Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple model for sequence prediction based on dendritic spatiotemporal integration

Recent experiments on dendritic spatiotemporal integration reveal the much bigger computational potential of a single neuron. An individual dendritic branch can work as a coincidence detector due to a dendritic spike initiated with locally spatially and temporally activated synapses. Here, we investigate a proposed idea that dendrites can perform temporal integration on behavior timescale ~1s, thus weakening simultaneous activation constraint. We construct the model of the recurrent neural network where each neuron activates not as a weighted summation of inputs, but due to their coincident activation both in space and time. We show that with using sparse distributed representation and tracking activity of the network in a certain time window it is possible to achieve a high capacity prediction system. We perform the theoretical analysis and estimate the capacity for the different parameters of the model where even the network with 100 neurons can store millions of sequences. Such a capacity results in a biologically unrealistic high number of synapses, much more than 100×100. However, this mechanism of tracking space-time coincidences in sparse activation can be realized in a limited biological neural network but still with a good sequence transition memory.
A model for global biomass burning in preindustrial time : LPJ-LMfire ( v 1 . 0 ) Journal Item

Fire is the primary disturbance factor in many terrestrial ecosystems. Wildfire alters vegetation structure and composition, affects carbon storage and biogeochemical cycling, and results in the release of climatically relevant trace gases including CO 2, CO, CH4, NOx, and aerosols. One way of assessing the impacts of global wildfire on centennial to multi-millennial timescales is to use process-based fire models linked to dynamic global vegetation models (DGVMs). Here we present an update to the LPJ-DGVM and a new fire module based on SPITFIRE that includes several improvements to the way in which fire occurrence, behaviour, and the effects of fire on vegetation are simulated. The new LPJ-LMfire model includes explicit calculation of natural ignitions, the representation of multi-day burning and coalescence of fires, and the calculation of rates of spread in different vegetation types. We describe a new representation of anthropogenic biomass burning under preindustrial conditions that distinguishes the different relationships between humans and fire among hunter-gatherers, pastoralists, and farmers. We evaluate our model simulations against remotesensing-based estimates of burned area at regional and global scale. While wildfire in much of the modern world is largely influenced by anthropogenic suppression and ignitions, in those parts of the world where natural fire is still the dominant process (e.g. in remote areas of the boreal forest and subarctic), our results demonstrate a significant improvement in simulated burned area over the original SPITFIRE. The new fire model we present here is particularly suited for the investigation of climate–human–fire relationships on multimillennial timescales prior to the Industrial Revolution.
A Robust Cryptographic Approach using Multilevel Key Sharing Paradigm

Cloud computing is a popular technology that provides services to the users on demand and on pay-per-usage fee that is they only pay for the data utilized when required. With the vast growth in the use of mobile phone applications, the users are relying on their phones for their personal as well as professional work and suffering from many problems (storage, processing, security etc). To overcome these limitations and growth in the use of cloud applications, a new development area has emerged recently called as Mobile cloud computing. Mobile cloud computing is an integration of three technologies cloud computing, mobile computing and internet, enabling the users to access the services at any time and from any place. Mobile phones are sensitive devices and the personal data is not secured when user stores data on cloud and can be easily attacked by unauthorized person. This paper presents a two level encryption through a mobile application that encrypts the data before moving it to the cloud that ensures the security and the users authentication.
Physical and chemical consequences of artificially deepened thermocline in a small humic lake – a paired whole-lake climate change experiment

Abstract. Climate change with higher air temperatures and changes in cloud cover, radiation and wind speed alters the heat balance and stratification patterns of lakes. A paired whole-lake thermocline manipulation experiment of a small (0.047 km2) shallow dystrophic lake (Halsjarvi) was carried out in southern Finland. A thermodynamic model (MyLake) was used for both predicting the impacts of climate change scenarios and for determining the manipulation target of the experiment. The model simulations assuming several climate change scenarios indicated large increases in the whole-lake monthly mean temperature (+1.4–4.4 °C in April–October for the A2 scenario), and shortening of the length of the ice covered period by 56–89 days. The thermocline manipulation resulted in large changes in the thermodynamic properties of the lake, and those were rather well consistent with the simulated future increases in the heat content during the summer-autumn season. The manipulation also resulted in changes in the oxygen stratification, and the expansion of the oxic water layer increased the spatial extent of the sediment surface oxic-anoxic interfaces. In addition, the experiment affected several other chemical constituents; concentrations of organic carbon, TotN, and NH4 showed a statistically significant decrease, likely due to both changes in hydrological conditions during the experiment period and increased decomposition and sedimentation. In comparison with the results of a similar whole-lake manipulation experiment in a deep, oligotrophic, clear-watered lake in Norway, it is evident that shallow dystrophic lakes, common in the boreal region, are more sensitive to physical perturbations. This means that projected climate change may modify their physical and chemical conditions in the future.
Spin Resolved Analysis on Electronic Structural Properties of Zinc Oxide Nanosheet Attached to Ni Electrodes with Carbon Sheet for Comparison

We explored spin dependent electronic structural properties of two dimensional systems in the form of periodic and device structures. As a two dimensional system we mainly focused on ZnO sheets. In addition to ZnO sheets, C sheets were also examined in order to make a comparison in terms of spin dependent electronic structural properties. In the device structures, they were placed between two homogenous ferromagnetic Nickel (Ni) electrodes that helped us to investigate and analyze possible effects concerning the spin dependent behavior or spin polarization. Besides, induced magnetic moment was considered as well to reveal the magnetic properties of these two dimensional systems. In addition to perfect structures, we also studied the effect of distortion and doping atoms introduced in the sheets. It was observed that both the distortion and dopants provided by transition metal atoms have a crucial role on the electronic structural properties. Using ferromagnetic electrodes, distortion and dopants, one can play with spin dependent behavior needed in developing spintronic devices.
The Effect of Short-Term Antidepressant Treatment on Serum Levels of Nesfatin-1, Nitric Oxide and Ghrelin in Patients with Major Depressive Disorder *

Objective: Recent studies have shown that major depressive disorders (MDD) have important effects on nesfatin-1, nitric oxide (NO) and ghrelin hormones. To understand the possible roles of nesfatin-1, NO and ghrelin on effectiveness of MDD treatment, the effects of short-term antidepressant venlafaxine treatment on these parameters in patients with MDD were evaluated. Material and Methods: Sixty subjects (20 MDD patients and 40 healthy controls) enrolled in the study. Venlafaxine (225 mg/day for 3 months) was given to patients. Fasting blood samples were taken at onset of the study and at the end of the 3 months. Biochemical parameters, serum ghrelin, nesfatin-1 and NO were analysed. Results: After 3 months treatment, the patients prognosis, evaluated by the HAM-D scale, improved from 36.6±1 to 15.9±0.6 (P=0.04). Nesfatin-1 (p=0.001) and ghrelin (P=0.001) levels decreased and NO levels increased significantly (P=0.04) after treatment in MDD patients. Conclusions: Decreased nesfatin-1, and increased NO levels comparing to their normal levels after venlafaxine application could be an important criteria to evaluate patients’ response to treatment and also prognosis in addition to the improvement of HAM-D scales.
Unsupervised speaker adaptation based on sufficient HMM statistics of selected speakers

Describes an efficient method for unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient HMM statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are required for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal speaker cluster because the clustering result is determined according to test speaker's data on-line. Experimental results show that the proposed method attains better improvement than MLLR from the speaker independent model. Moreover the proposed method utilizes only one unsupervised sentence utterance, while MLLR usually utilizes more than ten supervised sentence utterances.
Creating a Non-Word List to Match 226 of the Snodgrass Standardised Picture Set

Creating non-word lists is a necessary but time consuming exercise often needed when conducting behavioural language tasks involving lexical decision-making or non-word reading. The following article describes the process whereby we created a list of 226 non-words matching 226 of the Snodgrass picture set (Snodgrass & Vanderwart, 1980). The non-words were matched for number of syllables, stress pattern, number of phonemes, bigram count and presence and location of the target sound when relevant.
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition

Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.
Low-threshold surface-passivated photonic crystal nanocavity laser

The efficiency and operating range of a photonic crystal laser are improved by passivating the In–GaAs quantum well gain medium and GaAs membrane using a (NH4)S treatment. The passivated laser shows a fourfold reduction in nonradiative surface recombination rate, resulting in a fourfold reduction in lasing threshold. A three-level carrier dynamics model explains the results and shows that typical parameters of such lasers lead to a lasing threshold as much determined by surface recombination as by the overall impact of the cavity quality factor. Surface passivation therefore appears crucial in operating such lasers under practical conditions.
NOVEL RESOURCE EFFICIENT CIRCUIT DESIGNS FOR REBOOTING COMPUTING

OF THESIS NOVEL RESOURCE EFFICIENT CIRCUIT DESIGNS FOR REBOOTING COMPUTING CMOS based computing is reaching its limits. To take computation beyond Moores law (the number of transistors and hence processing power on a chip doubles every 18 months to 3 years) requires research explorations in (i) new materials, devices, and processes, (ii) new architectures and algorithms, (iii) new paradigms. The focus is on fundamental new ways to compute under the umbrella of rebooting computing. Therefore, this thesis highlights explicitly Quantum computing and Adiabatic logic, two new computing paradigms that come under the umbrella of rebooting computing. Quantum computing is investigated for its promising application in high-performance computing. The first contribution of this thesis is the design of two resource-efficient designs for quantum integer division. The first design is based on non-restoring division algorithm and the second one is based on restoring division algorithm. Both the designs are compared and shown to be superior to the existing work in terms of T-count and T-depth. The proliferation of IoT devices which work on low-power also has drawn interests to the rebooting computing. Hence, the second contribution of this thesis is proving that Adiabatic Logic is a promising candidate for implementation in IoT devices. The adiabatic logic family called Symmetric Pass Gate Adiabatic Logic (SPGAL) is implemented in PRESENT-80 lightweight algorithm. Adiabatic Logic is extended to emerging transistor devices such as FinFET, TFET and UTB-SOI.
Automatically Categorizing Software Technologies

Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called <inline-formula><tex-math notation="LaTeX">$\mathrm{Witt}$</tex-math><alternatives><mml:math><mml:mi> Witt </mml:mi></mml:math><inline-graphic xlink:href="nassif-ieq1-2836450.gif"/></alternatives></inline-formula> for the categorization of software technologies (an expanded version of the hypernym discovery problem). <inline-formula><tex-math notation="LaTeX">$\mathrm{Witt}$</tex-math><alternatives><mml:math><mml:mi> Witt </mml:mi></mml:math><inline-graphic xlink:href="nassif-ieq2-2836450.gif"/></alternatives></inline-formula> takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., web application frameworks). Our approach relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms. We compared <inline-formula><tex-math notation="LaTeX">$\mathrm{Witt}$</tex-math><alternatives><mml:math><mml:mi> Witt </mml:mi></mml:math><inline-graphic xlink:href="nassif-ieq3-2836450.gif"/></alternatives></inline-formula> with six independent taxonomy tools and found that, when applied to software terms, <inline-formula><tex-math notation="LaTeX">$\mathrm{Witt}$</tex-math><alternatives><mml:math><mml:mi> Witt </mml:mi></mml:math><inline-graphic xlink:href="nassif-ieq4-2836450.gif"/></alternatives></inline-formula> demonstrated better coverage than all evaluated alternative solutions, without a corresponding degradation in false positive rate.
The Impact of Stratospheric Circulation Extremes on Minimum Arctic Sea Ice Extent

Given the rapidly changing Arctic climate, there is an urgent need for improved seasonal predictions of Arctic sea ice. Yet, Arctic sea ice prediction is inherently complex. Among other factors, wintertime atmospheric circulation has been shown to be predictive of summertime Arctic sea ice extent. Specifically, many studies have shown that the interannual variability of summertime Arctic sea ice extent (SIE) is anticorrelated with the leading mode of extratropical atmospheric variability, the Arctic Oscillation (AO), in the preceding winter. Given this relationship, the potential predictive role of stratospheric circulation extremes and stratosphere–troposphere coupling in linking the AO and Arctic SIE variability is examined. It is shown that extremes in the stratospheric circulation during the winter season, namely, stratospheric sudden warming (SSW) and strong polar vortex (SPV) events, are associated with significant anomalies in sea ice concentration in the Barents Sea in spring and along the Eurasian coastline in summer in both observations and a fully coupled, stratosphere-resolving general circulation model. Consistent with previous work on the AO, it is shown that SSWs, which are followed by the negative phase of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the AO at the surface, result in sea ice loss, although the mechanisms in the Barents Sea and along the Eurasian coastline are different. The analysis suggests that the presence or absence of stratospheric circulation extremes in winter may play a nontrivial role in determining total September Arctic SIE when combined with other factors.
Richness and distribution of sponge megabenthos in continental margin canyons off southeastern Australia

Submarine canyons are spectacular topographical features that intersect the continen- tal margins of the world's oceans. Canyons comprise unique habitats in terms of complexity, instabil- ity, material processing, and hydrodynamics, and they may support diverse assemblages of larger epibenthos. Yet, quantitative data on the biodiversity of the megabenthos in canyons are scant. Con- sequently, we quantified the diversity of sponges (a key and dominant group of the megabenthos) in 5 canyons located on the continental margin off southeastern Australia at depths from 114 to 612 m. The canyons harboured a rich sponge fauna, with a total of 165 species, belonging to 65 genera, 41 families, 10 orders, and 2 classes in 14 sled samples. Species richness declined with depth, but was positively linked to spatial heterogeneity of bottom types. Areas comprised of a broader range of bot- tom types (e.g. mixed rocky and sandy/muddy bottoms) contained more species than areas with more uniform substratum properties. Spatial patterns of the sponge assemblages were characterized by (1) high species turnover both between sites in individual canyons and between different canyons, and (2) low levels of site occupancy of the component species, with most species recorded from sin- gle canyons only. Variations in depth, substratum type and topographic relief resulted in heteroge- neous environmental conditions of benthic habitats in canyons that corresponded to changes in the assemblage structure of sponges. A broad comparison with other abrupt topographical features in the bathyal zone of the region suggests that canyon assemblages may rival the diversity of sponges on seamounts. Site-to-site variation in diversity and species composition within individual canyons sug- gests that biological patterns may be finer-grained than the spatial scale of conventional geomorpho- logical units. Consequently, from a perspective of conservation planning, a single or a few canyons are unlikely to accurately represent the regional faunal diversity, because of the strong biotic separation of communities between canyons and the limited distributional ranges of the component species.
The U.S. National Lightning Detection NetworkTM and Applications of Cloud-to-Ground Lightning Data by Electric Power Utilities

Lightning is a significant cause of interruptions or damage in almost every electrical or electronic system that is exposed to thunderstorms. The problem is particularly severe for electric power utilities that have exposed assets covering large areas. We summarize the basic properties of cloud-to-ground (CG) lightning, the primary hazard to structures on the ground, and then we discuss methods of detecting and locating such discharges. We describe the US National Lightning Detection Network/sup TM/ (NLDN), a system that senses the electromagnetic fields that are radiated by individual return strokes in CG flashes. This network provides data on the time of such strokes, their location and polarity and an estimate of the peak current. We discuss the network detection efficiency and location accuracy and some of the limitations that are inherent in any detection system that operates with a finite number of sensors with fixed trigger thresholds. We also discuss how NLDN data have benefited utilities by providing lightning warnings in real time and information on whether CG strokes are the cause of faults, documenting the response of fixed assets that are exposed to lightning, and quantifying the effectiveness of lightning protection systems. We conclude with some general observations on the use of lightning data by power utilities and we provide some guidelines on the uncertainties in lightning parameters that are acceptable in the industry.
BI-FIT : The fit between Business Intelligence end-users , tasks and technologies

This paper aims at investigating the factors that influence the ―fit‖ between Business Intelligence (BI) end-users, tasks and technologies (BI-FIT). Based on an extensive literature study on the elements of BI-FIT, in this research a BI end-user segmentation model is developed that shows the most relevant factors and the interrelationships between BI end-users, tasks and technologies. This model can be used to help organizations to identify and fulfill the needs of BI endusers, thereby improving adoption and increasing satisfaction of the BI end-user base. The BI-FIT model is evaluated empirically through a multiple case study to enrich and validate the theory.