Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Biomass Retrieval From L-Band SAR Using Tomographic Ground Backscatter Removal

A tomographic synthetic aperture radar (TomoSAR) represents a possible route to improved retrievals of forest parameters. Simulated orbital L-band TomoSAR data corresponding to the proposed Satellites for Observation and Communications-Companion Satellite (SAOCOM-CS) mission (1.275 GHz) are evaluated for retrieval of above-ground biomass in boreal forest. L-band data and biomass measurements, collected at the Krycklan test site in northern Sweden as part of the BioSAR 2008 campaign, are used to compare biomass retrievals from SAOCOM-CS to those based on SAOCOM SAR data. Both data sets are in turn compared with the corresponding airborne case, as represented by experimental airborne SAR through processing of the original SAR data. TomoSAR retrievals use a model involving a logarithmic transform of the volumetric backscatter intensity, <inline-formula> <tex-math notation="LaTeX">$I_{\mathrm{ vol}}$ </tex-math></inline-formula>, defined as the total backscatter originating between 10 and 30 m above ground. SAR retrievals are obtained with slope-compensated intensity <inline-formula> <tex-math notation="LaTeX">$\gamma ^{0}$ </tex-math></inline-formula> using the same model. It is concluded that tomography using SAOCOM-CS represents an improvement over an airborne SAR imagery, resulting in biomass retrievals from a single polarization (HH) having a 26%–30% root-mean-square error with a little to no impact from the look direction or the local topography.
SAR Image Classification by Exploiting Adaptive Contextual Information and Composite Kernels

For synthetic aperture radar (SAR) image land cover classification, traditional feature-based methods are not always effective because of the heavy multiplicative noise. To solve this problem, we herein propose a new classification method for SAR images considering adaptive spatial contextual information. In contrast to preceding studies, the spatial contextual information of the SAR images is exploited via composite kernels (CKs). Additionally, an image superpixel strategy is employed to design an adaptive neighborhood, which enables the extraction of more accurate spatial information than a fixed-size neighborhood. Specifically, a modified superpixel map is first generated to produce the neighborhood. With this neighborhood, a context kernel is then defined by means of the Gaussian radial basis function. The resulting context kernel is combined with the conventional feature kernel via the designed CKs scheme. The relative proportion of these two kernels is controlled by a weight parameter. The label of each pixel is predicted by feeding the final CKs into a support vector machine classifier. Experiments on two real SAR images demonstrate that the proposed method can greatly improve the classification performance, both visually and quantitatively, in comparison to other traditional feature-based methods.
Refining risk classification in childhood B acute lymphoblastic leukemia: results of DFCI ALL Consortium Protocol 05-001.

Dana-Farber Cancer Institute (DFCI) ALL Consortium Protocol 05-001 tested a new risk stratification system in children and adolescents with newly diagnosed acute lymphoblastic leukemia (ALL). At study entry, B-ALL patients were classified as standard risk (SR) or high risk (HR) based on age, white blood cell (WBC) count, and central nervous system status. After achieving complete remission (CR), patients with high end-induction minimal residual disease (MRD) (≥10-3 by polymerase chain reaction analysis of patient-specific antigen receptor rearrangements) and/or adverse cytogenetics (KMT2A rearrangement or hypodiploidy) were reclassified as very high risk (VHR) and received intensified therapy. IKZF1 deletion status was retrospectively evaluated by multiplex ligation-dependent probe amplification. Between 2005 and 2011, 678 Philadelphia chromosome-negative B-ALL patients aged 1 to 18 years enrolled; 651 achieved CR and 648 received a final risk group. Among all 678 patients, 5-year event-free survival (EFS) was 87% (95% confidence interval [CI], 84-89) and overall survival 93% (95% CI, 90-94). Five-year disease-free survival of SR patients (N = 407) was 94% (95% CI, 91-96), HR (N = 176) was 84% (95% CI, 77-88), and VHR (N = 65) was 79% (95% CI, 67-87). IKZF1 deletion was present in 62 of 385 (16%) assessed patients and was associated with inferior 5-year EFS (63%; 95% CI, 49%-74% vs 88%; 95% CI, 84%-91%; P < .001), and higher 5-year cumulative incidence of relapse, including among those with low MRD (24% vs 8%, P = .001). In multivariable analysis, age ≥15 years, WBC ≥50 × 109/L, IKZF1 deletion, and MRD ≥10-4 was each associated with inferior outcome. In conclusion, risk-stratified therapy on DFCI 05-001 resulted in favorable outcomes for B-ALL patients, including those with VHR features. IKZF1 deletion was an independent predictor of inferior outcome. This trial was registered at www.clinicaltrials. gov as #NCT00400946.
Big data stream learning based on hybridized Kalman filter and backpropagation through time method

Most real-time control systems are often accompanied with various changes such as variations of working load and changes of the environment. Hence it is necessary to perform real-time process modeling so that the model can adjust itself in runtime to maintain high accuracy of states under control. This paper considers process model represented as a deep recurrent neural network. We propose a new hybridized learning method for online updating the weights of such recurrent neural networks by exploiting both fast convergence of Kalman filter and stable search of the Backpropagation through time algorithm. Several experiments were made to show that the proposed learning method has fast convergence, high accuracy and good adaptivity. It can not only achieve high modeling accuracy for a static process but also quickly adapt to changes of characteristics in a time-varying process.
Development of an Exergame on Mobile Phones to Increase Physical Activity for Adults with Severe Mental Illness

Maintaining a certain level of Physical Activity (PA) is important to prevent some chronic pathologies. This is even more important for individuals with severe mental health problems, but they have many barriers that make it very difficult for them to be physically active, including lack of motivation. In this paper, we propose an exergame that aims to help these people integrate PA into their daily lives. This exergame is designed on a smartphone in order to be able to follow the level of activity of the player on a daily basis. It offers game mechanics that allow the player to manage their PA. However, it encourages them to be physically active so that they can progress more easily in the game. The application uses an activity detection algorithm to measure the level of PA. We offer a preliminary study on healthy subjects on a demo version of the game that observes their gaming experience. The results show that the mechanics are globally appreciated and that the game allows each player to manage his PA as he wishes.
Chip-scale cooling of power semiconductor devices: Fabrication of Jet impingement design

Chip-scale cooling is proposed for future wide bandgap (WBG) power semiconductor devices to overcome challenges associated with device power dissipation, high heat fluxes up to 1 kW/cm2, and traditional package thermal resistance. In the current paper, fabrication of a chip-scale cooler that utilizes fluid jet impingement plus flow through an optimized branching microchannel topology is described. This chip-scale cooling structure is expected to provide an estimated 70% higher cooling performance for the same pumping power and more uniform cooling relative to a straight microchannel design based on prior numerical and experimental studies. The proposed embedded flow structure is defined by three layers, and each layer is fabricated by double-sided etching of a six-inch silicon wafer. The final device is obtained by bonding the three etched layers together and dicing the three-wafer stack into individual chips. The design and fabrication of the cooling chip, including process challenges and solutions, is the focus of the current paper. Discussion of the current technology and a vision of its future application is also provided.
A new method for performance evaluation of enterprise architecture using streotypes

These days, we see many organizations with extremely complex systems with various processes, organizational units, individuals, and information technology support where there are complex relationships among their various elements. In these organizations, poor architecture reduces efficiency and flexibility. Enterprise architecture, with full description of the functions of information technology in the organization, attempts to reduce the complexity of the most efficient tools to reach organizational objectives. Enterprise architecture can better assess the optimal conditions for achieving organizational goals. For evaluating enterprise architecture, executable model need to be applied. Executable model using a static architectural view to describe necessary documents need to be created. Therefore, to make an executable model, we need a requirement to produce products of the enterprise architecture to create an executable model. In this paper, for the production of an enterprise architecture, object-oriented approach is implemented. We present an algorithm to use stereotypes by considering reliability assessment. The approach taken in this algorithm is to improve the reliability by considering additional components in parallel and using redundancy techniques to maintain the minimum number of components. Furthermore, we implement the proposed algorithm on a case study and the results are compared with previous algorithms.
Center-Point-Guided Proposal Generation for Detection of Small and Dense Buildings in Aerial Imagery

For automatic building detection in aerial images, small and dense buildings make it a very challenging task. It is because small objects lack sufficient information, and dense building distribution makes the localization of the objects confusing. High-quality building proposals can certainly promote the detection performance. The key to the problem is adopting sufficiently proper size and location of bounding boxes to use the image information for the proposal generation. Based on machine learning with a deep convolutional neural network, this letter proposes a new pipeline of building proposal generation, which is an end-to-end process during training and testing. First, the proposed pipeline attempts to find possible object center points called point proposals. Subsequently, a location refinement module and an object scoring module are applied to the boxes generated from the point proposals with a series of sizes and aspect ratios to obtain the final object proposals. This center-point-guided location refinement and multibox scoring method effectively alleviates the small and dense object problems. Experiments in INRIA Aerial Image Labeling data set demonstrate the better performance of our approach than other state-of-the-art proposal methods. In addition, we add a normal classification branch based on our generated proposals to conduct experiments on detection task. Detection result outperforms the latest detection framework R-FCN equipped with ResNet-101 7% mean average precision at 0.7.
Reliable Transmission of Short Packets Through Queues and Noisy Channels Under Latency and Peak-Age Violation Guarantees

This paper investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme—a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases—is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on larg-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.
Reelin Haploinsufficiency and Late-Adolescent Corticosterone Treatment Induce Long-Lasting and Female-Specific Molecular Changes in the Dorsal Hippocampus

Reelin depletion and stress seem to affect similar pathways including GABAergic and glutamatergic signaling and both are implicated in psychiatric disorders in late adolescence/early adulthood. The interaction between reelin depletion and stress, however, remains unclear. To investigate this, male and female heterozygous reelin mice (HRM) and wildtype (WT) controls were treated with the stress hormone, corticosterone (CORT), during late adolescence to simulate chronic stress. Glucocorticoid receptors (GR), N-methyl-d-aspartate receptor (NMDAr) subunits, glutamic acid decarboxylase (GAD67) and parvalbumin (PV) were measured in the hippocampus and the prefrontal cortex (PFC) in adulthood. While no changes were seen in male mice, female HRM showed a significant reduction in GR expression in the dorsal hippocampus. In addition, CORT reduced GR levels as well as GluN2B and GluN2C subunits of NMDAr in the dorsal hippocampus in female mice only. CORT furthermore reduced GluN1 levels in the PFC of female mice. The combined effect of HRM and CORT treatment appeared to be additive in terms of GR expression in the dorsal hippocampus. Female-specific CORT-induced changes were associated with overall higher circulating CORT levels in female compared to male mice. This study shows differential effects of reelin depletion and CORT treatment on GR and NMDAr protein expression in male and female mice, suggesting that females are more susceptible to reelin haploinsufficiency as well as late-adolescent stress. These findings shed more light on female-specific vulnerability to stress and have implications for stress-associated mental illnesses with a female bias including anxiety and major depression.
Analyst intuition inspired high velocity big data analysis using PCA ranked fuzzy k-means clustering with multi-layer perceptron (MLP) to obviate cyber security risk

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.
A novel dual channel receive front-end module with MEMS technology

This paper introduces an dual channel receive front-end module which is fabricated by MEMS package technology. The module is composed of radio frequency (RF) components, is extremely-small, low-cost and high reliability. The microwave monolithic integrated circuits of the receive front-end module are mounted onto a silicon substrate fabricated by the MEMS technology. Some passive components, such as the filters, the attenuators and the transmission lines, are embedded in the silicon substrate. The full module consists of two low noise amplifiers, two digital attenuators, two mixers, a power divider, some chip capacitances and two MEMS filters. The measured receive gain and the output power at 1-dB gain compression point are 5dB and 0dBm respectively. By comparing with the samples obtained by the common process, while the performance of them are basically the same, which verifies the feasibility of the technical approach.
Describing and Classifying Post-Mortem Content on Social Media

As the quantity of user profiles on social media grows, so does the number of post-mortem profiles. In this paper, we present a computational linguistic analysis of post-mortem social media profiles. Specifically, we provide an analysis of pre- vs. post-mortem language use, followed by a description of classifiers we developed that can accurately classify the mortality of social media profiles. These results shed initial lights into the ways in which people speak to the dead, and mark a first step toward accurately identifying mortality on a large scale.
Learning STRIPS Action Models with Classical Planning

This paper presents a novel approach for learning strips action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given strips action model, even if this model is not fully specified.
Deep learning to predict user rating in imbalance classification data incorporating ensemble methods

In recent years, machine learning gets more attention in research field with the successful breakthrough of deep learning. Deep learning has demonstrated some remarkable success through its capacity to create detailed (deep) models of complex multivariate in structured data. Deep learning can be characterized in several different ways, but the most important that deep learning can learn higher-order interactions among features using a cascade of many layers. Despite the successful breakthrough of deep learning, it still faces big challenges in computation complexity with hyper-parameter and time-consuming process to calculate many fully connected layers. We propose an alternate way to learn deep learning model in imbalance dataset. We incorporate ensemble models to learn every class target (user rating) with one base deep learning classifier models from user comments, in this way we will learn every rating with its fitting model. The main idea for ensemble model is to give an alternate solution for base classifiers to select the best results. In ensemble model, we could use different feature selection for input layer. Using all features in the same model could cause computation complexity and make the running process consume much time. Also, some features could be assessed whether significant or not. Less significant feature later could be prune in classifier or could be substituted with other features which are extracted from sampling data. This classification is based on two dimensions: how predictions are combined (rule-based and Meta-learning) and how the learning process is done (parallel or sequential). In rule-based approach, predictions will be combined using rule to average the performance. Meta-learning techniques use predictions from component classifiers as features for a Meta-learning model. In output layer, we apply voting methods to select user rating as output from each base classifier to generate user rating predicting results with more extensive results.
A two-stage hierarchical community structure detected algorithm in complex network

Series of algorithms have been devised in detecting community. To make the detected community structure match better with the real network, we propose a new hierarchical community detection algorithm called Perm-Mod. The algorithm includes two stages. In the first stage, using the heuristic method, we optimize an objective function. When the function achieves maximum value, a few small communities are precisely detected. In the second stage, we estimate whether the network has community structure based on the maximum value. If yes, we merge the above small communities. Compared to other algorithms, our algorithm solves three problems at the same time: (i) we estimate whether the network has community structure (ii) the community structure found by our algorithm matches well with the ground-truth network and (iii) the process of merging reflects the hierarchy.
Indoor scene recognition based on deep learning and sparse representation

Indoor scene images have the characteristics of small inter-class variety and large intra-class variety because of the content complexity of indoor scene images and the influence of illumination change and partial occlusion. This makes it difficult to effectively represent the semantic information of the indoor scene using traditional shallow feature learning. We present a comprehensive method combining deep features and sparse representation for indoor scene recognition in this paper. In terms of feature extraction, a Faster R-CNN based multi-class detector is training for extracting object information to be as the low-level features. An improved bag-of-words model is designed to build mid-level features from object-based low-level features, which retain the spatial information of low-level features. For improving the robustness of the proposed method, sparse representation is used to make the final decision of indoor scene recognition from mid-level features. Experimental results on indoor scene subset of MIT-67 dataset show that our proposed method can achieve a superior performance in comparison to baseline methods.
Culturaturismo.piemonte: Coordinating Cultural and Touristic Operators via the Georeferenced Civic Social Network FirstLife

"FirstLife" is an innovative example of ICT and information visualization technology that can be used as a tool for the organization and co-production of CH related available information in a specific territory. FirstLife is a map-based civic social network that offers a geo-referenced representation of open and crowdsourced data through an interactive map and that enable communities to collaborate in creating dynamic and public contents related to the area. It overcomes some of the major limits of social networks and web applications when they are used for sharing and generating contents related to a territorial community, representing the state of the art in crowdsourcing and showing information on interactive maps. ACM Reference Format: Guido Boella, Luigi Sanasi, Claudio Schifanella, and Cristina Viano. 2018. Culturaturismo.piemonte: Coordinating Cultural and Touristic Operators via the Georeferenced Civic Social Network FirstLife. In Proceedings of 2nd Workshop on Advanced Visual Interfaces for Cultural Heritage (AVI-CH 2018). Vol. 2091. CEUR-WS.org, Article 4. http://ceur-ws.org/Vol-2091/paper4.pdf,