Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-processing of qualities of decision rules within a testing phase

Knowledge bases are often generated by learning (data mining) algorithms. One possible form of such a knowledge base (decision scheme, model) is a set of decision rules. If a classifier uses an unordered set of rules (which happens frequently) a problem arises concerning what to do if the classification of an unseen object 'fires' rules of different classes. One possible solution consists of calculating a numerical factor that would explicitly indicate a quality (predictive power) of each rule and giving a higher priority to the rule with a higher quality. In the literature, there can be found several formulas for calculations of rule quality. So far as we know, in existing models the rule qualities are calculated by the learning (data mining) algorithm itself and remain constant during the phase of classification. This paper introduces a new strategy that allows to modify (refine) the rule qualities during the classification of unseen objects. The refinement is performed in a feedback loop that can increase or decrease the quality of the rule involved with the classification of each unseen object. Thus, the entire method of refinement of rule qualities may be viewed as a post-processing of a set of decision rules performed within the testing phase. The results of experiments with both real-world and artificial data are discussed and analysed.
THE ABELS FRAMEWORK FOR LINKING DISTRIBUTED SIMULATIONS USING SOFTWARE AGENTS

Many simulations need access to dynamicallychanging data from other sources such as sensors or even other simulations. For example, a forest fire simulation may need data from sensors in the forest and a weather simulation at a remote site. We have developed an agentbased software framework to facilitate the dynamic exchange of data between distributed simulations and other remote data resources. The framework, called ABELS (Agent-Based Environment for Linking Simulations), allows independently designed simulations to communicate seamlessly with no a priori knowledge of the details of other simulations and data resources. This paper discusses our architecture and current implementation using Sun Microsystems’ Jini technology and the D’Agents mobile agent system. This paper extends earlier work by describing the implementation of the brokering system for matching data producers and consumers and providing additional details of other components.
A Distributed Architecture for Multiplayer Interactive Applications on the Internet

This paper describes the design, implementation, and evaluation of MiMaze, a distributed multiplayer game on the Internet, and, more precisely, on the design of dedicated transmission control mechanisms. MiMaze is implemented on a completely distributed communication architecture based on the IP multicast protocol suite (RTP/UDP/IP). This is the first work to analyze a distributed interactive game on the multicast Internet. The major element of the MiMaze architecture is a distributed synchronization mechanism that guarantees the consistency of the game regardless of network delay. This paper provides an evaluation of the MiMaze game on the MBone, and discusses approaches to monitor and evaluate this new type of application. The main contribution of this work is to show, based on an example, the feasibility of this new family of applications on a best-effort network. It is shown that real-time interactivity can be maintained, provided that some level of inconsistency can be tolerated by the application. This work also highlights the role of multicast as an enabling technology for a real-time Internet.
Low rate speech coding for telecommunications

Over the last decade major advances have been made in speech coding technology which is now widely used in international, digital mobile and satellite networks. The most recent techniques permit telephone network quality speech transmission at 8 kbit/s, but there are still demands for even lower rates and more flexible, good quality coding techniques for various network applications. This paper reviews the developments so far, and describes a new class of speech coding methods known as speech interpolation coding which has the potential to provide toll-quality, speech coding at or below 4 kbit/s.
Security notions for information theoretically secure encryptions

This paper is concerned with several security notions for information theoretically secure encryptions defined by the variational (statistical) distance. To ensure the perfect secrecy (PS), the mutual information is often used to evaluate the statistical independence between a message and a cryptogram. On the other hand, in order to recognize the information theoretically secure encryptions and computationally secure ones comprehensively, it is necessary to reconsider the notion of PS in terms of the variational distance. However, based on the variational distance, three kinds of definitions for PS are naturally introduced, but their relations are not known. In this paper, we clarify that one of three definitions for PS with the variational distance, which is a straightforward extension of Shannon's perfect secrecy, is stronger than the others, and the weaker two definitions of PS are essentially equivalent to the statistical versions of indistinguishability and semantic security.
Evaluation and improvements of programming models for the Intel SCC many-core processor

Since the beginning of the multicore era, parallel processing has become prevalent across the board. On a traditional multicore system, a single operating system manages all cores and schedules threads and processes among them, inherently supported by hardware-implemented cache coherence protocols. However, a further growth of the number of cores per system implies an increasing chip complexity, especially with respect to the cache coherence protocols. Therefore, a very attractive alternative for future many-core systems is to waive the hardware-based cache coherency and to introduce a software-oriented message-passing based architecture instead: a so-called Cluster-on-Chip architecture. Intel's Single-chip Cloud Computer (SCC), a many-core research processor with 48 non-coherent memory-coupled cores, is a very recent example for such a Cluster-on-Chip architecture. The SCC can be configured to run one operating system instance per core by partitioning the shared main memory in a strict manner. However, it is also possible to access the shared main memory in an unsplit and concurrent manner, provided that the cache coherency is then ensured by software. In this paper, we detail our first experiences gained while developing low-level software for message-passing and shared-memory programming on the SCC. In doing so, we evaluate the potential of both programming models and we show how these models can be improved especially with respect to the SCC's many-core architecture.
Insider Knowledge–Influenced Pedagogy

This article summarizes an approach that enhances adult student engagement and success by consciously incorporating student experiences that relate to topics covered in class. A specific form of didactic exercise is used to incorporate student experience. This approach also provides an opportunity for exploration and discussion of less formal kinds of knowledge that may nevertheless be of value and significance to students and contribute to their engagement in learning. Some implications for the epistemology of learning are discussed.
The global impact of supersaturation in a coupled chemistry-climate model

Ice supersaturation is important for understand- ing condensation in the upper troposphere. Many general circulation models however do not permit supersaturation. In this study, a coupled chemistry climate model, the Whole Atmosphere Community Climate Model (WACCM), is mod- ified to include supersaturation for the ice phase. Rather than a study of a detailed parameterization of supersatura- tion, the study is intended as a sensitivity experiment, to understand the potential impact of supersaturation, and of expected changes to stratospheric water vapor, on climate and chemistry. High clouds decrease and water vapor in the stratosphere increases at a similar rate to the prescribed supersaturation (20% supersaturation increases water vapor by nearly 20%). The stratospheric Brewer-Dobson circula- tion slows at high southern latitudes, consistent with slight changes in temperature likely induced by changes to cloud radiative forcing. The cloud changes also cause an increase in the seasonal cycle of near tropopause temperatures, in- creasing them in boreal summer over boreal winter. There are also impacts on chemistry, with small increases in ozone in the tropical lower stratosphere driven by enhanced pro- duction. The radiative impact of changing water vapor is dominated by the reduction in cloud forcing associated with fewer clouds ( +0.6 Wm 2 ) with a small component likely from the radiative effect (greenhouse trapping) of the extra water vapor ( +0.2 Wm 2 ), consistent with previous work. Representing supersaturation is thus important, and changes to supersaturation resulting from changes in aerosol loading for example, might have a modest impact on global radia- tive forcing, mostly through changes to clouds. There is no evidence of a strong impact of water vapor on tropical tropopause temperatures.
Automatic sightline stabilisation in noisy imagery

This paper examines the use of automatic image registration for the stabilisation of images from a moving camera in the presence of noise. In good quality imagery, the motion of the camera platform can be compensated by matching the features from image to image and deriving the appropriate image transformation that will correct for the motion of the camera. This paper considers the sensitivity of such registration algorithms to the different types of noise that occur in infrared imaging. The most reliable method for the registration of infrared imagery in the presence of these noise sources were Fourier registration and region- based registration. Both of them are robust and the Fourier method had the added advantage which is relatively insensitive to the initial image translation errors.
B7-CD28 costimulation unveils the hierarchy of tumor epitopes recognized by major histocompatibility complex class I-restricted CD8+ cytolytic T lymphocytes

Immunization of mice with tumors genetically engineered to express the B7 costimulatory molecules amplifies the antitumor immune response mediated by CD8+ cytolytic T lymphocytes (CTL). In this report, we examined the effect of B7-CD28 costimulation on the hierarchy of tumor epitopes. Using a combination of affinity chromatography/reversed-phase high performance liquid chromatography and CTL cloning, we show that major histocompatibility complex (MHC) class I molecules from EL4 lymphoma cells can present at least six distinct CTL epitopes presented by MHC class I molecules. Nevertheless, mice immunized with wild-type B7-negative EL4 cells develop CTL only to one immunodominant epitope. In contrast, immunization with B7-transduced EL4 cells led to not only the amplification of the CTL response to this immunodominant epitope, but also to the recognition of five otherwise silent subdominant epitopes. The adoptive transfer of a CTL clone against such a subdominant epitope cured mice bearing EL4 lymphoma growing as an ascites tumor. The fact that CTL response can be spread to normally silent epitopes as a result of B7-CD28 costimulation suggests a novel approach to manipulate the hierarchy of CTL epitopes and offers an opportunity to explore novel targets for T cell-mediated cancer therapy.
Vanishing point estimation for challenging road images

In this paper, we present an efficient vanishing point detection method for challenging road images. This detection process is based on the geometrical features of the roads. The slope distribution of the line segments is analyzed to reduce the spurious lines. A distance-based weighting scheme is also utilized to eliminate the voting noise in the voting stage. The proposed algorithm has been tested on a natural data set from Defense Advanced Research Projects Agency (DARPA). Experimental results with both quantitative and qualitative analyses are provided, which demonstrate the superiority of the proposed method over some state-of-the-art methods.
Real-time flood forecasting with the use of inadequate data

Abstract A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes.
An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data

We propose an efficient method for estimating the parameters of a Bayesian network, from incomplete datasets, i.e., datasets containing variables with missing values. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach is capable of producing consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM. When data is scarce, we learn parameters of comparable quality to EM. Given sufficient data, we can learn parameters that are orders of magnitude closer to the true parameters.
Update on the COP 9 Signalosome The COP 9 Signalosome : Its Regulation of Cullin-Based E 3 Ubiquitin Ligases and Role in Photomorphogenesis 1

Plants are sessile organisms that have evolved an extraordinary phenotypic and developmental plasticity to optimally adapt to the ambient environment. Light is one of the most critical environmental factors, since it is a key signal for regulating a wide range of developmental and physiological processes. The Constitutively Photomorphogenic9 (COP9) signalosome (CSN), originally discovered as an essential complex that regulates light-induced development in Arabidopsis (Arabidopsis thaliana), is a conserved protein complex found among all eukaryotes (Wei and Deng, 1992; Wei et al., 1994, 1998; Chamovitz et al., 1996). This evolutionarily conserved complex has a central role regulating the ubiquitin (Ubq)/26S proteasome pathway and participates in diverse cellular and developmental processes, including cell cycle progression, gene expression, embryogenesis, circadian rhythms, plant responses to light and hormones, and DNA repair (Wei and Deng, 2003; Wolf et al., 2003). The Ubq-proteasome pathway mediates the degradation of proteins and is the dominant selective protein turnover system in plants (Dreher and Callis, 2007). Ubiquitination and proteolysis by this pathway are achieved by the consecutive activities of three enzymes, E1, E2, and E3. Proteins that are destined for proteasomal degradation are first recognized by E3 Ubq ligases, which subsequently position the substrate for the optimal transfer of Ubq. E2 Ubq-conjugating enzymes, which have been charged with Ubq molecules by E1 Ubq-activating enzymes, provide the activated Ubq, which allows E3 to stimulate the conjugation of Ubq to the substrate. The polyubiquitinated substrates are then degraded by the 26S proteasome (Smalle and Vierstra, 2004). The best-studied biochemical function of CSN is the regulation of the cullin-RING-type E3 ubiquitin ligases (CRLs) by physically interacting with CRLs and removing RUB1 (for RELATED TO UBIQUITIN, or NEDD8 in animals), a Ubq-like protein, from the cullin subunit (Wei and Deng, 2003; Chew and Hagen, 2007). Although the rubylated and derubylated forms of CRLs are both important for their proper function, the role of this modification is still not fully understood. With the recent advances in structural biology and the diverse and growing range of developmental and signaling pathways that CRLs are implicated in, new roles have been assigned to CSN as a regulator of CRLs. In this Update article, we will focus on the structure, regulation, and function of CSN and describe how CSN is a regulator of plant photomorphogenesis.
ENERGY–MOMENTUM DISTRIBUTION: A CRUCIAL PROBLEM IN GENERAL RELATIVITY

This paper is aimed to elaborate the problem of energy–momentum in general relativity. In this connection, we use the prescriptions of Einstein, Landau–Lifshitz, Papapetrou and Moller to compute the energy–momentum densities for two exact solutions of Einstein field equations. The space–times under consideration are the nonnull Einstein–Maxwell solutions and the singularity-free cosmological model. The electromagnetic generalization of the Godel solution and the Godel metric become special cases of the nonnull Einstein–Maxwell solutions. It turns out that these prescriptions do not provide consistent results for any of these space–times. These inconsistent results verify the well-known proposal that the idea of localization does not follow the lines of pseudotensorial construction but instead follows from the energy–momentum tensor itself. These differences can also be understood with the help of the Hamiltonian approach.
Financial Forecasting with a Neuro-Genetic Approach

Traditional approaches for time series modeling assume a linear underlying relationship among the past and the future values of a time series, but they may be totally inappropriate if the underlying process is nonlinear. Artificial Neural Networks (ANNs) are a particularly promising branch on soft computing techniques, as they possess the ability to determine non-linear relationships, and are particularly adept at dealing with noisy datasets. The possibility of forecasting a financial time-series is tested in this work by using an evolutionary approach for the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. In particular, the implemented approach uses different financial instruments to forecast the next-day closing price of the Dow Jones Industrial Average (DJIA). The results seem to substantially confirm the efficient market hypothesis, by providing strong evidence that the next-day closing price of the DJIA is unpredictable, even by considering a variety of related financial indicators.
Random Sampling and Locality Constraint for Fast Face Sketch Synthesis

Face sketch synthesis plays an important role in both digital entertainment and law enforcement. It generally consists of two parts: neighbor selection and reconstruction weight representation. State-of-the-art face sketch synthesis methods perform neighbor selection online in a data-driven manner by K nearest neighbor (K-NN) searching. However, the online search increases the time consuming for synthesis. Moreover, since these methods need to traverse the whole training dataset for neighbor selection, the computational complexity increases with the scale of the training database and hence these methods have limited scalability. In addition, state-of-the-art methods consider that all selected neighbors contribute equally to the reconstruction weight computation process while the distinct similarity between the test patch and these neighbors are neglected. In this paper, we employ offline random sampling in place of online K-NN search to improve the synthesis efficiency. Locality constraint is introduced to model the distinct correlations between the test patch and random sampled patches. Extensive experiments on public face sketch databases demonstrate the superiority of the proposed method in comparison to state-of-the-art methods, in terms of both synthesis quality and time consumption. The proposed method could be extended to other heterogeneous face image transformation problems such as face hallucination. We will release the source codes of our proposed methods and the evaluation metrics for future study online: http://www.ihitworld. com/RSLCR.html.
On the Restriction to D ∗ × D ∗ of Representations of p-adic GL 2 ( D ) In memory of my mother

Let D be a division algebra over a nonarchimedean local field. Given an irreducible representation π of GL2(D), we describe its restriction to the diagonal subgroup D∗×D∗. The description is in terms of the structure of the twisted Jacquet module of the representation π. The proof involves Kirillov theory that we have developed earlier in joint work with Dipendra Prasad. The main result on restriction also shows that π is D∗ × D∗-distinguished if and only if π admits a Shalika model. We further prove that if D is a quaternion division algebra then the twisted Jacquet module is multiplicityfree by proving an appropriate theorem on invariant distributions; this then proves a multiplicity-one theorem on the restriction to D∗ × D∗ in the quaternionic case.
ATF4 differentially regulates transcriptional activation of myeloid‐specific genes by C/EBPε and C/EBPα

Dimerization between different basic region leucine zipper (ZIP) transcription factors is regarded as an important mechanism for integrating various extracellular signals to control specific patterns of gene expression in cells. The activating transcription factor 4 (ATF4) protein was identified as a principal partner for the myeloid‐specific transcriptional factor C/EBPε. Dimerization required the ZIP motif of each protein and redirected DNA binding of C/EBPε and ATF4 from their respective symmetric consensus sites to asymmetric C/EBP and cAMP response element sites. The C/EBPε:ATF4 heterodimer bound to the C/EBP sites in the promoters of the myeloid‐specific genes encoding neutrophil elastase (NE) and the G‐CSF receptor (G‐CSFR). Also, the heterodimer bound a previously uncharacterized site in the promoter of the mim‐1 gene at nucleotide –174. Coexpression of ATF4 and C/EBPε in the presence of c‐Myb synergistically activated the mim‐1 and NE promoters compared with C/EBPε plus c‐Myb alone. Synergistic activation was not observed for the G‐CSFR promoter and only occurred in the presence of c‐myb with the NE or mim‐1 promoters. In contrast, ATF4:C/EBPα dimers bound to the C/EBP sites in the G‐CSFR and NE promoters, but transcriptional activation was inhibited by 30–80% in the presence or absence of c‐Myb. We propose that ATF4 may regulate myeloid gene expression differentially by potentiating C/EBPε but inhibiting C/EBPα‐mediated transcriptional activation.
CYUT Chinese Textual Entailment Recognition System for NTCIR-10 RITE-2

ABSTRACT Textual Entailment (TE) is a critical issue in natural language processing (NLP). In this paper we report our approach to the Chinese textual entailment and the system result on NTCIR-10 RITE-2 both simplified and traditional Chinese dataset. Our approach is based on more observation on training data and finding more types of linguistic features. The approach is a complement to the traditional machine learning approach, which treat every pair in a standard process. In the official runs, we tested three types of entailment features, i.e. the usage of negative words, time expression, and numbers. The experimental result is promising; we find this extensible approach can include more types.