Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Choice Applied to Consumer Preferences

Standard model assessment techniques such as residual plots or Akaike’s information criterion can be difficult to use or provide limited insight into model fit when applied in non-standard regression contexts such as random effects or mixture models. This paper considers the application of Bayesian ideas to model choice for a large consumer preference dataset where non-standard models appear to be needed but it is unclear which model is most appropriate. In addition, it is shown that suitably chosen graphical methods can provide insights into which of a set of competing models is most useful for different subsets of the data.
Up-Regulation of Nerve Growth Factor in Cholestatic Livers and Its Hepatoprotective Role against Oxidative Stress

The role of nerve growth factor (NGF) in liver injury induced by bile duct ligation (BDL) remains elusive. This study aimed to investigate the relationship between inflammation and hepatic NGF expression, to explore the possible upstream molecules up-regulating NGF, and to determine whether NGF could protect hepatocytes from oxidative liver injury. Biochemical and molecular detection showed that NGF was up-regulated in cholestatic livers and plasma, and well correlated with systemic and hepatic inflammation. Conversely, systemic immunosuppression reduced serum NGF levels and resulted in higher mortality in BDL-treated mice. Immunohistochemistry showed that the up-regulated NGF was mainly localized in parenchymal hepatocytes. In vitro mechanistic study further demonstrated that TGF-β1 up-regulated NGF expression in clone-9 and primary rat hepatocytes. Exogenous NGF supplementation and endogenous NGF overexpression effectively protected hepatocytes against TGF-β1- and oxidative stress-induced cell death in vitro, along with reduced formation of oxidative adducted proteins modified by 4-HNE and 8-OHdG. TUNEL staining confirmed the involvement of anti-apoptosis in the NGF-exhibited hepatoprotection. Moreover, NGF potently induced Akt phosphorylation and increased Bcl-2 to Bax ratios, whereas these molecular alterations by NGF were only seen in the H2O2-, but not TGF-β1-treated hepatocytes. In conclusion, NGF exhibits anti-oxidative and hepatoprotective effects and is suggested to be therapeutically applicable in treating cholestatic liver diseases.
Joint Charge and Thermal Management for Batteries in Portable Systems With Hybrid Power Sources

This paper introduces a joint charge and thermal management problem for batteries in a battery-supercapacitor hybrid power source of a portable system, which has been equipped with a forced convection cooling technique, such as a fan. A key consideration in such a system is that the battery aging depends strongly on the battery temperature, which is in turn a function of the workload running on the device and the control policy for the fan. More precisely, this paper presents a hierarchical algorithm for maximizing the battery lifespan under given workload conditions. The algorithm relies on a combination of reinforcement learning and dynamic programming techniques. Simulation results show that the proposed algorithm achieves up to 2× improvements in battery lifespan, resulting in completion of up to 80% additional workload before the battery expires.
Using stepwise regression and support vector regression to comprise REITs' portfolio

Real estate investment trusts (REITs) are income-based financial assets whose yields are largely related to financial and operating conditions. Their revenue comes mainly from the rent, and more than 90% distribute dividends. Therefore, the return on investment is stable and predictable. This paper attempts to study the relationship between yield and key indicators, and then constructs a good portfolio of REITs. Empirical data are 165 listed REITs with 50 key statistics (including three kinds of indicators: enterprise valuation, financial highlights, and trading information). Stepwise regression and support vector regression are used for modeling. A strategy, the Top 10% Strategy, is proposed as an application of this study, and this approach is validated by simulation. With STEP and SVR methods, the strategy has gained approximately 20% yield, which is much higher than the average of dataset, 8%. The results show that using key statistics can significantly increase profitability of REITs investment.
Incomplete Multi-Modal Visual Data Grouping

Nowadays multi-modal visual data are much easier to access as the technology develops. Nevertheless, there is an underlying problem hidden behind the emerging multi-modality techniques: What if one/more modal data fail? Motivated by this question, we propose an unsupervised method which well handles the incomplete multimodal data by transforming the original and incomplete data to a new and complete representation in a latent space. Different from the existing efforts that simply project data from each modality into a common subspace, a novel graph Laplacian term with a good probabilistic interpretation is proposed to couple the incomplete multi-modal samples. In such a way, a compact global structure over the entire heterogeneous data is well preserved, leading to a strong grouping discriminability. As a non-trivial contribution, we provide the optimization solution to the proposed model. In experiments, we extensively test our method and competitors on one synthetic data, two RGB-D video datasets and two image datasets. The superior results validate the benefits of the proposed method, especially when multimodal data suffer from large incompleteness.
Comparison of Approaches for Predicting Break Indices in Mandarin Speech Synthesis

This study adopts a large-scale corpus with five-tier break indices annotated according to C-TOBI. Based on it, several approaches, N-gram, Markov model and decision tree learning are applied to predict break indices automatically for unrestricted mandarin text. These approaches differ mutually not only in model, but also on features and even part-of-speech tag size. A deep comparison and analysis among these approaches was made in the research.
Eddy-Driven Buoyancy Gradients on Eastern Boundaries and Their Role in the Thermocline

Abstract It is demonstrated that eddy fluxes of buoyancy at the eastern and western boundaries maintain alongshore buoyancy gradients along the coast. Eddy fluxes arise near the eastern and western boundaries because on both coasts buoyancy gradients normal to the boundary are strong. The eddy fluxes are accompanied by mean vertical flows that take place in narrow boundary layers next to the coast where the geostrophic constraint is broken. These ageostrophic cells have a velocity component normal to the coast that balances the geostrophic mean velocity. It is shown that the dynamics in these thin ageostrophic boundary layers can be replaced by effective boundary conditions for the interior flow, relating the eddy flux of buoyancy at the seaward edge of the boundary layers to the buoyancy gradient along the coast. These effective boundary conditions are applied to a model of the thermocline linearized around a mean stratification and a state of rest. The linear model parameterizes the eddy fluxes of buoya.
Efficient Realization of Multichannel Frequency-Domain Adaptive Filtering and Application to Hands-Free Speech Communication

In unknown environments where we need to identify, model, or track unknown and time-varying channels, adaptive filtering has been proven to be an effective tool. In this contribution, we exclusively focus on multichannel algorithms in the frequency domain. For the single-channel case, there are many frequency-domain algorithms in the literature, where most of them are derived from existing time-domain algorithms and different heuristical considerations. Here, we propose a new theory for the rigorous derivation of a whole class of multichannel adaptive filtering algorithms in the frequency domain based on a recursive least-squares criterion. Then, from the so-called normal equation, we derive a generic adaptive algorithm in the frequency domain that we can write in different ways. An analysis of this algorithm shows that the mean-squared error convergence is independent of the input signal statistics. We suggest a very useful approximation, deduce some well-known algorithms, and give design rules for important parameters to optimize the performance in practice. Due to the rigorous approach, the proposed framework inherently takes the coherence between all input signal channels into account, while the computational complexity is kept low by introducing several new techniques, such as a robust recursive Kalman gain computation in the frequency domain and efficient fast Fourier transform (FFT) computation tailored for overlapping data blocks. Simulation results and real-time performance for applications such as multichannel acoustic echo cancellation on regular personal computers show the high efficiency of the approach. H. Buchner and W. Kellermann are with the University of Erlangen-Nuremberg, Cauerstr. 7, 91058 Erlangen, Germany. E-mail: {buchner, wk}@LNT.de J. Benesty is with Bell Laboratories, Lucent Technologies, 700 Mountain Avenue, 2D-518, Murray Hill, NJ 07974, USA. E-mail: jbenesty@bell-labs.com TECH. MEMO, TELECOMMUNICATIONS LAB. , UNIV. OF ERLANGEN-NUREMBERG, GERMANY, MARCH 2002 2
The KMOS Redshift One Spectroscopic Survey (KROSS) : rotational velocities and angular momentum of z ≈ 0.9 galaxies.

We present dynamical measurements for 586 H-alpha detected star-forming galaxies from the KMOS (K-band Multi-Object Spectrograph) Redshift One Spectroscopic Survey (KROSS). The sample represents typical star-forming galaxies at this redshift (z=0.6-1.0), with a median star formation rate of ~7 Msol/yr and a stellar mass range of log[M/Msol]~9-11. We find that the rotation velocity-stellar mass relationship (the inverse of the Tully-Fisher relationship) for our rotationally-dominated sources (v/sigma>1) has a consistent slope and normalisation as that observed for z=0 disks. In contrast, the specific angular momentum (j; angular momentum divided by stellar mass), is ~0.2-0.3 dex lower on average compared to z=0 disks. The specific angular momentum scales as M^[0.6+/-0.2], consistent with that expected for dark matter (i.e., proportional to M^[2/3]). We find that z~0.9 star-forming galaxies have decreasing specific angular momentum with increasing Sersic index. Visually, the sources with the highest specific angular momentum, for a given mass, have the most disk-dominated morphologies. This implies that an angular momentum-mass-morphology relationship, similar to that observed in local massive galaxies, is already in place by z~1.
Comparative analysis of Laparoscopic versus open surgical radiofrequency ablation for malignant liver tumors

Backgrounds/Aims This study aims to evaluate the comparative effectiveness of two surgical approaches on the treatment outcomes of radiofrequency ablation (RFA) for malignant liver tumors. Methods Fifty-seven patients with malignant liver tumors, hepatocellular carcinoma, cholangiocarcinoma and liver metastases, who were candidates for RFA, underwent laparoscopic or open surgical treatments. Results The patients' characteristics were comparable in the two groups that received open (n=33, 57.9%) and laparoscopic (n=24, 42.1%) surgical treatments. There were no statistically significant differences between the two groups in terms of recurrence rate (p=0.337) and overall survival (p=0.423). However, patients in the laparoscopic RFA group had significantly shorter hospital stay (14.1 vs. 5.9 days, p<0.05) and experienced fewer complications (Grade I: 62.5% vs. 26.3%, p=0.102). Conclusions Laparoscopic RFA can be performed for malignant liver tumors with lower morbidity rates, less invasiveness and lower expense compared to open surgical approach.
Interaction Testing: From Pairwise to Variable Strength Interaction

Although desirable as an important activity for quality assurances and enhancing reliability, complete and exhaustive software testing is prohibitively impossible due to resources as well as timing constraints. While earlier work has indicated that uniform pairwise testing (i.e. based on 2-way interaction of variables) can be effective to detect most faults in a typical software system, a counter argument suggests such conclusion cannot be generalized to all software system faults. In some system, faults may also be non-uniform and caused by more than two parameters. Considering these issues, this paper explores the issues pertaining to t-way testing from pairwise to variable strength interaction in order to highlight the state-of-the-art as well as the current state-of-practice.
Writer Identification for Offline Handwritten Kanji Characters Using Multiple Features

 Abstract—This paper presents a study on character features and recognizers used for writer identification of offline handwritten Kanji characters. It is shown that a combination of two global features, two local features, and majority voting as a recognizer is efficient for writer identification. We performed experiments using an offline Kanji character database containing one-hundred Kanji characters, each written by one-hundred writers, and fifty samples of each Kanji character for a given writer. The experimental results show that the identification rate is 7 points higher than the conventional method using a single feature and obtained an identification rate higher than 99% by using three character classes. Writer identification based on scanned images of handwritten characters is a useful biometric modality with applications in forensic and historical document analysis. Research on writer identification that uses online characters is widespread, but offline characters lack form for conveying dynamic information. Nevertheless, research on writer identification using offline characters has proposed many features to acquire useful information. We studied efficient character features and recognizers for writer identification of handwritten offline Kanji characters. Kanji consists of logographic Chinese characters adopted in Japanese writing. The text-dependent writer identification in our research uses a character recognition process before writer identification because, in text-dependent writer identification, a character class is assumed to be already recognized. Therefore, most of the research on text-dependent writer identification of handwritten characters has the following characteristics: employs features developed for character recognition, does not use multiple character features, and uses the local features of a character. In this paper, we propose efficient character features and a recognizer for text-dependent writer identification of a handwritten Kanji character (1).
Web-based Multimedia GP Medical System

This paper introduced GP-Soft, a powerful new patient management system, which is designed to improve the records management for clinic, nursing home and hospital. GP-Soft is a complete patient management system that includes a complete set of tools for handling prescription writings, medication, patient management and patient billing. In addition, GP-Soft also provides a set of integrated communication tools that will allow General Practitioners (GP) to carry out collaboration and discussion. It is the first web-based system to combine patient management, patient accounting and communication tools into an all-in-one solution desired by every GP in practice. The web-based system can provide easy access to the user using Internet/Intranet and operating system independent. The system supports a centralised control of patients' data on the server and provides information sharing and distributed data management through the network structure. GP-Soft can be installed on any platform system and is supported by a back-end database system.
Genetic programming using a minimum description length principle

This paper introduces a Minimum Description Length (MDL) principle to de ne tness functions in Genetic Programming (GP). In traditional (Koza-style) GP, the size of trees was usually controlled by user-de ned parameters, such as the maximum number of nodes and maximum tree depth. Large tree sizes meant that the time necessary to measure their tnesses often dominated total processing time. To overcome this di culty, we introduce a method for controlling tree growth, which uses an MDL principle. Initially we choose a \decision tree" representation for the GP chromosomes, and then show how an MDL principle can be used to de ne GP tness functions. Thereafter we apply the MDL-based tness functions to some practical problems. Using our implemented system \STROGANOFF", we show how MDL-based tness functions can be applied successfully to problems of pattern recognitions. The results demonstrate that our approach is superior to usual neural networks in terms of generalization of learning.
An integrated resource allocation scheme for multi-tenant data-center

The success of multi-tenant data-centers depends on the ability to provide performance guarantees in terms of the resources provisioned to the tenants. As bandwidth is shared in a best-effort way in today's data-centers, traffic generated between a set of VMs affect the traffic between another set of VMs sharing the same physical links. This paper proposes an integrated resource allocation scheme that considers not only server-resources, but also network-resource, to decide the mapping of VMs onto servers. We present a three-phase mechanism that finds the right set of servers for the requested VMs, with an aim of reducing the bandwidth on shared links. This mechanism provisions the required bandwidth for the tenants, besides increasing the number of tenants that can cohabit in a data-center. We demonstrate, using simulations, that the proposed scheme accommodates 10%-23% more requests in comparison to a load-balancing allocator that does not consider bandwidth requirements of VMs.
Towards rigorous practice: A framework for assessing naturalistic evaluations in the development sector

This article reflects on the design and application of a framework for assessing the rigour of naturalistic evaluations in the development sector. The framework was employed in a review of a naturalistic evaluation ‘model’. It focused on the quality of evaluation results generated by the application of this model to outcome and impact assessments of development programmes. Naturalistic evaluation research is frequently portrayed as lacking in rigour, so we wanted to know whether the outcomes of development interventions could be assessed with an appropriate level of rigour when a naturalistic evaluation approach is used. The assessment entailed a meta-evaluation of five outcome/impact evaluations; these were undertaken by a Cape Town-based development consultancy over a four-year period. Using Lincoln and Guba’s trustworthiness criteria as the foundation for a meta-evaluation framework, we demonstrated that hallmarks of rigorous practice could be developed and applied to evaluations that employ a naturalistic evaluation model.
Decomposing the roles of perseveration and expected value representation in models of the Iowa gambling task

Models of human behavior in the Iowa Gambling Task (IGT) have played a pivotal role in accounting for behavioral differences during decision-making. One critical difference between models that have been used to account for behavior in the IGT is the inclusion or exclusion of the assumption that participants tend to persevere, or stay with the same option over consecutive trials. Models that allow for this assumption include win-stay-lose-shift (WSLS) models and reinforcement learning (RL) models that include a decay learning rule where expected values for each option decay as they are chosen less often. One shortcoming of RL models that have included decay rules is that the tendency to persevere by sticking with the same option has been conflated with the tendency to select the option with the highest expected value because a single term is used to represent both of these tendencies. In the current work we isolate the tendencies to perseverate and to select the option with the highest expected value by including them as separate terms in a Value-Plus-Perseveration (VPP) RL model. Overall the VPP model provides a better fit to data from a large group of participants than models that include a single term to account for both perseveration and the representation of expected value. Simulations of each model show that the VPP model's simulated choices most closely resemble the decision-making behavior of human subjects. In addition, we also find that parameter estimates of loss aversion are more strongly correlated with performance when perseverative tendencies and expected value representations are decomposed as separate terms within the model. The results suggest that the tendency to persevere and the tendency to select the option that leads to the best net payoff are central components of decision-making behavior in the IGT. Future work should use this model to better examine decision-making behavior.
Eventology versus contemporary theories of uncertainty

The development of probability theory together with the Bayesian approach in the three last centuries is caused by two factors: the variability of the physical phenomena and partial ignorance about them. As now it is standard to believe [Dubois, 2007], the nature of these key factors is so various, that their descriptions are required special uncertainty theories, which differ from the probability theory and the Bayesian credo, and provide a better account of the various facets of uncertainty by putting together probabilistic and set-valued representations of information to catch a distinction between variability and ignorance. Eventology [Vorobyev, 2007], a new direction of probability theory and philosophy, offers the original event approach to the description of variability and ignorance, entering an agent, together with his/her beliefs, directly in the frameworks of scientific research in the form of eventological distribution of his/her own events. This allows eventology, by putting together probabilistic and set-event representation of information and philosophical concept of event as co-being [Bakhtin, 1920], to provide a unified strong account of various aspects of uncertainty catching distinction between variability and ignorance and opening an opportunity to define imprecise probability as a probability of imprecise event in the mathematical frameworks of Kolmogorov's probability theory [Kolmogorov, 1933].
Network Traffic Analysis and Intrusion Detection Using Packet Sniffer

Computer software that can intercept and log traffic passing over a digital network or part of a network is better known as packet sniffer. The sniffer captures these packets by setting the NIC card in the promiscuous mode and eventually decodes them. The decoded information can be used in any way depending upon the intention of the person concerned who decodes the data (i.e. malicious or beneficial purpose). Depending on the network structure one can sniff all or just parts of the traffic from a single machine within the network. However, there are some methods to avoid traffic narrowing by switches to gain access to traffic from other systems on the network. This paper focuses on the basics of packet sniffer and its working, development of the tool on Linux platform and its use for Intrusion Detection. It also discusses ways to detect the presence of such software on the network and to handle them in an efficient way. Focus has also been laid to analyze the bottleneck scenario arising in the network, using this self developed packet sniffer. Before the development of this indigenous software, minute observation has been made on the working behavior of already existing sniffer software such as wireshark (formerly known as ethereal), tcpdump, and snort, which serve as the base for the development of our sniffer software. For the capture of the packets, a library known as libpcap has been used. The development of such software gives a chance to the developer to incorporate the additional features that are not in the existing one.