Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement Scheduling for Soil Moisture Sensing: From Physical Models to Optimal Control

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.
Taking an AXE to L2 Spanning Trees

I think that I shall never see a structure more wasteful than a tree. Most links remain idle and unused while others are overloaded and abused. And with each failure comes disruption caused by the ensuing tree construction. Thus, L2 must discard its spanner, requiring flooding in a different manner. For the tree's fragile waste to be abated, trim no branches and detect packets duplicated. (With apologies to Radia Perlman and Joyce Kilmer.)
Combining Deep Learning and Active Contours Opens The Way to Robust, Automated Analysis of Brain Cytoarchitectonics

Deep learning has thoroughly changed the field of image analysis yielding impressive results whenever enough annotated data can be gathered. While partial annotation can be very fast, manual segmentation of 3D biological structures is tedious and error-prone. Additionally, high-level shape concepts such as topology or boundary smoothness are hard if not impossible to encode in Feedforward Neural Networks. Here we present a modular strategy for the accurate segmentation of neural cell bodies from light-sheet microscopy combining mixed-scale convolutional neural networks and topology-preserving geometric deformable models. We show that the network can be trained efficiently from simple cell centroid annotations, and that the final segmentation provides accurate cell detection and smooth segmentations that do not introduce further cell splitting or merging.
Annotation and prediction of stress and workload from physiological and inertial signals

Continuous daily stress and high workload can have negative effects on individuals' physical and mental well-being. It has been shown that physiological signals may support the prediction of stress and workload. However, previous research is limited by the low diversity of signals concurring to such predictive tasks and controlled experimental design. In this paper we present 1) a pipeline for continuous and real-life acquisition of physiological and inertial signals 2) a mobile agent application for on-the-go event annotation and 3) an end-to-end signal processing and classification system for stress and workload from diverse signal streams. We study physiological signals such as Galvanic Skin Response (GSR), Skin Temperature (ST), Inter Beat Interval (IBI) and Blood Volume Pulse (BVP) collected using a non-invasive wearable device; and inertial signals collected from accelerometer and gyroscope sensors. We combine them with subjects' inputs (e.g. event tagging) acquired using the agent application, and their emotion regulation scores. In our experiments we explore signal combination and selection techniques for stress and workload prediction from subjects whose signals have been recorded continuously during their daily life. The end-to-end classification system is described for feature extraction, signal artifact removal, and classification. We show that a combination of physiological, inertial and user event signals provides accurate prediction of stress for real-life users and signals.
Detection of Overlapping Communities in Dynamical Social Networks

Community detection on networks is a well-known problem encountered in many fields, for which the existing algorithms are inefficient 1) at capturing overlaps in-between communities, 2) at detecting communities having disparities in size and density 3) at taking into account the networks’ dynamics. In this paper, we propose a new algorithm (iLCD) for community detection using a radically new approach. Taking into account the dynamics of the network, it is designed for the detection of strongly overlapping communities. We first explain the main principles underlying the iLCD algorithm, introducing the two notions of intrinsic communities and longitudinal detection, and detail the algorithm. Then, we illustrate its efficiency in the case of a citation network, and then compare it with existing most efficient algorithms using a standard generator of community-based networks, the LFR benchmark.
Analytical modeling of device-circuit interactions for the power insulated gate bipolar transistor (IGBT)

The device-circuit interactions of the IGBT for a series resistor-inductor load, both with and without a snubber, are simulated. An analytical model for the transient operation of the IGBT is used in conjunction with the load circuit state equations for the simulations. The simulated results are compared with experimental results for all conditions. Devices with a variety of base lifetimes are studied. For the fastest devices studied (base lifetime=0.3 mu s), the voltage overshoot of the series resistor-inductor load circuit approaches the device voltage rating (500 V) for load inductances greater than 1 mu H. For slower devices the overshoot is much less, and a larger inductance can therefore be switched without a snubber circuit (e.g. 80 mu H for a 7.1- mu s device). The simulations are used to determine the conditions for which the different devices can be switched to safety without a snubber protection circuit. Simulations are also used to determine the required values and ratings for protection circuit components when protection circuits are necessary. <<ETX>>
Formal Methods for Reverse Engineering Gate-Level Netlists

Abstract : In this report, we present a systematic framework for automatically deriving high-level structures from the gate-level netlist of a digital circuit, and techniques that specifically address each of these challenges. To cope with the large functional space, we crafted a library that contains more than a thousand commonly used components. Leveraging formal verification techniques such as symbolic evaluation, model checking and equivalence checking, we further address the problem of a large implementation space per function, and recover structure from an unstructured netlist.
Implementation of Smart Parking Solution by Image Analysis

Modern smart city concept implies various smart aspects including smart parking management. Searching for a free parking lot can be a challenging task, especially during major events, therefore automatic system, which will help drivers to find a free parking is very valuable. There are many intrusive and non-intrusive technologies available for smart parking development, but authors of this paper developed a system based on video processing and analysis. Authors developed Python application for real-time parking lot monitoring based on video analysis of public video stream. Five classifier models (Logistic Regression, Linear Support Vector Machine, Radial Basis Function Support Vector Machine, Decision Tree and Random Forest) were compared for parking lot occupancy detection. Logistic regression classifier showed better results and was chosen for real-time parking monitoring application. System shows good performance and correctly predicted parking lot occupancy almost in all test cases.
Reliable Reporting of Delay-Sensitive Events in Wireless Sensor-Actuator Networks

Wireless sensor-actuator networks, or WSANs, greatly enhance the existing wireless sensor network architecture by introducing powerful and even mobile actuators. The actuators work with the sensor nodes, but can perform much richer application-specific actions. To act responsively and accurately, an efficient and reliable reporting scheme is crucial for the sensors to inform the actuators about the environmental events. Unfortunately, the low-power multi-hop communications in a WSAN are inherently unreliable; the frequent sensor failures and the excessive delays due to congestion or in-network data aggregation further aggravate the problem. In this paper, we propose a general reliability-centric framework for event reporting in WSANs. We argue that the reliability in such a real-time system depends not only on the accuracy, but also the importance and freshness of the reported data. Our design follows this argument and seamlessly integrates three key modules that process the event data, namely, an efficient and fault-tolerant event data aggregation algorithm, a delay-aware data transmission protocol, and an adaptive actuator allocation algorithm for unevenly distributed events. Our transmission protocol also adopts smart priority scheduling that differentiates the event data of non-uniform importance. We evaluate our framework through extensive simulations, and the results demonstrate that it achieves desirable reliability with minimized delay
Cross Signal Identification for PSG Applications

The standard investigational method for obstructive sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG), which consists of a simultaneous, usually overnight recording of multiple electro-physiological signals related to sleep and wakefulness. This is an expensive, encumbering and not a readily repeated protocol, and therefore there is need for simpler and easily implemented screening and detection techniques. Identification of apnea/hypopnea events in the screening recordings is the key factor for the diagnosis of OSAS. The analysis of a solely single-lead electrocardiographic (ECG) signal for OSAS diagnosis, which may be done with portable devices, at patient’s home, is the challenge of the last years. A novel artificial neural network (ANN) based approach for feature extraction and automatic identification of respiratory events in ECG signals is presented in this paper. A nonlinear principal component analysis (NLPCA) method was considered for feature extraction and support vector machine for classification/recognition. An alternative representation of the respiratory events by means of Kohonen type neural network is discussed. Our prospective study was based on OSAS patients of the Clinical Hospital of Pneumology from Iaşi, Romania, males and females, as well as on non-OSAS investigated human subjects. Our computed analysis includes a learning phase based on cross signal PSG annotation. Keywords—Artificial neural networks, feature extraction, obstructive sleep apnea syndrome, pattern recognition, signal processing.
Robust Morphological Measures for Large-Scale Structure in the Universe

We propose a novel method for the description of spatial patterns formed by a coverage of point sets representing galaxy samples. This method is based on a complete family of morphological measures known as Minkowski functionals, which includes the topological Euler characteristic and geometric descriptors to specify the content, shape and connectivity of spatial sets. The method is numerically robust even for small samples, independent of statistical assumptions, and yields global as well as local morphological information. We illustrate the method by applying it to a Poisson process, a `double-Poisson' process, and to the Abell catalogue of galaxy clusters.
Jointly optimal placement and power allocation of nodes of a wireless network

Title of thesis: Jointly optimal placement and power allocation of nodes of a wireless network Sina Firouzabadi, Master of Science, 2007 Dissertation directed by: Professor Nuno Martins Department of Electrical Engineering In this thesis, we investigate the optimal design of wireless networks. We consider wireless networks that have fixed and movable nodes, and we assume that all nodes feature adjustable transmission power. Hence, we aim at maximizing network centric objectives, by optimizing over admissible choices of the positions of the movable nodes as well as the transmission power at all the nodes. We adopt exponential path loss, which is a realistic assumption in urban and sub sea environments, and we propose ways of using this assumption to obtain a tractable optimization problem. Our formulation allows for the optimization of typical network centric objectives, such as power and throughput. It also allows signal-to-interference based constraints, such as rate-regions and outage probabilities, under the high signal to interference regime. We show that our optimization paradigm is convex and that it can be solved up to an arbitrary degree of accuracy via geometric programming techniques. By using a primal-dual decomposition, we also provide a case-study that illustrates how certain instances of our optimization paradigm can be solved via distributed iterative algorithms. We show that such a solution method also leads to a convenient layering in the primal step, whereby the power allocation and the node placement become two independent sub-problems.
Towards a unified framework for tracking and analysis of human motion

We propose a framework for detecting, tracking and analyzing non-rigid motion based on learned motion patterns. The framework features an appearance based approach to represent the spatial information and hidden Markov models (HMM) to encode the temporal dynamics of the time varying visual patterns. The low level spatial feature extraction is fused with the temporal analysis, providing a unified spatio-temporal approach to common detection, tracking and classification problems. This is a promising approach for many classes of human motion patterns. Visual tracking is achieved by extracting the most probable sequence of target locations from a video stream using a combination of random sampling and the forward procedure from HMM theory. The method allows us to perform a set of important tasks such as activity recognition, gait-analysis and keyframe extraction. The efficacy of the method is shown on both natural and synthetic test sequences.
Modelling Typical Alphabetic Analogical Reasoning

A study investigating the way in which people solve alphabetic analogical reasoning tasks (cf Copycat; Hofstadter & Mitchell, 1995), revealed that participants tend to use the same basic strategy, which was modelled in the cognitive architecture ACT-R. Performance evaluations indicate an average goodness of fit of 66.75% and a 100% goodness of fit on a subset of problems for which participants were significantly more likely to produce a single 'typical' response (p<0.05). The model is discussed in the context of various features of human analogical reasoning which were observed in the study, and in relation to Hoftstadter and Mitchell's (1995) discussion of 'elegant' solutions to problems.
A novel hybrid algorithm for binarization of badly illuminated document images

In this paper, we present a novel hybrid algorithm for binarization of badly illuminated document images. This algorithm locally enhances the document image and makes the gray levels of text and background pixels separable. Afterward a simple global binarization algorithm binarizes the enhanced image. The enhancement process is a novel method that uses a separate transformation function to map the gray level of each pixel into a new domain. For each pixel, the transformation function is determined using its neighboring pixels gray level. The proposed binarization algorithm is robust for wide variety of degraded document images. Evaluation over a set of degraded document images illustrates the effectiveness of our proposed binarization algorithm.
Ablation of the Pro-Apoptotic Protein Bax Protects Mice from Glucocorticoid-Induced Bone Growth Impairment

Dexamethasone (Dexa) is a widely used glucocorticoid to treat inflammatory diseases; however, a multitude of undesired effects have been reported to arise from this treatment including osteoporosis, obesity, and in children decreased longitudinal bone growth. We and others have previously shown that glucocorticoids induce apoptosis in growth plate chondrocytes. Here, we hypothesized that Bax, a pro-apoptotic member of the Bcl-2 family, plays a key role in Dexa-induced chondrocyte apoptosis and bone growth impairment. Indeed, experiments in the human HCS-2/8 chondrocytic cell line demonstrated that silencing of Bax expression using small-interfering (si) RNA efficiently blocked Dexa-induced apoptosis. Furthermore, ablation of Bax in female mice protected against Dexa-induced bone growth impairment. Finally, Bax activation by Dexa was confirmed in human growth plate cartilage specimens cultured ex vivo. Our findings could therefore open the door for new therapeutic approaches to prevent glucocorticoid-induced bone growth impairment through specific targeting of Bax.
Color estimation from a single surface color

This paper estimates illumination colors by using only a single surface color taken under multiple illumination colors. Past researchers have found that there is a difficulty in estimating illumination colors using a single surface color. However, the method presented here overcomes the problem. Surface color is estimated by considering four characteristics of illumination and surface color spaces. First, the outdoor-illumination colors exist in a specific color range. Second, multiple illuminations give constraints for the surface color. Third, multiple illuminations also give constraints for the color range. Fourth, each color component affects those constraints in a different manner. Based on those characteristics, a novel method can be designed. The proposed method produces consistently accurate results when multiple illumination colors are used, because the constraint (possible range of illumination colors) on illumination colors refines the estimated illumination colors, effectively.
Access Control Policies: Modeling and Validation

Access control requires authorization rules and constraints. To express access control policies, several languages, such as XACML, EPAL or PONDER, are used. These languages specify which subjects can (or cannot) access sets of resources or services to perform specific actions. A user can define several access control policies and rules, but these access control languages do not offer any mechanism to avoid conflicts and inconsistencies among them. In fact, it can happen that more than a rule or a policy, with opposite decisions, is applicable in a given context. We propose a method based on first order logic modeling to detect and visualize possible conflicts within sets of access control policies expressed in XACML. We translate the model into a relational first order logic language called Alloy. Alloy allows to specify sets of predicates and assertions defining the desired properties of a system. We can then analyze interactions and conflicts among access control policies by using the Alloy Analyzer tool.
Sphingosine kinase 1: A novel independent prognosis biomarker in hepatocellular carcinoma

Sphingosine kinase 1 (Sphk1) is an oncogenic kinase that is responsible for the phosphorylation of sphingosine to sphingosine-1-phosphate (S1P). Mounting evidence suggests that Sphk1 serves a crucial role in the proliferation and development of a variety of human cancer cells. However, the role of Sphk1 in hepatocellular carcinoma (HCC) has not been fully elucidated. Therefore, the expression of Sphk1 was examined in 127 formalin-fixed, paraffin-embedded HCC tissues using immunohistochemistry, and its clinical implications and prognostic significance were analyzed. As a result, the expression of Sphk1 in HCC tissue was revealed to be significantly higher than in normal tissue (P<0.01). In addition, Sphk1 expression was significantly associated with tumor size, tumor stage and histological differentiation (all P<0.05). The patients with low Sphk1 expression had higher overall survival and recurrence-free survival rates compared with patients with high Sphk1 expression. Furthermore, Sphk1-specific shRNA was used to downregulate the expression of Sphk1 in HCC cell lines, including hepatoblastoma G2 and HCC-9724. The CRISPR/Cas9 based transcription activation system was used to upregulate Sphk1 expression in the normal live cell, L02. Cell proliferation, mRNA expression and protein expression were measured using Cell Counting Kit-8, reverse transcription polymerase chain reaction and western blot analysis in the transfected cells. To the best of our knowledge, the present study provides the first evidence that Sphk1 promotes HCC cell proliferation and is involved in tumor progression. Notably, the data presented suggest that Sphk1 may be a potential independent prognosis biomarker for the treatment of HCC.
Linguistic Corpus Search

Searching corpora with linguistic questions requires both additional information encoded in the corpus and efficiency as in “traditional” search engines. We describe a search engine-like approach to querying plain as well as part-of-speech-tagged monolingual corpora. This approach makes use of a ‘minimalist’ query language which nevertheless allows powerful searches by optionally ignoring positional as well as inflectional features in the corpus sentences. Many queries can be formulated without detailed training via a simple web-based front-end. Relevant applications of this search tool in knowledge extraction are discussed as well.
The use of metformin is associated with decreased lumbar radiculopathy pain

Lumbar radiculopathy pain represents a major public health problem, with few effective long-term treatments. Preclinical neuropathic and postsurgical pain studies implicate the kinase adenosine monophosphate activated kinase (AMPK) as a potential pharmacological target for the treatment of chronic pain conditions. Metformin, which acts via AMPK, is a safe and clinically available drug used in the treatment of diabetes. Despite the strong preclinical rationale, the utility of metformin as a potential pain therapeutic has not yet been studied in humans. Our objective was to assess whether metformin is associated with decreased lumbar radiculopathy pain, in a retrospective chart review. We completed a retrospective chart review of patients who sought care from a university pain specialist for lumbar radiculopathy between 2008 and 2011. Patients on metformin at the time of visit to a university pain specialist were compared with patients who were not on metformin. We compared the pain outcomes in 46 patients on metformin and 94 patients not taking metformin therapy. The major finding was that metformin use was associated with a decrease in the mean of “pain now,” by −1.85 (confidence interval: −3.6 to −0.08) on a 0–10 visual analog scale, using a matched propensity scoring analysis and confirmed using a Bayesian analysis, with a significant mean decrease of −1.36 (credible interval: −2.6 to −0.03). Additionally, patients on metformin showed a non-statistically significant trend toward decreased pain on a variety of other pain descriptors. Our proof-of-concept findings suggest that metformin use is associated with a decrease in lumbar radiculopathy pain, providing a rational for larger retrospective trials in different pain populations and for prospective trials, to test the effectiveness of metformin in reducing neuropathic pain.