Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarimetric SAR Response of Snow-Covered Area Observed by Multi-Temporal ALOS PALSAR Fully Polarimetric Mode

This study discusses the capability assessment of fully polarimetric L-band spaceborne synthetic aperture radar (SAR) for detection of seasonal snow covered areas. In this paper, ALOS PALSAR time-series data sets obtained in quad-pol modes have been investigated to evaluate the polarimetric signal scattered from a snow-covered mountainous ecosystem in Niigata, Japan. Results show that changes in the scattering mechanism across the various snow states can be identified from polarimetric parameters. In particular, different polarimetric parameters offer complementary information on the snow properties. Based on the characteristic seasonal changes of polarimetric parameters, a new method to map snow-covered areas is proposed in this study using an information fusion approach. Snow extent can be identified successfully by combining polarimetric indices with an overall accuracy of 74.4% as compared with in situ measurements and 77.0% as compared with optical images.
Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework

This paper presents a unified dynamic modeling framework for differential-drive mobile robots (DDMR). Two formulations for mobile robot dynamics are developed; one is based on Lagrangian mechanics, and the other on Newton-Euler mechanics. Major difficulties experienced when modeling non-holonomic systems in both methods are illustrated and design procedures are outlined. It is shown that the two formulations are mathematically equivalent providing a check on their consistency. The presented work leads to an improved understanding of differentialdrive mobile robot dynamics, which will assist engineering students and researchers in the modeling and design of suitable controllers for DDMR navigation and trajectory tracking.
Scheduling and Ordering Production Policies in a Limited Capacity Manufacturing System: The Multiple Replenishment Products Case

This paper formulates production policies to maximize the performance of an apparel manufacturing system that replenishes basic items characterized by a flat average demand. The simulation-based model compares a number of production strategies and chooses an ordering and scheduling policy that increases the overall performance of the supply chain. In doing so, different supplier configurations (i.e., different capacity resources and cost structures) are compared and the optimal strategy is selected to maximize the supplier’s profit while maintaining a high order fill rate and minimizing the risk of carrying inventory. The system’s performance is then compared to an upper limit, calculated using Linear Programming. keywords: Ordering and Scheduling production; Heuristic Policies, Replenishment items;
Knowledge of animal grading and market participation among sheep and goats producers in Somaliland

• Factors that significantly influenced market participation included number of animals kept, gender of sales decision-maker, age of household head and livelihood zone. Background Livestock is the leading economic sector in Somaliland accounting for 40% of GDP and 80% of forex earnings. Sheep and goats are the most important type of livestock reared and marketed in Somaliland. In 2012, over 3 million sheep and goats worth over USD 200 million were traded and exported from the port of Berbera (SLCCIA 2013). The main importers of livestock from Somaliland include the Kingdom of Saudi Arabia, Yemen, Oman, Egypt and the United Arab Emirates. Apart from the various intermediaries involved in the Somali livestock export trade: exporters, agents of exporters, small-scale livestock traders and brokers (Negassa et al. 2008; Mugunieri et al. 2012), thousands of livestock producers are also important actors in the livestock export value chain.
Do I really need you? a team-centred analysis of task interdependence in innovation systems

The innovation-era has seen firms adopting a variety of organisation designs with teams as their basic building blocks. The increasing competitive importance of innovation and the prevalence of team-based organisation designs have confronted firms with the challenge to manage complex task interdependence configurations. Firms therefore resort to structural linking to integrate dispersed innovation activities across a multitude of teams. I find that structural linking in innovation-led firms occurs by means of linking teams: i.e. teams which are created by top managers to support or manage the innovation process across other teams. Within this context, I have set out to answer the central question of this study: how and why do teams shape the development of task interdependence? I examined how teams shape task interdependence over time through an inductive, longitudinal study of four high-technology firms. 122 interviews were conducted over a period of 24 months including a 3 month ethnographic stage. My central contribution is a model of how teams shape the development of task interdependence. Overall, I present the conjunction of task and social interdependence as a dynamic, cyclical, process, which is shaped by the collective agency of teams. This study proposes that studying task interdependence from the perspective of teams requires the inclusion of social interdependence because teams form different perceptions of the designed task interdependence which can be positive, negative, or individualistic. These perceptions emerge in response to perceived goal structures between teams and as a response to how task interdependence relates to the identity and autonomy of the involved teams. These perceptions produce distinct patterns of interaction between teams in innovation systems, which subsequently instigate reconfigurations of both task and social interdependence.
Fuzzy forwarding for opportunistic networks

Routing in opportunistic networks is difficult due to the intermittently connected environment and lack of global view on network topology. In such scenarios, nodes attempt to transmit packets in a store-carry-and forward manner. The main issue is which forwarding mechanism achieves the best trade off between successful packet delivery ratio and cost. We address this challenge by proposing the Fuzzy Forwarding (FF) approach, in which packets are preferred jointly considering the knowledge of performance metrics (e.g., the mean delivery delay and average number of hops per packet) and that of node contacts (e.g., durations, times and locations). FF gives higher priority to packets, as compared to the heuristic knowledge, if they have shorter TTL (time to live) and smaller number of hops, and nodes desiring to relay them have higher delivery probability to destination. Furthermore, to deal with the incompleteness and uncertainty of such knowledge, a fuzzy logic engine is developed for the computation of packet preference. The trace-driven simulation results demonstrate that FF achieves significantly better cost compared to the state-of-the-art works, while keeping better delivery ratios and mean delivery delays under different TTL requirements.
Robust Object Segmentation Using Graph Cut with Object and Background Seed Estimation

In this paper we propose a new robust way of extracting accurate human silhouettes indoors with an active stereo camera. We first infer the parts of object and background areas of high confidence by fusing color, stereo matching information and image segmentation methods. Then the inferred areas (seeds) are incorporated in a graph cut. The experimental results were presented with image sequences taken with pan-tilt stereo camera. Our proposed algorithms were evaluated with respect to the ground truth data. We proved that our algorithms can outperform other methods that are based on either color/contrast or stereo/contrast principles alone
Ultra-high-frequency temperature-compensated oscillators based on ovenized AlN contour-mode MEMS resonators

This work demonstrates the possibility of achieving good temperature stability in ultra-high frequency AlN MEMS CMR based oscillators by means of resonator ovenization. This technique results in low power consumption, fast turn-on time, and good short term stability. Over the temperature range -45 ÷ 85 °C, the 582 MHz oscillator exhibits a temperature stability as good as 125 ppm (limited by the electronic circuitry) and stable phase noise values better than -93 dBc/Hz at 1 kHz, and -160 dBc/Hz at 10 MHz offsets.
Multiple instance learning with random forests and applications in industrial optical inspection

Automatic defect detection in industrial optical inspection requires algorithms that can learn from data. A special challenge is data with incomplete labels. One of the methods that the field of machine learning has brought forth to deal with incomplete labels is multiple instance learning. One trait of this setting is that it groups datapoints (instances) into bags. We propose a novel method to predict bag probabilities from given instance probabilities that has the advantage that its results do not depend on bag size. Also, we propose an extension of the multiple instance model that allows the user to steer the number of instances that are classified as positive. We implement these methods with an algorithm based on the well-known random forest classifier. Results on a standard benchmark dataset show competitive performance. Furthermore, we apply this algorithm to image data that reflects the challenges of industrial optical inspection, and we show that in this setting it improves over the standard random forest.
An evaluation of the links between microRNA, autophagy, and epilepsy

Abstract Epilepsy is a serious chronic neurologic disorder characterized by recurrent unprovoked seizures resulting from abnormal and highly synchronous neuronal discharges within the brain. Small noncoding RNAs, called microRNAs, play vital roles in epileptogenesis, with potential contributions as valuable biomarkers and targets for the treatment of epilepsy. To maintain cellular homeostasis, cellular components, such as organelles, proteins, protein complexes/oligomers, and pathogens, are delivered to the lysosome for degradation through a process called autophagy, which plays either a protective or a harmful role under epileptic stress. Several autophagic mechanisms have been implicated in epileptogenesis, including the mammalian target of rapamycin pathway, aberrant substrate accumulation, and the formation of epileptic networks. In addition, the regulation of autophagy through microRNAs (miRNAs) represents a novel posttranscriptional regulatory mechanism through ‘autophagamiRNAs’. The correlation between autophagy and miRNA has increased our understanding of the underlying pathogenesis of human diseases. Here, we review the current findings regarding the correlations between miRNA, autophagy, and epilepsy to provide a solid foundation for further examination of the miRNA-autophagy pathway involved in epilepsy pathophysiology.
Optimal Design of Neuro-Mechanical Networks

This paper is about a simple method for solving nonlinear, non-convex optimization problems (NLPs) with matrix inequality constraints. The method is based on the fact that a symmetric matrix is positive semi-definite if and only if it admits a Cholesky decomposition, and works by reformulating the original matrix inequality constrained problem into a standard NLP, for which there are currently many high-quality codes available. Examples of optimization problems involving matrix inequality constraints are relatively frequent in the structural optimization literature, and to illustrate a potential usage of our method we present numerical solutions for weight minimization of trusses subject to compliance and global buckling constraints. Looking ahead, we also see problems involving simultaneous optimization of both structure and control systems being common, and since matrix inequality constrained problems appear frequently in control theory, we believe that the number of applications for codes like the one presented here will continue to grow rapidly.
Semi-supervised weighted distance metric learning for kNN classification

K-Nearest Neighbor (kNN) classification is one of the most popular machine learning techniques, but it often fails to work well due to less known information or inappropriate choice of distance metric or the presence of a lot of unrelated features. To handle those issues, we introduce a semi-supervised weighted distance metric learning method for kNN classification. This method uses a graph-based semi-supervised Label Propagation algorithm to gain more classification information with tiny initial classification information, then resorts to improved weighted Relevant Component Analysis to learn a Mahalanobis distance metric, and finally uses learned Mahalanobis distance metric to replace the original Euclidean distance of kNN classifier. Experiments on UCI datasets show the effectiveness of our method.
Evaluation of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Identification of Nontuberculous Mycobacteria from Clinical Isolates

ABSTRACT Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) for the identification of nontuberculous mycobacterial (NTM) isolates was evaluated in this study. Overall, 125 NTM isolates were analyzed by MALDI-TOF and GenoType CM/AS. Identification by 16S rRNA/hsp65 sequencing was considered the gold standard. Agreements between MALDI-TOF and GenoType CM/AS with the reference method were, respectively, 94.4% and 84.0%. In 17 cases (13.6%), results provided by GenoType and MALDI-TOF were discordant; however, the reference method agreed with MALDI-TOF in 16/17 cases (94.1%; P = 0.002).
Supervised classification of protein structures based on convex hull representation

One of the central problems in functional genomics is to establish the classification schemes of protein structures. In this paper the relationship of protein structures is uncovered within the framework of supervised learning. Specifically, the novel patterns based on convex hull representation are firstly extracted from a protein structure, then the classification system is constructed and machine learning methods such as neural networks, Hidden Markov Models (HMM) and Support Vector Machines (SVMs) are applied. The CATH scheme is highlighted in the classification experiments. The results indicate that the proposed supervised classification scheme is effective and efficient.
CHEMICAL TAGGING CAN WORK: IDENTIFICATION OF STELLAR PHASE-SPACE STRUCTURES PURELY BY CHEMICAL-ABUNDANCE SIMILARITY

Chemical tagging promises to use detailed abundance measurements to identify spatially separated stars that were, in fact, born together (in the same molecular cloud) long ago. This idea has not yielded much practical success, presumably because of the noise and incompleteness in chemical-abundance measurements. We have succeeded in substantially improving spectroscopic measurements with The Cannon, which has now delivered 15 individual abundances for stars observed as part of the APOGEE spectroscopic survey, with precisions around 0.04 dex. We test the chemical-tagging hypothesis by looking at clusters in abundance space and confirming that they are clustered in phase space. We identify (by the k-means algorithm) overdensities of stars in the 15-dimensional chemical-abundance space delivered by The Cannon, and plot the associated stars in phase space. We use only abundance-space information (no positional information) to identify stellar groups. We find that clusters in abundance space are indeed clusters in phase space, and we recover some known phase-space clusters and find other interesting structures. This is the first-ever project to identify phase-space structures at the survey-scale by blind search purely in abundance space; it verifies the precision of the abundance measurements delivered by The Cannon; the prospects for future data sets appear very good.
Colloidal gold markers and probes for routine application in microscopy

Colloidal gold can be used as an electron‐dense cytochemical probe for direct or indirect labelling techniques. Gold markers can be prepared in a size range of 5–150 nm, and show size‐dependent shape characteristics and absorption spectra. Size and shape distribution increases with mean particle diameter, with appreciable overlapping between populations of different mean size. Using radio isotope‐binding assays, spectrophotometric analysis and an innovative rapid microtitration technique, the effect of pH, ionic strength and protein concentration on gold‐protein interaction has been studied. Efficient adsorption of protein to gold occurs at, or near, the pI of the protein. The amount of protein needed to effect stabilization is both a function of pH and of ionic strength, but does not reflect the amount of protein binding for all proteins. There is evidence for multilamellar adsorption of proteins to gold, which is discussed in context of the bioactivity, and stability of the probe. A working protocol for the routine reproducible manufacture of protein‐gold probes is given, making use of the microtitration assay.
Active Learning for Data Streams Under Concept Drift and Concept Evolution

Data streams classification is an important problem however, poses many challenges. Since the length of the data is theoretically infinite, it is impractical to store and process all the historical data. Data streams also experience change of its underlying dis-tribution (concept drift), thus the classifier must adapt. Another challenge of data stream classification is the possible emergence and disappearance of classes which is known as (concept evolution) problem. On the top of these challenges, acquiring labels with such large data is expensive. In this paper, we propose a stream-based active learning (AL) strategy (SAL) that handles the aforementioned challenges. SAL aims at querying the labels of samples which results in optimizing the expected future error. It handles concept drift and concept evolution by adapting to the change in the stream. Furthermore, as a part of the error reduction process, SAL handles the sampling bias problem and queries the samples that caused the change i.e., drifted samples or samples coming from new classes. To tackle the lack of prior knowledge about the streaming data, non-parametric Bayesian modelling is adopted namely the two representations of Dirichlet process; Dirichlet mixture models and stick breaking process. Empirical results obtained on real-world benchmarks show the high performance of the proposed SAL method compared to the state-of-the-art methods.
A Moxa Stimulation on the Leg Affected the Function of Stomach via Autonomic Nerve System and Polymodal Receptors

The moxa therapy is a popular traditional hyperthermia therapy in East Asia. The moxa is made from dried mugwort (Artemisiaargyi). The moxa is usually put on a meridian point and then is burnt on to obtain a hyperthermic stimulation to the skin. However, very few researches have been studied in the effects and mechanism of the moxa therapy. Therefore, in this study, we gave the moxa stimulation to healthy subjects. In the moxa group, the moxa was lit on the ST36 (Zusanli: an acupuncture point in anterior tibialis of the leg) for 5 minutes. Their responses were traced with electrogastrogram (EGG) and assessed the change of gastric function. At the same time, their heart rate (HR) was recorded and observed to study the mechanism of moxa treatment. And then their results were compared with those of the control group. After the moxa stimulation, EGG showed significant increase and stayed 3.0 - 3.1 cpm while HR prominently decreased. On the other hand, the control group did not indicate such results. Those results are common to the re-sponse of acupuncture-like stimulation by the reflex arc which consists of receptor, sensory neuron, central nervous system, motor neuron and effector. It may be explained as below: the elements of the acupuncture-like stimulation and the moxa are pain and hyperthermia respectively and they both are received by polymodal receptors. Thus the moxa stimulation applied on ST36 may be ef-fective to enhance stomach function.
Efficacy trials of biomedical strategies to prevent HIV infection

Purpose of reviewThe aim of this article is to outline key challenges facing the conduct of efficacy trials for biomedical strategies to prevent HIV infection. Recent findingsThe past 2–3 years have seen tumultuous development in this field. There have been disappointing findings in efficacy trials of vaginal microbicides, vaccines, the diaphragm and suppressive therapy for herpes, but a major breakthrough with the evidence that male circumcision prevents acquisition of infection. Meanwhile, clarity has started to emerge on a number of issues regarding trial conduct, including provision of standard of care, involvement of communities, and preparation for implementing effective interventions. SummaryHIV prevention practice must continue to rely on condom promotion and other established strategies while biomedical approaches continue to be assessed and their implementation strategies evolve.
Positive association of the pericentrin (PCNT) gene with major depressive disorder in the Japanese population.

BACKGROUND Pericentrin (PCNT) interacts with disruption-in-schizophrenia 1 (DISC1), a known genetic risk factor for schizophrenia, bipolar disorder and major depressive disorder (MDD). We sought to determine whether the PCNT gene is implicated in MDD. METHODS We performed case-control association analyses in the Japanese population. We analyzed 9 single nucleotide polymorphisms (SNPs) in 173 patients with MDD and 348 healthy controls. RESULTS We found a significant allelic association between 3 SNPs (rs3788265, rs2073376 and rs2073380) of the PCNT gene and MDD (p = 0.006, 0.005 and 0.021, respectively). After correction for multiple testing, 2 SNPs (rs3788265 and rs2073376) retained significant allelic associations with MDD. In addition, we found a significant association between the 2 marker haplotypes (r3788265 and rs2073376) and MDD (permutation p = 0.011). LIMITATIONS Our sample was small and comprised only Japanese participants. In addition, owing to the late onset of MDD, it is possible that the disorder will develop in at least some participants in our control group. Finally, we did not show how SNPs of the PCNT gene alter its function. CONCLUSION Our results suggest that genetic variations in the PCNT gene may play a significant role in the etiology of MDD in the Japanese population.
Modeling susceptibility to drug-induced long QT with a panel of subject-specific induced pluripotent stem cells

A large number of drugs can induce prolongation of cardiac repolarization and life-threatening cardiac arrhythmias. The prediction of this side effect is however challenging as it usually develops in some genetically predisposed individuals with normal cardiac repolarization at baseline. Here, we describe a platform based on a genetically diverse panel of induced pluripotent stem cells (iPSCs) that reproduces susceptibility to develop a cardiotoxic drug response. We generated iPSC-derived cardiomyocytes from patients presenting in vivo with extremely low or high changes in cardiac repolarization in response to a pharmacological challenge with sotalol. In vitro, the responses to sotalol were highly variable but strongly correlated to the inter-individual differences observed in vivo. Transcriptomic profiling identified dysregulation of genes (DLG2, KCNE4, PTRF, HTR2C, CAMKV) involved in downstream regulation of cardiac repolarization machinery as underlying high sensitivity to sotalol. Our findings offer novel insights for the development of iPSC-based screening assays for testing individual drug reactions. DOI: http://dx.doi. org/10.7554/eLife.19406. 001