Miracle-dz/UniFAR-FTUs huggingface dataset:
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.
Reseach on a new approach of auto self-balancing for rescue robot on uneven terrain

This paper introduces a new approach to estimate stability of robot in rugged terrain and presents an algorithm to make robot achieve self-balance automatically. In consideration of the terrain is unknown and uncertain, it will be effective to keep robot in a better stable state to meet the challenges of terrain. We define a new parameter, stability angle, to describe the stable state of robot. By calculating the stability angle in real-time, stable state of robot will be known. The rescue robot in this paper is composed of manipulator and mobile vehicle, and it's found that the posture of manipulator makes an obvious influence to the stability of our rescue robot. As a consequence, optimizing the posture of manipulator can make the robot more stable. Using gradient computation and searching algorithm, we can adjust manipulator to optimal posture, which makes the robot reach an optimal stable state. The experiment and simulation revealed that the methods used in this paper can effectively help robot keep stable.
Detection of vulnerable road users in blind spots through Bluetooth Low Energy

Around a vehicle, a blind spot is an area, which cannot be seen by the driver. Blind spot accidents are a frequently occurring problem. Annually, dozens of deaths are caused by this phenomenon despite legally compulsory prevention tools and large investments in raising awareness and developing prevention systems. Because this is not enough to reduce the number of accidents, proactive solutions must be found. This article describes the evaluation of a new blind spot system based on BLE (Bluetooth Low Energy). In this paper, a complete system is designed with on-the-shelf products and finally realized in a proof-of-concept. The final design requirements are fixed, by figuring out where the blind spots are and which various advantages and disadvantages the existing systems have. In a further stage, the system is tested in several possible critical situations, proving that it is very robust. Vulnerable road users are indeed detected by visualization on the LCD display.
Latency and throughput characterization of convolutional neural networks for mobile computer vision

We study performance characteristics of convolutional neural networks (CNN) for mobile computer vision systems. CNNs have proven to be a powerful and efficient approach to implement such systems. However, the system performance depends largely on the utilization of hardware accelerators, which are able to speed up the execution of the underlying mathematical operations tremendously through massive parallelism. Our contribution is performance characterization of multiple CNN-based models for object recognition and detection with several different hardware platforms and software frameworks, using both local (on-device) and remote (network-side server) computation. The measurements are conducted using real workloads and real processing platforms. On the platform side, we concentrate especially on TensorFlow and TensorRT. Our measurements include embedded processors found on mobile devices and high-performance processors that can be used on the network side of mobile systems. We show that there exists significant latency-throughput trade-offs but the behavior is very complex. We demonstrate and discuss several factors that affect the performance and yield this complex behavior.
Pritchard ( Acari : Phytoseiidae : Tetranychidae ) on Screenhouse Tomatoes

The spider mite Tetranychus evansi Baker & Pritchard can cause severe damage to tomato crops. The predatory mite Phytoseiulus longipes Evans was recently reported in association with T. evansi in Uruguaiana, Rio Grande do Sul State, Brazil. The objective of the present study was to evaluate the effects of P. longipes on the population of T. evansi on tomatoes under screenhouse condition. The study consisted on four experiments, in each of which 80 potted plantlets were distributed in two plots of 40 plantlets each. Two weeks later, each plantlet of both plots was infested with eight adult females of T. evansi; one week after, four adult females of P. longipes were released onto each plant of one plot. The population levels of T. evansi and the damage caused by these mites were signifi cantly lower (P ≤ 0.05; linear mixed-effect model) in the plots where P. longipes had been released. The results indicate the potential of this predator as a candidate for classical biological control of T. evansi by inoculative releases on tomato plants.
Does the cause of the mild traumatic brain injury affect the expectation of persistent postconcussion symptoms and psychological trauma?

ABSTRACT Introduction: A controlled experiment of the effect of injury cause on expectations of outcome from mild traumatic brain injury (TBI) was conducted. Method: Ninety-three participants were randomly assigned to one of four conditions. The participants read a vignette that described a mild TBI (with fixed injury parameters) from a different cause (sport, domestic assault, fall, or motor vehicle accident). The effect of the manipulation on expectations of persistent postconcussion symptoms and psychological trauma was assessed with standard measures and a novel “threat-to-life” measure. Results: The Kruskal–Wallis H test for group differences revealed a significant but selective effect of group on symptom and trauma outcomes (ŋ2s ≥ .10; large effects). Post hoc pairwise tests showed that, in most cases, there was an expectation of a worse outcome following mild TBI from a domestic assault than from the other causes (small-to-medium effects). Conclusion: Expectations were selectively altered by an experimental manipulation of injury cause. Given that expectations of outcome are known to affect mild TBI prognosis, the findings suggest the need for greater attention to injury cause.
Reduced SMAD2/3 activation independently predicts increased depth of human cutaneous squamous cell carcinoma

The incidence of cutaneous squamous cell carcinoma (cSCC) is rising. Whilst the majority are cured surgically, aggressive metastatic cSCC carry a poor prognosis. Inactivating mutations in transforming growth factor beta (TGF-β) receptors have been identified amongst genetic drivers of sporadic tumours and murine models of cSCC, suggesting a tumour suppressor function for TGF-β in normal skin. However, paradoxically, TGF-β acts as a tumour promoter in some murine model systems. Few studies have analysed the role of TGF-β/activin signalling in human normal skin, hyper-proliferative skin disorders and cSCC. Antibodies recognising phospho-SMAD proteins which are activated during canonical TGF-β/activin signalling were validated for use in immunohistochemistry. A tissue microarray comprising FFPE lesional and perilesional tissue from human primary invasive cSCC (n=238), cSCC in-situ (n=2) and keratocanthoma (n=9) were analysed in comparison with tissues from normal human scalp (n=10). Phosphorylated SMAD2 and SMAD3 were detected in normal interfollicular epidermal keratinocytes and were also highly localised to inner root sheath, matrix cells and Keratin 15 positive cells. Lesional cSCC tissue had significantly reduced activated SMAD2/3 compared to perilesional tissue, consistent with a tumour suppressor role for SMAD2/3 activators in cSCC. Increased cSCC tumour thickness inversely correlated with the presence of phospho-SMADs in tumour tissue suggesting that a reduction in canonical TGF-β/activin signalling may be associated with disease progression.
IL‐6 increases podocyte motility via MLC‐mediated focal adhesion impairment and cytoskeleton disassembly

The disturbance of podocyte motility is an essential pathogenic mechanisms of foot process effacement during proteinuric diseases, and myosin light chain (MLC) is a pivotal component in regulating the motility of podocytes. Inflammatory cytokine interleukin‐6 (IL‐6) has been reported to induce podocyte abnormalities by various mechanisms, however, whether aberrant cell motility contributes to the IL‐6‐induced podocyte injury remains unknown. Here, by wound healing, transwell, and cell migration assays, we confirmed that IL‐6 accelerates the motility of podocyte. Simultaneously, the phosphorylation of MLC is elevated along with perturbed focal adhesion (FAs) and cytoskeleton. Next, via genetic and pharmacologic interruption of MLC or its phosphorylation we revealed that the activation of MLC is implicated in IL‐6‐mediated podocyte hypermotility as well as the disassembly of FAs and F‐actin. By using stattic, an inhibitor for STAT3 phosphorylation, we uncovered that STAT3 activation is the upstream event for MLC phosphorylation and the following aberrant motility of podocytes. Additionally, we found that calcitriol markedly attenuates podocyte hypermotility via blocking STAT3‐MLC. In conclusion, our study demonstrated that IL‐6 interrupts FAs dynamic, cytoskeleton organization, and eventually leads to podocyte hypermotility via STAT3/MLC, whereas calcitriol exerts its protective role by inhibiting this pathway. These findings enrich the mechanisms accounting for IL‐6‐mediated podocyte injury from the standpoint of cell motility and provide a novel therapeutic target for podocyte disorders.
Allocation wall: a limiting factor of Java applications on emerging multi-core platforms

Multi-core processors are widely used in computer systems. As the performance of microprocessors greatly exceeds that of memory, the memory wall becomes a limiting factor. It is important to understand how the large disparity of speed between processor and memory influences the performance and scalability of Java applications on emerging multi-core platforms. In this paper, we studied two popular Java benchmarks, SPECjbb2005 and SPECjvm2008, on multi-core platforms including Intel Clovertown and AMD Phenom. We focus on the "partially scalable" benchmark programs. With smaller number of CPU cores these programs scale perfectly, but when more cores and software threads are used, the slope of the scalability curve degrades dramatically. We identified a strong correlation between scalability, object allocation rate and memory bus write traffic in our experiments with our partially scalable programs. We find that these applications allocate large amounts of memory and consume almost all the memory write bandwidth in our hardware platforms. Because the write bandwidth is so limited, we propose the following hypothesis: the scalability and performance is limited by the object allocation on emerging multi-core platforms for those objects-allocation intensive Java applications, as if these applications are running into an "allocation wall". In order to verify this hypothesis, several experiments are performed, including measuring key architecture level metrics, composing a micro-benchmark program, and studying the effect of modifying some of the "partially scalable" programs. All the experiments strongly suggest the existence of the allocation wall.
Experimental investigation of size effect on thermal conductivity for ultra-thin amorphous poly(methyl methacrylate) (PMMA) films

Experimental Investigation of Size Effect on Thermal Conductivity for Ultra-thin Amorphous Poly(methyl methacrylate) (PMMA) Films. (May 2007) Ick Chan Kim, B.En. , In-Ha University Chair of Advisory Committee: Dr. Egidio E. Marotta An investigation was conducted to determine whether a “size effect” phenomenon for one particular thermophysical property, thermal conductivity, actually exists for amorphous poly(methyl methacrylate) (PMMA) films with thicknesses ranging from 40 nm to 2 μm. This was done by using a non-contact, non-invasive, in-situ Transient Thermo-Reflectance (TTR) laser based technique. The results demonstrated that the intrinsic thermal conductivity of a 40 nm PMMA film deposited on native oxide of silicon increases by a factor of three over bulk PMMA values, and a distinct increase in the thermal conductivity of PMMA film was observed in ultra-thin (sub 100 nm) films. This confirmed the importance of film thickness for the through-plane thermal conductivity value of PMMA film on native oxide of silicon.
Surface damage in electrodischarge machining

L'influence de la duree des impulsions et du type d'electrode (cuivre ou graphite) sur l'absorption de carbone par la surface d'un acier pour matrice mis en forme par usinage par electro-erosion et sur les microfissures superficielles a ete etudiee
On the robustness of machine learning based malware detection algorithms

With the rapid popularity of the Internet, a large amount of new malware is produced every day, while the traditional signature based malware detection algorithm is unable to detect such unseen malware. In recent years, many machine learning based algorithms have been proposed to detect new malware, and several of these algorithms are able to achieve quite good detection performance when supplied with plenty of training data. However, most of these algorithms just focus on how to improve the classification performance, while the robustness is not taken into consideration. This paper performs a detailed analysis on the robustness of four well-known machine learning based malware detection approaches, i.e. the DLL and API feature, the string feature, PE-Miner and the byte level N-Gram feature. We proposed two pretense approaches under which malware is able to pretend to be benign and bypass the detection algorithms. Experimental results show that the performances of these detection algorithms decline greatly under the pretense approaches. The lack of robustness makes these algorithms unable to be used in real world applications. In future works of machine learning based malware detection, researchers have to take the problem of robustness seriously.
Linear-Time Representation Algorithms for Proper Circular-Arc Graphs and Proper Interval Graphs

Our main result is a linear-time (that is, time $O(m+n)$) algorithm to recognize and represent proper circular-arc graphs. The best previous algorithm, due to A. Tucker, has time complexity $O(n^2)$. We take advantage of the fact that (among connected graphs) proper circular-arc graphs are precisely the graphs orientable as local tournaments, and we use a new characterization of local tournaments. The algorithm depends on repeated representation of portions of the input graph as proper interval graphs. Thus we also find it useful to give a new linear-time algorithm to represent proper interval graphs. This latter algorithm also depends on an orientation characterization of proper interval graphs. It is conceptually simple and does not use complex data structures. As a byproduct of the correctness proof of the algorithm, we also obtain a new proof of a characterization of proper interval graphs by forbidden subgraphs.
Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures

Owning to its clinical accessibility, T1-weighted MRI (Magnetic Resonance Imaging) has been extensively studied in the past decades for prediction of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The volumes of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are the most commonly used measurements, resulting in many successful applications. It has been widely observed that disease-induced structural changes may not occur at isolated spots, but in several inter-related regions. Therefore, for better characterization of brain pathology, we propose in this paper a means to extract inter-regional correlation based features from local volumetric measurements. Specifically, our approach involves constructing an anatomical brain network for each subject, with each node representing a Region of Interest (ROI) and each edge representing Pearson correlation of tissue volumetric measurements between ROI pairs. As second order volumetric measurements, network features are more descriptive but also more sensitive to noise. To overcome this limitation, a hierarchy of ROIs is used to suppress noise at different scales. Pairwise interactions are considered not only for ROIs with the same scale in the same layer of the hierarchy, but also for ROIs across different scales in different layers. To address the high dimensionality problem resulting from the large number of network features, a supervised dimensionality reduction method is further employed to embed a selected subset of features into a low dimensional feature space, while at the same time preserving discriminative information. We demonstrate with experimental results the efficacy of this embedding strategy in comparison with some other commonly used approaches. In addition, although the proposed method can be easily generalized to incorporate other metrics of regional similarities, the benefits of using Pearson correlation in our application are reinforced by the experimental results. Without requiring new sources of information, our proposed approach improves the accuracy of MCI prediction from (of conventional volumetric features) to (of hierarchical network features), evaluated using data sets randomly drawn from the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset.
A unified pattern recognition approach for low-frequency biometric signals

During the last decade many intelligent systems that help us in everyday life have come into being. Artificial intelligence is a widespread sphere among lots of people and scientific laboratories, as it includes different fields that are instantly developing nowadays. Pattern recognition is an important part of artificial intelligence: it helps to build secure information systems, technical devices, applications that use augmented reality techniques, etc. Biometric identification approaches apply pattern recognition methods for different types of signals in order to protect access to the systems' data. However, most of the existing methods consider only one type of biometric signals, as it is hard to combine characteristics for various signals; moreover, they are focused on obtaining the data from high frequency devices. This paper presents a new unified pattern recognition approach that can be used in order to identify various types of biometric signals based on their characteristics.
Odor tracing in turbulent airflow field using only three MOX sensors

An odor source orientation inference method with few adjustable parameters and high automaticity is proposed for two-dimensional (2D) odor source localization tasks. This method utilizes three MOX sensors to measure the horizontal dispersion process of gas. Wavelet transform and modulus maxima lines are adopted to discover the time difference information hidden in the spatiotemporal signals of gas sensors. The final odor source orientation is obtained by particle filter using a sequence of odor dispersion directions and flow rates inferred from the time difference information. Using a mobile robot, the odor tracing experiments have demonstrated the effectiveness of this method in the time-varying airflow field.
Individual self > relational self > collective self-But why? Processes driving the self-hierarchy in self- and person perception.

OBJECTIVE The self has three parts: individual, relational, and collective. Typically, people personally value their individual self most, their relational self less, and their collective self least. This self-hierarchy is consequential, but underlying processes have remained unknown. Here, we propose two process accounts. The content account draws upon selves' agentic-communal content, explaining why the individual self is preferred most. The teleology account draws upon selves' instrumentality for becoming one's personal ideal, explaining why the collective self is preferred least. METHOD In Study 1 (N = 200, 45% female, Mage = 32.9 years, 79% Caucasian), participants listed characteristics of their three selves (individual, relational, collective) and evaluated those characteristics in seven preference tasks. Additionally, we analyzed the characteristics' agentic-communal content, and participants rated their characteristics' teleological instrumentality. Study 2 (N = 396, 55% female, Mage = 34.5 years, 76% Caucasian) used identical methodology and featured an additional condition, where participants evaluated the selves of a friend. RESULTS Study 1 reconfirmed the self-hierarchy and supported both process accounts. Study 2 replicated and extended findings. As hypothesized, when people evaluate others' selves, a different self-hierarchy emerges (relational > individual > collective). CONCLUSIONS This research pioneers process-driven explanations for the self-hierarchy, establishing why people prefer different self-parts in themselves than in others.
Ferroelectric and magnetic properties of Pb(Fe2∕3W1∕3)O3-based multiferroic compounds with cation order

BiFeO3 and PbTiO3 were introduced to a Sc-modified Pb(Fe2∕3W1∕3)O3 compound with strong cation order to improve the multiferroic properties. It is found that the degree of cation order decreases as the amount of BiFeO3 or PbTiO3 increases. As a result, the saturation magnetization deteriorates. Solid solutions with BiFeO3 show an increase in both ferroelectric and magnetic transition temperatures. However, the ferroelectric remanent polarization is dramatically suppressed. In contrast, solid solution with PbTiO3 leads to an increase in the ferroelectric transition temperature, a decrease in the magnetic transition temperature, and a significant enhancement of remanent polarization. The composition 0.93[0.79Pb(Fe2∕3W1∕3)O3–0.21Pb(Sc2∕3W1∕3)O3]–0.07PbTiO3 shows the optimized properties of Tmax of 208K, Pr of 3. 6μC∕cm2 between 120 and 210K, TN of 209K, and Ms of 0. 23μB∕f.u. (3.7emu∕g) at 10K under 5T.
Quantification of soil macroporosity and its relationship with soil properties

Image analysis was used to estimate macroporosity (pores ~ 0.54 mm in diameter) of undisturbed soil columns obtained from a field under no-till for 5 years. A high resolution x-ray Image Intensifier (XRII)-based CT scanner was used to investigate soil macroporosity at different depths in the soil column. Total macroporosity in the Aand Bhorizons averaged 3.70 and 1.94%, respectively, and was consistently lower in the B-horizon. The relative contributions of macroporosity to the total porosity in the A and B horizons were 7.76 and 4.92%, respectively. There was a statistically significant decreasing trend of macroporosity through the combined A and B horizon depth (0 - 400 mm) and in the A horizon (depth 0 - 200 mm), but not in the B-horizon (depth 200 - 400 mm). Saturated hydraulic conductivity correlated significantly with the total macroporosity of the Aand the B horizons, within the A horizon, bulk density also significantly correlating with total macroporosity. Keywords: bulk density, computer tomography, image analysis, macroporosity, hydraulic conductivity, no-till.