremental model of learning segmentation that combines multiple cues whose use by children and adults were attested by experimental studies. The cues we exploit in this study are predictability statistics, phonotactics, lexical stress and partial lexical information. The performance of the model presented in this paper is competitive with the state-of-the-art segmentation models in the literature, while following the child language acquisition more faithfully. Besides the performance improvements over the similar models in the literature, the cues are combined in an explicit manner, allowing easier interpretation of what the model learns.
Urea Amidolyase (DUR1,2) Contributes to Virulence and Kidney Pathogenesis of Candida albicans

The intracellular enzyme urea amidolyase (Dur1,2p) enables C. albicans to utilize urea as a sole nitrogen source. Because deletion of the DUR1,2 gene reduces survival of C. albicans co-cultured with a murine macrophage cell line, we investigated the role of Dur1,2p in pathogenesis using a mouse model of disseminated candidiasis. A dur1,2Δ/dur1,2Δ strain was significantly less virulent than the wild-type strain, showing significantly higher survival rate, better renal function, and decreased and less sustained fungal colonization in kidney and brain. Complementation of the mutant restored virulence. DUR1,2 deletion resulted in a milder host inflammatory reaction. Immunohistochemistry, flow cytometry, and magnetic resonance imaging showed decreased phagocytic infiltration into infected kidneys. Systemic cytokine levels of wild-type mice infected with the dur1,2 mutant showed a more balanced systemic pro-inflammatory cytokine response. Host gene expression and protein analysis in infected kidneys revealed parallel changes in the local immune response. Significant differences were observed in the kidney IL-1 inflammatory pathway, IL-15 signaling, MAP kinase signaling, and the alternative complement pathway. We conclude that Dur1,2p is important for kidney colonization during disseminated candidiasis and contributes to an unbalanced host inflammatory response and subsequent renal failure. Therefore, this Candida-specific enzyme may represent a useful drug target to protect the host from kidney damage associated with disseminated candidiasis.
Impacts of Computer-Mediated Communications on Social Behaviors of MSN and Facebook Users

Internet has changed how people communicate with the others; however, few studies have investigated the relationship between people’s new communication behaviors and their innermost feelings. Therefore, this study aims to how people choose communication tool to interact with the others under the impacts of Computer-Mediated-Communication (CMC). This study refers theories of communication and social psychology to explore the impacts of Computer-Mediated-Communication (CMC) on Facebook/MSN’s users’ social behaviors in terms of MSN Messenger and Facebook are the most popular online communication tools worldwide. The following facts were found: 1. Users start using Facebook mainly effected by peers and environment. 2. Users tend to choose MSN as a tool to communicate with others when the message is positive; however, they prefer to use Facebook to deal with messages which is negative. 3. Facebook’s function such as "share", allowing users to share real-time restaurants or attractions etc., which makes online world associate to real life, increasing social interactions among people. 4. Most users change their "MSN nickname Personal status" seldom, as most users get friends’ latest status via Facebook. Besides, interviewees said that the messages they posted on the "MSN nickname Personal status" is simply for their selfawareness, not for friends, so that many words presented in short.