Miracle-dz/UniFAR-FTUs huggingface dataset:
DENOISING SOURCE SEPARATION: FROM TEMPORAL TO CONTEXTUAL INVARIANCE

We have developed a general framework that allows the guidance of source separation by prior knowledge of the source characteristics. This framework is called denoising source separation (DSS) [2]. The prior knowledge can vary from very detailed knowledge to general principles resulting in blind source separation (BSS). In DSS, the source separation is achieved by decorrelating the input, projecting the input to the source level, denoising and adapting the projection according to the denoised source estimates in a Hebbian way. Additionally, DSS can be realised using online learning. Until now, we have only considered linear projections. However, DSS should generalize to nonlinear projections quite easily. We have shown that certain independent component analysis (ICA) algorithms, such as FastICA or nonlinear PCA, can be seen as special cases of DSS. In addition, DSS allows for building of the denoising in a procedural way. This is especially useful when the prior knowledge is best described hierarchically. We have, for instance, contemplated the use of speech recognition models to guide auditive source separation. Neurons actually carry out computations similar to DSS: The decorrelation in DSS implements competition between the elements resembling the lateral inhibitory connections of neurons. Furthermore, neurons learn in a Hebbian way and connect to other neurons forming projections. Finally, denoising can be seen as the central goal of neural information processing. For this reason, it does not seem unnatural that source separation type of approaches have been successful in reproducing certain features of the cells of the visual cortex. For example, when exposed to natural image data, ICA has been shown to mimic the simple cells of primary visual cortex V1. Even more important has been the reproduction of some constances of the complex cells such as translational and scaling invariance. These have been successfully implemented in slow feature analysis (SFA) [3] and bubble-ICA [1]. The acting principle in these algorithms is the slowness of the stimuli (in bubbleICA this is called temporal coherence). To us, slowness seems to be one way of achieving context dependency. However, there are examples where stimulus does not change slowly, but the context can aid in making sense of it nevertheless. Such a case is presented in recognition of written characters. Though different characters do not have any visual similarities, the context can be used to determine whether a character is capital ’O’ or number zero. In context of other letters, the character would more likely be a letter than a number and vice versa. We speculate that, in addition to slowness, it is possible to account for other types of context invariance as well in DSS framework. References
Binary Paragraph Vectors

Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection.
Performance Evaluation of RCPT Hybrid ARQ Schemes for DS-CDMA Mobile Radio over Frequency Selective Rayleigh Fading Channel (移動通信ワークショップ)

In this paper, we evaluate by computer simulations the throughput performance of the turbo-coded hybrid ARQ for direct sequence code division multiple access (DS-CDMA) with antenna diversity reception and Rake combining in a frequency selective Rayleigh fading channel. The effect of a range of system parameters and propagation parameters (viz., the frame length, puncturing rate, spreading factor, number of diversity antennas, fading rate, and the power delay profile of the multipath channel (number of paths and profile shape)) is investigated. It is found that the rate compatible punctured turbo (RCPT) coded hybrid ARQ system has the highest throughput when minimum amount of redundancy bits is transmitted with each retransmission and that the throughput is almost independent of the frame length and the fading rate. It is also found that smaller spreading factor for a given chip rate gives a higher throughput in bits/sec.
A Clustering Approach for Web Vulnerabilities Detection

This paper presents a new algorithm aimed at the vulnerability assessment of web applications following a black-box approach. The objective is to improve the detection efficiency of existing vulnerability scanners and to move a step forward toward the automation of this process. Our approach covers various types of vulnerabilities but this paper mainly focuses on SQL injections. The proposed algorithm is based on the automatic classification of the responses returned by the web servers using data clustering techniques and provides especially crafted inputs that lead to successful attacks when vulnerabilities are present. Experimental results on several vulnerable applications and comparative analysis with some existing tools confirm the effectiveness of our approach.
Empirical evaluation of segmentation algorithms for lung modelling

Lung modelling has emerged as a useful method for diagnosing lung diseases. Image segmentation is an important part of lung modelling systems. The ill-defined nature of image segmentation makes automated lung modelling difficult. Also, low resolution of lung images further increases the difficulty of the lung image segmentation. It is therefore important to identify a suitable segmentation algorithm that can enhance lung modelling accuracies. This paper investigates six image segmentation algorithms, used in medical imaging, and also their application to lung modelling. The algorithms are: normalised cuts, graph, region growing, watershed, Markov random field, and mean shift. The performance of the six segmentation algorithms is determined through a set of experiments on realistic 2D CT lung images. An experimental procedure is devised to measure the performance of the tested algorithms. The measured segmentation accuracies as well as execution times of the six algorithms are then compared and discussed.
Evaluating Different Machine Learning Methods for Upscaling Evapotranspiration from Flux Towers to the Regional Scale

Evapotranspiration (ET) is a vital variable for land‐atmosphere interactions that links surface energy balance, water, and carbon cycles. The in situ techniques can measure ET accurately but the observations have limited spatial and temporal coverage. Modeling approaches have been used to estimate ET at broad spatial and temporal scales, while accurately simulating ET at regional scales remains a major challenge. In this study, we upscale ET from eddy covariance flux tower sites to the regional scale with machine learning algorithms. Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine. The machine learning methods are trained and tested at 36 flux towers sites (65 site years) across the Heihe River Basin and are then applied to estimate ET for each grid cell (1 km × 1 km) within the watershed and for each day over the period 2012–2016. The artificial neural network, Cubist, random forest, and support vector machine algorithms have almost identical performance in estimating ET and have slightly lower root‐mean‐square error than deep belief network at the site scale. The random forest algorithm has slightly lower relative uncertainty at the regional scale than other methods based on three‐cornered hat method. Additionally, the machine learning methods perform better over densely vegetated conditions than barren land or sparsely vegetated conditions. The regional ET generated from the machine learning approaches captured the spatial and temporal patterns of ET at the regional scale.
A comprehensive analysis of hail events in Portugal: Climatology and consistency with atmospheric circulation

A first hail climatology in mainland Portugal is provided, based on a network of 15 meteorological stations and over the period of 1971–2009 (39 years). Three hail sub‐classes based on the SYNOP codes were considered (small hail, hail without thunder and hail with thunder). Furthermore, hail occurrences were also compared with thunderstorm, convection and precipitation days. Hail events are more frequent in northern Portugal during winter and spring (peaking in April). Both thunderstorm and convection events present a maximum in April–May and a secondary peak in autumn (October). This secondary peak is frequently associated with convective storms without hailfall at the ground, as no clear autumnal maximum is recorded in the hail frequency, particularly in the case of small hail and hail without thunder. The typically higher temperatures in October than in February–April may lead to excessively high‐freezing level heights that hamper hailfall at the surface. The dynamical mechanisms underlying hail occurrence in mainland Portugal were also analysed using eight three‐dimensional weather types. The atmospheric conditions associated with three weather types: Western European Trough, Blocking and Scandinavian Trough were responsible for ca. 75% of the hail events throughout Portugal. Overall, hail events are favoured either by extra‐tropical depressions, with cold front passages over Portugal, or by upper‐level troughs/lows over western Iberia, but preferably occurring in the period from December to May. These dynamical drivers promote instability conditions, which were also diagnosed by the convective available potential energy and total‐totals index.
Projection based whole body motion planning for legged robots

In this paper we present a new approach for dynamic motion planning for legged robots. We formulate a trajectory optimization problem based on a compact form of the robot dynamics. Such a form is obtained by projecting the rigid body dynamics onto the null space of the Constraint Jacobian. As consequence of the projection, contact forces are removed from the model but their effects are still taken into account. This approach permits to solve the optimal control problem of a floating base constrained multibody system while avoiding the use of an explicit contact model. We use direct transcription to numerically solve the optimization. As the contact forces are not part of the decision variables the size of the resultant discrete mathematical program is reduced and therefore solutions can be obtained in a tractable time. Using a predefined sequence of contact configurations (phases), our approach solves motions where contact switches occur. Transitions between phases are automatically resolved without using a model for switching dynamics. We present results on a hydraulic quadruped robot (HyQ), including single phase (standing, crouching) as well as multiple phase (rearing, diagonal leg balancing and stepping) dynamic motions.
Evaluation of MP-SoC interconnect architectures: a case study

Multiprocessor (MP-SoC) platforms are emerging as the latest trend in SoC design. These MP-SoCs consist of a large number of IP blocks in the form of functionally heterogeneous embedded processors. In this design paradigm, IP blocks need to be integrated using a structured interconnect template, for example, according to high-performance parallel computing architectures. A formal evaluation process is required before adopting a specific parallel architecture to SoC domain. Here, we propose an evaluation methodology based on performance metrics that include latency, throughput and silicon area requirements. As a case study, we present the results of such an evaluation for two MP-SoC interconnect topologies, i.e., the mesh and the butterfly fat-tree (BFT). This evaluation methodology can be extended to any other SoC interconnect topology without loss of generality.
Bioreactor strategies for improving production yield and functionality of a recombinant human protein in transgenic tobacco cell cultures

Plant cell culture production of recombinant products offers a number of advantages over traditional eukaryotic expression systems, particularly if the product can be targeted to and purified from the cell culture broth. However, one of the main obstacles is product degradation by proteases that are produced during cell culture, and/or the loss of biological activity of secreted (extracellular) products as a result of alteration in the protein conformation. Because proteolysis activity and target protein stability can be significantly influenced by culture conditions, it is important to evaluate bioprocess conditions that minimize these effects. In this study, a bioreactor strategy using a protocol involving pH adjustment and medium exchange during plant cell culture is proposed for improving the production of functional recombinant α1‐antitrypsin (rAAT), a human blood protein, produced using several alternative expression systems, including a Cauliflower mosaic virus (CaMV) 35S constitutive promoter expression system, a chemically inducible, estrogen receptor‐based promoter (XVE) expression system, and a novel Cucumber mosaic virus (CMV) inducible viral amplicon (CMViva) expression system developed by our group. We have demonstrated that higher medium pH help reduce protease activity derived from cell cultures and improve the inherent stability of human AAT protein as well. This strategy resulted in a fourfold increase in the productivity of extracellular functional rAAT (100 µg/L) and a twofold increase in the ratio of functional rAAT to total rAAT (48%) in transgenic N. benthamiana cell cultures using a chemically inducible viral amplicon expression system. Biotechnol. Bioeng. 2009;102: 508–520. © 2008 Wiley Periodicals, Inc.
A Proposal of Four-Panel Cartoon CAPTCHA

A CAPTCHA is a technique that is used to prevent automated programs from being able to acquire free e-mail or online service accounts. However, as many researchers have already reported, conventional CAPTCHAs could be overcome by state-of-the-art malwares since the capabilities of computers are approaching those of humans. Therefore, CAPTCHAs should be based on even more advanced human-cognitive-processing abilities. In addition, it is also important to keep in mind that answering CAPTCHAs is an added annoyance for users, who feel troublesome to prove that they are humans at every Web accesses. So, CAPTCHAs should be enjoyable for users. To cope with these issues, we have focused on the human ability to understand humor which is considered one of the most advanced human cognitive processing abilities, and already proposed the concept of a new type of Turing test that uses four-panel cartoons, which would make CATPCHAs fun and enjoyable. So in this paper, we carry out experimental studies to confirm the usability of the proposed CAPTCHA.
A Distributed Game-Theoretic Approach for IaaS Service Trading in an Auction-Based Cloud Market

With the rapid development of IaaS market, how to efficiently trade between cloud providers and users is becoming a new challenge which attracts huge attentions from both industry and academia. Compared with traditional fixed-price model, market-oriented trading mechanism such as auction demonstrates greater promise for resource pricing and allocation in clouds due to its adaptability and flexibility. In this paper, we focus on the competitive and bidding scenario among independent users in an auction-based IaaS market. Participants have different composite service demands and they adjust their bidding prices during the trading with the aim of obtaining appropriate amount of resources to maximize their profits. We formulate this scenario as a dynamic noncooperative game with incomplete information and propose a feedback-based distributed bidding adjustment approach to find the approximated optimal bids (i.e. Nash Equilibrium) for each user so as to achieve a fair and multi-win resources allocation outcome in the whole market. Our experimental investigation showed that with the help of our agent-based automated trading model and distributed bidding algorithms, the Nash equilibrium state of the cloud resource allocation game can be reached efficiently within an acceptable time, and the optimal bidding prices of each user could be obtained at the same time.
Finding bugs is easy

Many techniques have been developed over the years to automatically find bugs in software. Often, these techniques rely on formal methods and sophisticated program analysis. While these techniques are valuable, they can be difficult to apply, and they aren't always effective in finding real bugs.Bug patterns are code idioms that are often errors. We have implemented automatic detectors for a variety of bug patterns found in Java programs. In this paper, we describe how we have used bug pattern detectors to find serious bugs in several widely used Java applications and libraries. We have found that the effort required to implement a bug pattern detector tends to be low, and that even extremely simple detectors find bugs in real applications.From our experience applying bug pattern detectors to real programs, we have drawn several interesting conclusions. First, we have found that even well tested code written by experts contains a surprising number of obvious bugs. Second, Java (and similar languages) have many language features and APIs which are prone to misuse. Finally, that simple automatic techniques can be effective at countering the impact of both ordinary mistakes and misunderstood language features.
Design of a microstripe bandpass filter with a wide stopband

This paper develops a novel microstrip bandpass filter with a wide stopband. With the assistance of open stubs and one open-circuit coupled line using symmetrical feeding, a wide stopband with a sufficient rejection level can be achieved. Detail design and synthesis procedures are provided. Moreover, the electromagnetic simulator IE3D is used, and the prototype of the bandpass filter is fabricated and measured. Good agreement between measured and simulated results demonstrates feasibility of the proposed filter.
Generalized DC voltage regulation strategy for η:1 relation cascade H-bridge converter-based STATCOM

This paper presents a generalized DC voltage regulation strategy, for both symmetric (η=1) and asymmetric (η=2,3) multilevel converters used in a static VAR compensator. The objective is to keep regulated the DC links and the reactive power compensation. The strategy is applied to a single phase converter with a combination of staircase and PWM technique. Simulation results on PSIM are presented showing a good performance of proposed control strategy.
Rapid G0/1 transition and cell cycle progression in CD8+ T cells compared to CD4+ T cells following in vitro stimulation

T‐cell population consists of two major subsets, CD4+ T cells and CD8+ T cells, which can be distinguished by the expression of CD4 or CD8 molecules, respectively. Although they play quite different roles in the immune system, many of their basic cellular processes such as proliferation following stimulation are presumably common. In this study, we have carefully analyzed time–course of G0/1 transition as well as cell cycle progression in the two subsets of quiescent T‐cell population following in vitro growth stimulation. We found that CD8+ T cells promote G0/1 transition more rapidly and drive their cell cycle progression faster compared to CD4+ T cells. In addition, expression of CD25 and effects of its blockade revealed that IL‐2 is implicated in the rapid progression, but not the earlier G0/1 transition, of CD8+ T cells.
Nanocarrier: A potential tool for future antioxidant therapy

Abstract Antioxidants are believed to have great potential for the treatment of diseases that are induced by oxidative stress. However, their pharmaceutical application is severely limited by their poor bioavailability and low biocompatibility. To solve this problem, the antioxidants can be encapsulated or covalently linked with nanomaterials to form nanoantioxidants. In this review, we aim to summarize the use of nanocarriers for antioxidant delivery and discuss their enhancement of antioxidant activity. We also suggest future research directions toward bringing nanoantioxidants into the realm of clinical applications.
Speed Up Strategies for the Creation of Multimodal and Multilingual Dialogue Systems

In this paper we will summarize the work done in the PhD thesis that follows the same title as this document. In the thesis we propose different innovative, dynamic, and intelligent acceleration strategies applied to a development platform for reducing the design time of multimodal and multilingual dialogue systems and to improve the runtime modules. Throughout the paper we will describe the three different kinds of accelerations proposed, which are innovative with respect to current commercial and research platforms. The first kind of strategies was applied to the design platform in order to allow the prediction of the information required to complete the different aspects of the service. These strategies are mainly based on using the data model structure and database contents, as well as cumulative information obtained from the previous and sequential steps in the design. Thanks to them, the design is reduced, most of the times, to simple confirmations from the designer to the “proposals” that the platform automatically provides. The second kind of strategies is the incorporation of a new adaptation algorithm to the language models used by a machine translation system that automatically translates system’s prompts (in audio or text) into an animated sequence in Sign Language for providing the designed service to deaf users using an avatar. Finally, the third kind is an innovative LID technique based on using a discriminative ranking of ngrams that allows the incorporation of contextual longer-span information into the language models used to identify the system needs to use to interact with the users of the service. Index Terms: Dialogue Systems, Language Identification, Language Model Adaptation, Machine Translation.
Assessment of LST and NDMI indices using MODIS and Landsat images in Karun riparian forest

Riparian forest plays a significant role in ecosystems. Also, research on land surface temperature and soil moisture is essential in earth science and forest studies. Because measuring methods are difficult to apply in large areas and especially in dense forests, in this study normalized difference moisture index (NDMI) and land surface temperature (LST) were estimated using the infrared thermal method by data of Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) in the Karun riparian forest that is of ecological importance in the Khuzestan province of Iran. The results showed that the accuracy for estimated NDMI and LST was appropriate (root mean square error = 3.45). In addition, the used polynomial support vector machine algorithm for classification by four classes (forest, agriculture, river, and others) and the validity of classification in these areas were suitable (overall accuracy = 95%, kappa coefficient = 0.93). Also, the NDMI index was dependent on changes in LST and Pearson coefficients were 0.94 and 0.84 for Landsat 8 and MODIS data, respectively. The average temperature of the area was obtained as 43.22 and 42.77 for Landsat 8 and MODIS, respectively. Finally, more protection of this forest against LST enhancement and reduction in soil moisture is necessary.