Miracle-dz/UniFAR-FTUs huggingface dataset:
The FL-SMIA Network: A Novel Architecture for Time Series Prediction

In this paper we propose the FL-SMIA model, a novel neural network model that combines the principles of the Functional Link Neural Network (FLNN) with the Self-organizing Multilayer Neural Network using the Immune Algorithm (SMIA). We describe the FL-SMIA architecture and operation and evaluate its predictive performance on different financial time series in comparison to other neural network models. The FL-SMIA model combines the higher-order inputs of the tensor-product FLNN, i.e. the products of raw input features, with the self-organizing hidden layer of SMIA that dynamically grows and adapts to the input vectors. The FL-SMIA has two advantages over other models. First, it can dynamically adapt to growing amounts of data with a model that grows increasingly complex. Second, it keeps an explicit representation of the patterns it recognises in the data. Experimental results show that the FL-SMIA improves performance, as measured by annualised return in five-days-ahead and one-day-ahead prediction tasks for share prices and exchange rates, over the SMIA networks alone and over standard multilayer perceptrons. It performs on the same level as the FLNN, sometimes better but not significantly so. The result that FLNN and FL-SMIA outperform other multilayer models indicates that particularly the higher-order features contribute to the improved performance and motivate further research into mixed neural network architectures for financial time series prediction.
Communication between different sub-systems of MRI

The Magnetic Resonance Imaging (MRI) scanner is one of the sophisticated clinical devices available. With Make in India initiative of Gov. of India, development of Indigenous MRI system is being started at SAMEER, India. In MR imaging data corresponding to MR signal is transmitted from a radio frequency (RF) receive coil to the MRI data processor. But this complete chain is embedded by various sub-systems viz. Patient support system, magnet, cooling systems, RF Coils and Amplifiers, Gradient Coils and amplifiers, Spectrometer receiver. For proper working of system, all the sub systems share their data with each other. Ethernet TCP/IP protocol is used to communicate between these sub-systems considering reliability of this protocols to sustain in magnetic environment. The strategy is to design sequence of operations in form of events which can be understood by controllers of all the sub-systems.
Music chord recognition from audio data using bidirectional encoder-decoder LSTMs

In this paper, we discuss some methods for chord recognition based on long short-term memory recurrent neural networks (LSTM, LSTM-RNN). Chord progressions play an important role in the generation process of music. Actually, music processing systems containing a model for chord progressions achieve high accuracies in tasks like music structure analysis, multi pitch analysis an automatic composition or accompaniment. In previous research, chord progressions were obtained rule- based or have been modeled using stochastic methods like hidden Markov models or probabilistic context-free grammars. Pitch patterns were then regarded as the observations resulting from the hidden states of the chord progression model. Recently, con- volutional neural networks have been used for chord recognition with considerable success. On the other hand, LSTM networks have been shown to be suitable for generating chord progressions, since these neural networks can process time series data very well. The purpose of this study is to evaluate and compare three types of LSTM networks based on the bidirectional and encoderdecoder structure with regards to their chord recognition performance. In order to extract more effective data for chord recognition, we use a constant-Q transform and specmurt analysis to suppress overtone components, and chroma vectorization to reduce the feature dimensionality. The evaluation results show that the encoder-decoder-based LSTM can learn the relationship between the observed chroma vectors and the associated chord progression more effectively than simpler LSTM networks.
A Java Task Pool Framework providing Fault-Tolerant Global Load Balancing

Fault tolerance is gaining importance in parallel computing, especially on large clusters. Traditional approaches handle the issue on system-level. Application-level approaches are becoming increasingly popular, since they may be more efficient. This paper presents a fault-tolerant work stealing technique on application level, and describes its implementation in a generic reusable task pool framework for Java. When using this framework, programmers can focus on writing sequential code to solve their actual problem. The framework is written in Java and utilizes the APGAS library for parallel programming. It implements a comparatively simple algorithm that relies on a resilient data structure for storing backups of local pools and other information. Our implementation uses Hazelcast's IMap for this purpose, which is an automatically distributed and fault-tolerant key-value store. The number of backup copies is configurable and determines how many simultaneous failures can be tolerated. Our algorithm is shown to be correct in the sense that failures are either tolerated and the computed result is the same as in non-failure case, or the program aborts with an error message. Experiments were conducted with the UTS, NQueens and BC benchmarks on up to 144 workers. First, we compared the performance of our framework with that of a non-fault-tolerant variant during failure-free operation. Depending on the parameter settings, the overhead was at most 46.06%. The particular value tended to increase with the number of steals. Second, we compared our framework's performance with that of a related, but less flexible fault-tolerant X10 framework. Here, we did not observe a clear winner. Finally, we measured the overheads for restoring a failed worker and for raising the number of backup copies from one to six, and found both to be negligible.
Energy efficient data aggregation in sensor network using multiple sink data node

Data collection is an important task in any network. Data collection and aggregation becomes highly challenging in a wide area network with numerous nodes like sensor nodes, data nodes, and communication nodes are present. Data aggregation from these distributed nodes is an energy consuming task as sensor nodes mainly work on consumable power. Major task of any network engineer lies in performing an energy efficient data aggregation, so that he preserves the non renewable energy supply of the sensor. In the research carried out in this paper energy efficient data aggregation approach is proposed. Two network specific algorithms are devised one can cluster the network efficiently and the other performs the aggregation from the sensor node to data node. Data node is the specialized proposed node made available in the network which can be later connected to any centralized storage like cloud.
Systematic review: Effective home support in dementia care, components and impacts – Stage 2, effectiveness of home support interventions

AIM The aim of this study was to explicate the outcomes of home support interventions for older people with dementia and/or their carers to inform clinical practice, policy and research. BACKGROUND Most people with dementia receive support at home. However, components and effectiveness of home support interventions have been little explored. DESIGN Systematic review with narrative summary. DATA SOURCES Electronic searches of published studies in English using PubMed, Cochrane Central Register of Controlled Trials, PsychINFO, CINAHL, Applied Social Science Index and CSA Social Services Abstracts. Databases and sources were searched from inception to April 2014 with no date restrictions to locate studies. REVIEW METHODS The PRISMA statement was followed and established systematic review methods used. Using 14 components of care for people with dementia and their carers, identified previously, data across studies were synthesized. Interventions were grouped and described and effectiveness ratings applied. Qualitative studies were synthesized using key themes. RESULTS Seventy studies (four qualitative) were included. Most were directed to carers and of high quality. Seven interventions for carers and two for people with dementia were identified, covering 81% of studies. Those relating to daily living, cognitive training and physical activity for people with dementia were absent. Measures of effectiveness were influenced mainly by the intensity (duration and frequency) of interventions. Those containing education, social support and behaviour management appeared most effective. CONCLUSION These interventions reflect emergent patterns of home support. Research is required to identify effective interventions linked to the stage of dementia, which can be applied as part of routine clinical care.
Summarizing Movement Graph for Mobility Pattern Analysis

Understanding human mobility is the key problem in many applications such as location-based services and recommendation systems. The mobility of a smartphone user can be modeled by a movement graph, in which the nodes represent locations and the edges are distances or traveling times between the locations. However, the resulting graph would be too big to be stored and queried on resource-devices such as smartphones. In this paper, we deploy a state-of-the-art graph summarization method to produce an abstract (coarse) graph easy to be processed and queried. After summarization, the movement graph becomes smaller resulting in a reduction in the required time and storage to deploy graph algorithms. We specifically investigate the effect of summarization on two algorithms related to human mobility mining: location prediction and similarity mining. The location prediction algorithm on the coarse graph causes coarse-grain results. Regarding computing the similarity, summarization reduces the computational cost but at the same time increases the uncertainty of the results. We show that the trade-off between accuracy, storage space and speed can be controlled by the compression ratio. As an illustration, if the size of the graph is reduced to half, the similarity algorithm becomes 4 times faster while the correlation between similarities of coarse and original graphs is 0.98.
Automation of product packaging for industrial applications

ABSTRACT This work presents a robotic-based solution devised to automate the product packaging in industrial environments. Although the proposed approach is illustrated for the case of the shoe industry, it applies to many other products requiring similar packaging processes. The main advantage obtained with the automated task is that productivity could be significantly increased. The key algorithms for the developed robot system are: object detection using a computer vision system; object grasping; trajectory planning with collision avoidance; and operator interaction using a force/torque sensor. All these algorithms have been experimentally tested in the laboratory to show the effectiveness and applicability of the proposed approach.
Melanin-concentrating hormone in human and rat.

Melanin-concentrating hormone (MCH) is a neuropeptide originally isolated from chum salmon pituitaries. To explore physiological roles of MCH in mammals, we studied the regional distribution of immunoreactive MCH in the rat tissues and the presence of immunoreactive MCH in human adrenal glands, adrenal tumors and plasma by radioimmunoassay, and the expression of MCH mRNA in rat tissues and human brain tissues by Northern blot analysis. Immunoreactive MCH was present in every region of rat brain and neurointermediate lobe of the pituitary gland, with the highest concentrations found in the hypothalamus (48.3 +/- 6.6 pmol/g wet weight, mean +/- SEM, n = 6). The immunoreactive MCH in rat hypothalamus, frontal lobe, and pons and medulla oblongata was eluted in the position of synthetic human/rat MCH in reverse-phase high-performance liquid chromatography. No immunoreactive MCH was detected in the rat peripheral tissues. Northern blot analysis showed that a single species of MCH mRNA (approximately 1 kb) was expressed specifically in the rat and human hypothalamus, but not detectable in other regions of brain or rat peripheral tissues. Immunoreactive MCH was not detected in human adrenal glands (< 0.5 pmol/g wet weight, n = 9) or adrenal tumors including pheochromocytomas. Immunoreactive MCH were not detected in plasma obtained from human healthy subjects (< 0.25 pmol/l) and rat (< 0.25 pmol/l). (ABSTRACT TRUNCATED AT 250 WORDS)
Rickettsia africae and Novel Rickettsial Strain in Amblyomma spp. Ticks, Nicaragua, 2013

We report molecular detection of Rickettsia africae in Amblyomma ovale ticks from Nicaragua and a novel rickettsial strain in an A. triste tick. Of 146 ticks from dogs, 16.4% were Rickettsia PCR positive. The presence of Rickettsia spp. in human-biting ticks in Nicaragua may pose a public health concern.
State of the Art: Novel Applications for Deep Brain Stimulation

Deep brain stimulation (DBS) is a rapidly developing field of neurosurgery with potential therapeutic applications that are relevant to conditions traditionally viewed as beyond the limits of neurosurgery. Our objective, in this review, is to highlight some of the emerging applications of DBS within three distinct but overlapping spheres, namely trauma, neuropsychiatry, and autonomic physiology.
Generalizing piecewise affine system identification to local model networks

The problem of nonlinear system identification with local model networks (LMNs) is considered. Local model networks consist of a specific number of local models which are blended by validity functions to predict the output of the system. For this model type a novel data based parameterization is introduced. This parameterization allows to reformulate the identification problem as an optimization problem which is similar to piecewise affine (PWA) system identification. Therefore, different algorithms developed to solve the PWA identification problem can be used to solve the identification problem of local model networks. This is demonstrated for the popular parameter clustering algorithm. Due to the usage of validity functions the estimation of polyhedral validity regions, which usually requires the solution of a classification problem, is mitigated. Furthermore, for local model networks the new representation avoids reactivation effects since always the same factor is used in the exponent of the data based membership functions.
Understanding multiple-input multiple-output active noise control from a perspective of sampling and reconstruction

This paper formulates the multiple-input multiple- output active noise control as a spatial sampling and reconstruction problem. With the proposed formulation, the inputs from the reference microphones and the outputs of the antinoise sources are regarded as spatial samples. We show that the proposed formulation is general and can unify the existing control strategies. Three control strategies, for instance, are derived from the proposed formulation and linked to different cost functions in the practical implementation. Finally, simulation results are presented to verify the effectiveness of our analysis.
Image manipulation on social media for encryption-then-compression systems

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this paper, the EtC systems are applied to social media like Twitter, that are known for carrying out some image manipulation. Block scrambling-based encryption schemes used in the EtC systems are evaluated in terms of the robustness against image manipulation on social media. This work aims to investigate how each social networking service (SNS) provider manipulates images, and to consider whether the encrypted images uploaded to SNS providers can avoid to include some distortion under the image manipulation. In the experiment, encrypted and non-encrypted JPEG images are uploaded to various SNS providers to confirm the robustness of EtC systems. It is shown that the EtC systems are applicable to almost all SNS providers.
Guidelines for the diagnosis and management of adult aplastic anaemia

Sally B. Killick (Writing Group Chair), Nick Bown, Jamie Cavenagh, Inderjeet Dokal, Theodora Foukaneli, Peter Hillmen, Robin Ireland, Austin Kulasekararaj, Ghulam Mufti, John A Snowden, Sujith Samarasinghe, Anna Wood (BCSH Task Force Member), Judith C.W. Marsh on behalf of the British Society for Standards in Haematology. The Royal Bournemouth and Christchurch Hospitals NHS Foundation Trust, Bournemouth, Northern Genetics Service, Newcastle upon Tyne, St Bartholomew’s Hospital, Barts Health NHS Trust, London, Barts and The London School of Medicine and Dentistry, Queen Mary University of London and Barts Health NHS Trust, London, Addenbrooks Hospital, University of Cambridge, Cambridge, Leeds Teaching Hospitals, Leeds, Kings College Hospital NHS Foundation Trust, London, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield Great Ormond Street Hospital for Children NHS Foundation Trust, London, West Hertfordshire NHS Trust, Watford.
Distribution of NADPH-Diaphorase and Nitric Oxide Synthase in the Trigeminal Ganglion and Mesencephalic Trigeminal Nucleus of the Cat

The trigeminal ganglion (TrG) and mesencephalic trigeminal nucleus (MTN) neurons are involved in the transmission of orofacial sensory information. The presence of nitric oxide (NO), a putative neurotransmitter substance in the nervous system, was examined in the cat TrG and MTN using nicotinamide adenine dinucleotide phosphate diaphorase (NADPH-d) histochemistry and nitric oxide synthase (NOS) immunohistochemistry. In the TrG, where the majority of the trigeminal primary afferent perikarya are located, most of the intensely NADPH-d/ NOS-stained cells were small in size and distributed randomly throughout the ganglion. The medium-sized neurons were moderately stained. A plexus of pericellular varicose arborizations around large unstained ganglion cells and densely stained fibers in-between could also be observed. In the caudal part of the MTN, both NADPH-d activity and NOS immunoreactivity was present in MTN neurons. In addition, a few scattered NADPH-d/NOS-containing neurons were found in the mesencephalic-pontine junction part of the nucleus. In contrast, only nerve fibers and their terminals were present at a more rostral level in the mid- and rostral MTN. MTN neuronal perikarya were enveloped in fine basket-like NADPH-d/ NOS-positive networks. Differential expression patterns of NOS and its marker NADPH-d suggest that trigeminal sensory information processing in the cat MTN is controlled by nitrergic input through different mechanisms. We introduce the concept that NO can act as a neurotransmitter in mediating nociceptive and proprioceptive information from periodontal mechanoreceptors but may also participate in modulating the activity of jaw-closing muscle afferent MTN neurons.
Non-intrusive wearable health monitoring systems for emotion detection

Anxiety and Stress caused by the visit to the health centre or hospital are very common among children and adult. White Coat Syndrome or Iatrophobia can make diagnostic of a health condition difficult and providing a correct treatment challenging for healthcare providers. Various approaches such as remote monitoring and friendly clinical environment have been proposed to reduce and overcome the stress and fear. In this paper, we present a wearable non-intrusive Clinical Health Analysis and Monitoring Personal Systems (CHAMPS) that can provide vital health information to the healthcare provider to determine the health and state of the patient. We implement and evaluate the CHAMPS in a real hardware using customised sensor on 12 participants with different ages. The results have shown that CHAMPS can help to provide critical pulse rate and temperature measurement accurately and can be used to detect mood change and fever.
A New Niche for Anoxygenic Phototrophs as Endoliths

ABSTRACT Anoxygenic phototrophic bacteria (APBs) occur in a wide range of aquatic habitats, from hot springs to freshwater lakes and intertidal microbial mats. Here, we report the discovery of a novel niche for APBs: endoliths within marine littoral carbonates. In a study of 40 locations around Isla de Mona, Puerto Rico, and Menorca, Spain, 16S rRNA high-throughput sequencing of endolithic community DNA revealed the presence of abundant phylotypes potentially belonging to well-known APB clades. An ad hoc phylogenetic classification of these sequences enabled us to refine the assignments more stringently. Even then, all locations contained such putative APBs, often reaching a significant proportion of all phototrophic sequences. In fact, in some 20% of samples, their contribution exceeded that of oxygenic phototrophs, previously regarded as the major type of endolithic microbe in carbonates. The communities contained representatives of APBs in the Chloroflexales, various proteobacterial groups, and Chlorobi. The most abundant phylotypes varied with geography: on Isla de Mona, Roseiflexus and Chlorothrix-related phylotypes dominated, whereas those related to Erythrobacter were the most common in Menorca. The presence of active populations of APBs was corroborated through an analysis of photopigments: bacteriochlorophylls were detected in all samples, bacteriochlorophyll c and a being most abundant. We discuss the potential metabolism and geomicrobial roles of endolithic APBs. Phylogenetic inference suggests that APBs may be playing a role as photoheterotrophs, adding biogeochemical complexity to our understanding of such communities. Given the global extent of coastal carbonate platforms, they likely represent a very large and unexplored habitat for APBs. IMPORTANCE Endolithic microbial communities from carbonates, which have been explored for over 2 centuries in predominantly naturalistic studies, were thought to be primarily composed of eukaryotic algae and cyanobacteria. Our report represents a paradigm shift in this regard, at least for the marine environment, demonstrating the presence of ubiquitous and abundant populations of APBs in this habitat. It raises questions about the role of these organisms in the geological dynamics of coastal carbonates, including coral reefs.
Fibre Bragg Gratings for the Monitoring of Wooden Structures

The aim of this work was to develop and validate an experimental methodology suitable for analysing on-site the behaviour of fibre-reinforced wooden structures. The proposed measurement method is based on the application of fibre Bragg grating (FBG) strain sensors. An analysis of adhesive behaviour was performed preliminarily, which provided indications for choosing the type of adhesive and for the fibre bonding length in accordance with the volume of measurement. The first series of tests was carried out on wood samples to verify the coupling between the measuring sensor and the wood support when the latter is subject to mechanical stresses. The second investigation was done on site to test the behaviour of a historical wood floor before and after reinforcement by means of a series of tests performed using optical fibres with the Bragg grating. The optical fibre system measurements were compared to those obtained using a laser vibrometer, a measurement system of proven stability and precision. The comparison makes it possible to confirm the validity of the results and the reliability of the system for the monitoring of historic wooden structures.