Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Multilayer Perceptrons Neural Network and Genetic Algorithm to Predict the NTD/USD Exchange Rate

In recent years, the global community has experienced economic difficulties, such as the 2008 financial crisis and the ongoing European debt crisis. Consequently, currency values have fluctuated significantly over short periods of time, which increases the difficulty of survival and fear of businesses that rely on import and export trade. Failure to properly and appropriately address the operational risks of exchange rate fluctuations can reduce corporate profit and even lead to operational losses. However, financial markets provide numerous methods for corporations to hedge the risks of exchange rate fluctuations. Nevertheless, a model for predicting exchange rate fluctuations can enable business owners to make more appropriate judgments. This study employs a multilayer perceptions (MLP) neural network with genetic algorithm (GA) to predict the New Taiwan dollar (NTD)/U.S. dollar (USD) exchange rate. The GA is used to determine the optimum number of input and hidden nodes for a feedforward neural network, the optimum slope of the activation function, and the optimum learning rates and momentum coefficients. The empirical results show that the ability of the proposed model to predict the NTD/USD exchange rate is excellent. The absolute relative error between the predicted value and the actual value was 0.2948%, and the correlation coefficient was 0.994802.
Measuring the aorta in the era of multimodality imaging: still to be agreed.

Thoracic aortic dilatation is associated with major vascular complications with fatal consequences, such as dissection and aortic rupture. We can predict the risk of rupture or dissection based on aortic size (1,2). Broad spectrum of aortic complications benefits from different cardiac imaging techniques: transthoracic echocardiography (TTE), transoesophageal echocardiography (TOE), computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, aortic dilatation is one of the most frequent clinical entities for request in cardiac imaging laboratories, both for the initial diagnosis and the monitoring and establishment of the optimal timing for surgery.
Efficient Strategies for Continuous Distributed Tracking Tasks

While traditional databases have focused on single query evaluation in a centralized setting, emerging applications require continuous tracking of queries on data that is widely distributed and constantly updated. We describe such scenarios, and describe the challenges involved in designing communication-efficient protocols for the tracking tasks we define. We outline some solutions to these problems, by abstracting a model of the communication system, defining the tracking tasks of interest, and building query-tracking schemes based on three guiding principles of minimizing global information, using summaries to capture whole data streams, and seeking stability of the protocols.
Generative Temporal Models with Memory

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, express uncertainty about those unpredictable elements, and rapidly identify novel elements that may help to predict the future. To create such models, we introduce Generative Temporal Models augmented with external memory systems. They are developed within the variational inference framework, which provides both a practical training methodology and methods to gain insight into the models' operation. We show, on a range of problems with sparse, long-term temporal dependencies, that these models store information from early in a sequence, and reuse this stored information efficiently. This allows them to perform substantially better than existing models based on well-known recurrent neural networks, like LSTMs.
Analysis of the effects of early nutritional environmenton inbreeding depression in Drosophila melanogaster

The impact of nutritional deficiencies early in life in determining life‐history variation in organisms is well recognized. The negative effects of inbreeding on fitness are also well known. Contrary to studies on vertebrates, studies on invertebrates are not consistent with the observation that inbreeding compromises resistance to parasites and pathogens. In this study, we investigated the effect of early nutrition on the magnitude of inbreeding depression in development time, adult body size and adult resistance to the bacterium Serratia marcescens in Drosophila melanogaster. We found that early nutritional environment had no effect on the magnitude of inbreeding depression in development time or adult body size but may have played a small role in adult resistance to the bacterial infection. Estimates of heritabilities for development time under the poor nutritional environment were larger than those measured under the standard nutritional conditions.
Modeling and torque control for a 4-wheel-drive electric vehicle

Recent developments in control systems have made it possible to manipulate wheel torques independently in order to improve vehicle handling characteristics of electric vehicles. These vehicles usually employ in-wheel motors which can be independently controlled. This paper presents a 4-wheel-drive electric vehicle for research purposes. A dynamic model based on the connection of the mechanical devices in the vehicle is implemented as a CAD model and as a simulator using Simmechanics in the MATLAB/Simulink framework. This model is validated using data from real experiments and it can be used to test new torque controllers before their implementation. The vehicle has a real-time control system that integrates the torque controller for the 4 motors. This control system fulfils the driver's demand computing the torque that must be supplied by each of the 4 motors. Experimental results are presented, that show that this vehicle seems to be a good test bench that can be used for testing advanced torque distribution controllers.
RAPD analysis for genetic diversity in Changium smyrnioides (Apiaceae), an endangered plant1

Random Amplified Polymorphic DNA (RAPD) markers were used to measure genetic diversity of Changium smyrnioides Wolff (Apiaceae), a genus endemic to eastern China and an endangered medicinal plant. collected from five populations along the Yangzi River A total of 92 amplified bands were scored from the 13 RAPD primers, and a mean of 7.1 amplified bands per primer and 69％(64 bands) percentages of polymorphic bands (PPB) was found. The Shannon＇s index was used to partition genetic diversity Genetic diversity estimates indicated that 51. ％ of total diversity was among populations and 48.8％ within populations The species shows higher genetic diversity between populations than other endangered plants In this study. the RAPD results of genetic diversity are similar to those of previous studies employing allozymes (58％ among populations and 42％ within populations) The RAPDs provide a useful tool for assessing genetic diversity of rare, endemic species and for resolving relationships among populations The results show that the genetic diversity of this species is high. possibly allowing it to more easily adapt to environmental variations A pattern of greater variation among rather than within populations was observed with both markers This distributive pattern of genetic variation of C. smyrnioides populations provides important baseline data for conservation and collection strategies for this species It is suggested that most of the populations should be sampled in ex-situation protection so as to retain as much genetic diversity as possible.
Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding

Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90–0.99) vs. 0.58 (CI = 0.50–0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA‐based approach has the potential to become the next‐generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems.
Impact of IEEE 802.11n/ac PHY/MAC High Throughput Enhancements over Transport/Application Layer Protocols - A Survey

Since the inception of Wireless Local Area Networks (WLANs) in the year 1997, it has tremendously grown in the last few years. IEEE 802.11 is popularly known as WLAN. To provide the last mile wireless broadband connectivity to users, IEEE 802.11 is enriched with IEEE 802.11a, IEEE 802.11b and IEEE 802.11g. More recently, IEEE 802.11n, IEEE 802.11ac and IEEE 802.11ad are introduced with enhancements to the physical (PHY) layer and medium access control (MAC) sublayer to provide much higher data rates and thus these amendments are called High Throughput WLANs (HT-WLANs). For both standards, PHY is enhanced with multiple-input multiple-output (MIMO) antenna technologies, channel bonding, short guard intervals (SGI), enhanced modulation and coding schemes (MCS). At the same time, MAC layer overhead is reduced by introducing frame aggregation and block acknowledgement technologies. However, existing studies reveal that although PHY and MAC enhancements promise to improve physical data rate significantly, they yield negative impact over upper layer protocols -- mainly for reliable end-to-end transport/application layer protocols. As a consequence, a large number of schools have focused researches on HT-WLANs to improve the coordination among PHY/MAC and upper layer protocols and thus, boost up the performance benefit. In this survey, we discuss the impact of enhancements of PHY/MAC layer in HT-WLANs over transport/application layer protocols. list down different open challenges that can be explored for the development of next generation HT-WLAN technologies.
A reassessment of the low temperature thermal degradation of cellulose

Reviews the current knowledge of chemical degradation of cellulose and relate it to electrical insulation in oil-filled transformers. Paper impregnated with insulating oil is still widely used to insulate the windings of power transformers. A typical 600 MVA unit contains 12 t of paper and 45 t (40000 l) of oil. >
CNS Tumor Prediction Using Gene Expression Data Part II

In this chapter, we propose a novel algorithm for characterizing a variety of CNS tumors. The proposed algorithm is illustrated with an analysis of an Affymetrix gene expression data from CNS tumor samples (Pomeroy et al., 2002). As discussed in the previous chapter entitled: CNS Tumor Prediction Using Gene Expression Data Part I, we used an ANOVA model to normalize the microarray gene expression measurements. In this chapter, we introduce a systemic way of building tumor prototypes to facilitate automatic prediction of CNS tumors.
Strategic Topology Switching for Security-Part II: Detection & Switching Topologies

This two-part paper considers strategic topology switching for security in the second-order multi-agent system. In Part II, we propose a strategy on switching topologies to detect zero-dynamics attack (ZDA), whose attack-starting time is allowed to be not the initial time. We first characterize the sufficient and necessary condition for detectability of ZDA, in terms of the network topologies to be switched to and the set of agents to be monitored. We then propose an attack detection algorithm based on the Luenberger observer, using the characterized detectability condition. Employing the strategy on switching times proposed in Part I and the strategy on switching topologies proposed here, a strategic topology-switching algorithm is derived. Its primary advantages are threefold: (i) in achieving consensus in the absence of attacks, the control protocol does not need velocity measurements and the algorithm has no constraint on the magnitudes of coupling weights; (ii) in tracking system in the absence of attacks, the Luenberger observer has no constraint on the magnitudes of observer gains and the number of monitored agents, i.e., only one monitored agent's output is sufficient; (iii) in detecting ZDA, the algorithm allows the defender to have no knowledge of the attack-starting time and the number of misbehaving agents (i.e., agents under attack). Simulations are provided to verify the effectiveness of the strategic topology-switching algorithm.
Feudal Reinforcement Learning

One way to speed up reinforcement learning is to enable learning to happen simultaneously at multiple resolutions in space and time. This paper shows how to create a Q-learning managerial hierarchy in which high level managers learn how to set tasks to their submanagers who, in turn, learn how to satisfy them. Submanagers need not initially understand their managers' commands. They simply learn to maximise their reinforcement in the context of the current command. We illustrate the system using a simple maze task. As the system learns how to get around, satisfying commands at the multiple levels, it explores more efficiently than standard, flat, Q-learning and builds a more comprehensive map.
The art of search: a study of art directors

We formulated a model of visual search by conducting a work flow study and task analysis of art directors as they searched for images to use in an advertisement. The analysis revealed the presence of artistic and image concepts, flexible structures which guide the search and are molded by them. Analysis results were used to build a model-based interface for visual search. Results from presenting the interface to users indicate that the interface has the potential to make significant contributions to the visual search task, both in time savings and as an aid to the creative process.
Leveraging software architectures to guide and verify the development of sense/compute/control applications

A software architecture describes the structure of a computing system by specifying software components and their interactions. Mapping a software architecture to an implementation is a well known challenge. A key element of this mapping is the architecture's description of the data and control-flow interactions between components. The characterization of these interactions can be rather abstract or very concrete, providing more or less implementation guidance, programming support, and static verification. In this paper, we explore one point in the design space between abstract and concrete component interaction specifications. We introduce a notion of interaction contract that expresses allowed interactions between components, describing both data and control-flow constraints. This declaration is part of the architecture description, allows generation of extensive programming support, and enables various verifications. We instantiate our approach in an architecture description language for Sense/Compute/Control applications, and describe associated compilation and verification strategies
The scaffolding function of the RLTPR protein explains its essential role for CD28 co-stimulation in mouse and human T cells

In two complementary papers, Casanova, Malissen, and collaborators report the discovery of human RLTPR deficiency, the first primary immunodeficiency of the human CD28 pathway in T cells. Together, the two studies elucidate the largely (but not completely) overlapping roles of RLTPR in CD28 signaling in T and B cells of humans and mice.
Characterization of protein release from hydrolytically degradable poly(ethylene glycol) hydrogels

We present a novel fully hydrophilic, hydrolytically degradable poly(ethylene glycol) (PEG) hydrogel suitable for soft tissue engineering and delivery of protein drugs. The gels were designed to overcome drawbacks associated with current PEG hydrogels (i.e., reaction mechanisms or degradation products that compromise protein stability): the highly selective and mild cross‐linking reaction allowed for encapsulating proteins prior to gelation without altering their secondary structure as shown by circular dichroism experiments. Further, hydrogel degradation and structure, represented by mesh size, were correlated to protein release. It was determined that polymer density had the most profound effect on protein diffusivity, followed by the polymer molecular weight, and finally by the specific chemical structure of the cross‐linker. By examining the diffusion of several model proteins, we confirmed that the protein diffusivity was dependent on protein size as smaller proteins (e.g., lysozyme) diffused faster than larger proteins (e.g., Ig). Furthermore, we demonstrated that the protein physical state was preserved upon encapsulation and subsequent release from the PEG hydrogels and contained negligible aggregation or protein–polymer adducts. These initial studies indicate that the developed PEG hydrogels are suitable for release of stable proteins in drug delivery and tissue engineering applications. Biotechnol. Bioeng. 2011; 108:197–206. © 2010 Wiley Periodicals, Inc.
Mining Dense Periodic Patterns in Time Series Data

Existing techniques to mine periodic patterns in time series data are focused on discovering full-cycle periodic patterns from an entire time series. However, many useful partial periodic patterns are hidden in long and complex time series data. In this paper, we aim to discover the partial periodicity in local segments of the time series data. We introduce the notion of character density to partition the time series into variable-length fragments and to determine the lower bound of each character’s period. We propose a novel algorithm, called DPMiner, to find the dense periodic patterns in time series data. Experimental results on both synthetic and real-life datasets demonstrate that the proposed algorithm is effective and efficient to reveal interesting dense periodic patterns.
Photoprotective role of epidermal melanin granules against ultraviolet damage and DNA repair in guinea pig skin.

We previously developed a quantitative autoradiographic technique with special forceps for measuring unscheduled DNA synthesis (UDS) in mouse skin after treatment with ultraviolet light in vivo. By this method, we investigated the relationship between the protective role of melanin and UV-induced DNA repair in black-and-white guinea pigs. Flat areas containing a sharp border between pigmented and unpigmented skin were selected. The skin of the selected areas was shaved and irradiated with short-wave UV (254 nm) or UV-AB (270 to 440 nm, emission peak at 312 nm) at various doses. Immediately after irradiation, the skin was clamped off with forceps, and an isotonic aqueous solution of [methyl-3H]thymidine was injected s.c. into the clamped off portion. UDS was clearly demonstrated as silver grains in this portion of the skin after irradiation with 254 nm UV or UV-AB. Errors due to individual differences were avoided by comparing the intensities of UDS in basal cells from pigmented skin and unpigmented skin of the same animals. Unexpectedly, in groups of animals treated with 254 nm UV or UV-AB, no difference in UDS in pigmented and unpigmented skin was seen at any UV dose. These results suggested that epidermal melanin granules do not significantly protect DNA of basal cells against 254 nm UV or UV-AB irradiation. Results of a study on the effect of the wavelength of irradiation on the UDS response of albino guinea pigs are also reported.
Microstructure and Deformation Characteristics of X80 High Deformability Pipeline Steel

Two kinds of X80 high deformability pipeline steels have been processed by applying two-stage cooling process (TSC) and heat treatment on-line process (HOP). The microstructure of TSC steel and HOP steel are polygonal ferrite (PF) + quasi-polygonal ferrite (QF) + granular bainite (GB) multiphase and QF + GB+ martensite-austenite (M/A) multiphase respectively. In HOP steel, the volume of M/A is much more and the size is much larger than that in TSC steel. Some degenerated M/A constituents are also observed in HOP steel. The HOP steel has shown higher tensile strength, lower yield ratio and lower uniform elongation than TSC steel. The strain-nI (instantaneous n-value) curve of HOP steel could be divided into two stages and the TSC’s could be separated to three stages.
Forward-backward pursuit algorithm for Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a powerful imaging technique that allows dynamically and three-dimensionally resolving the metabolic process of radiopharmaceuticals. It uses optical method to detect radiopharmaceuticals with low cost and high sensitivity. However, because of the strong absorption and scatter of biological tissues, the reconstruction of CLT is always converted to an ill-posed linear system which is hard to solve. An accurate and fast reconstruct algorithm becomes a current issue. The traditional reconstruction algorithm based on l2 norm regularization is too smooth and with low accuracy. Some novel sparse reconstruction algorithm has satisfying accuracy and convergence rate, but lose its accuracy for multi-source situation. In this work, a novel CLT method based on forward-backward greedy algorithm is proposed to solve the ill-posed problem. Digital simulations and in vivo experiment were conducted to test the algorithm. The reconstruct results were compared with traditional orthogonal matching pursuit (OMP) algorithm and Tikhonov algorithm. Both the Digital simulations and in vivo experiment show that this approach can reconstruct the distribution of radiopharmaceuticals effectively and accurately.
Bayesian computational models for inharmonicity in musical instruments

In this paper we describe recent advances in harmonic models for musical signal analysis. In particular we consider cases where inharmonic musical instruments are present. A particular model corresponding to a plucked string instrument is adopted and its parameters are automatically estimated from the data. In addition we describe new Bayesian prior distributions for musical pitch, which allow automatic adjustment to the pitch of the instruments, adaptive modelling of harmonic decay across frequency and automatic selection of the basis function window length. The methods are implemented using sophisticated MCMC algorithms and results demonstrated on a small set of examples, including harpsichord and guitar. The methods are readily adapted to polyphonic settings for pitch transcription and can be expected to lead to improvements in that setting.