Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust tracking with per-exemplar support vector machine

The authors extend exemplar representation to the field of tracking and propose a robust tracking algorithm with per-exemplar support vector machine (SVM) classifiers. First, the authors train the simple yet effective exemplar SVM classifier using the target object as the single positive and mining its surroundings as hard negatives. Second, the authors propose an online ensemble tracker, which integrates the useful ‘key historical templates’ of the target to refine the current template, leading to better discriminative power of tracker and effectively decreasing the risk of drift. Experiments on challenging sequences demonstrate that the tracker performs well in accuracy and robustness, especially under the sequences with strong illumination variation and scale variation, as well as pose change and partial occlusion in the long-time sequence.
Accurate shellcode recognition from network traffic data using artificial neural nets

This paper presents an approach to shellcode recognition directly from network traffic data using a multi-layer perceptron with back-propagation learning algorithm. Using raw network data composed of a mixture of shellcode, image files, and DLL-Dynamic Link Library files, our proposed design was able to classify the three types of data with high accuracy and high precision with neither false positives nor false negatives. The proposed method comprises simple and fast pre-processing of raw data of a fixed length for each network data package and yields perfect results with 100% accuracy for the three data types considered. The research is significant in the context of network security and intrusion detection systems. Work is under way for real time recognition and fine-tuning the differentiation between various shellcodes.
Circular Double‐Patterning Lithography Using a Block Copolymer Template and Atomic Layer Deposition

A novel and feasible methodology is developed to fabricate well‐ordered, freestanding 1D n‐ZnO/p‐Si nanotube (NT) and nanorod (NR) arrays via double‐patterning technology with block copolymer (BCP) self‐assembly, atomic layer deposition (ALD), and inductively coupled plasma (ICP) dry etching. To obtain the well‐ordered NT pattern, a self‐assembled, Si‐containing poly(styrene‐block‐4‐(tert‐butyldimethylsilyl)oxystyrene) BCP on an SU‐8/p‐Si wafer is employed as a template. After n‐ZnO deposition on the self‐assembled BCP template by ALD, an ICP etching process is performed to produce well‐defined, independent n‐ZnO/p‐Si NT arrays. The insights into the nanoarrays presented here are directly applicable to the fabrication of n‐ZnO/p‐Si NT/NR patterns and Si NT/NR patterns by precisely controlling the ALD cycles and ICP etching time. The electrical properties of a single n‐ZnO/p‐Si NT are measured by conductive atomic force microscopy, and the results show the typical rectifying behavior of a nanodiode with superior electrical properties. This simple and useful approach provides a very convenient route for fabricating high‐density nanodiode patterns. Additionally, the possibility of various applications is confirmed by simple analyses, including examinations of contact angle and reflectance. Furthermore, the wettability and antireflection properties can be controlled by changing the nanoarray morphology.
Climate change and habitat conversion favour the same species.

Land-use change and climate change are driving a global biodiversity crisis. Yet, how species' responses to climate change are correlated with their responses to land-use change is poorly understood. Here, we assess the linkages between climate and land-use change on birds in Neotropical forest and agriculture. Across > 300 species, we show that affiliation with drier climates is associated with an ability to persist in and colonise agriculture. Further, species shift their habitat use along a precipitation gradient: species prefer forest in drier regions, but use agriculture more in wetter zones. Finally, forest-dependent species that avoid agriculture are most likely to experience decreases in habitable range size if current drying trends in the Neotropics continue as predicted. This linkage suggests a synergy between the primary drivers of biodiversity loss. Because they favour the same species, climate and land-use change will likely homogenise biodiversity more severely than otherwise anticipated.
SCC based modulo scheduling for coarse-grained reconfigurable processors

Coarse-grained reconfigurable arrays (CGRAs) architectures aim to offer high performance at low power consumption, especially for digital signal processing and streaming applications. To fully exploit the computing capability of CGRA, it is essential to develop a scheduling algorithm which maps operations over processing elements in CGRA. Modulo scheduling [1] is known as the state-of-art algorithm for CGRA scheduling, and there are many variants [2][3][4]. However, they suffer from dealing with inter-iteration dependences called as recurrences that form cyclic dependences. Hence we propose a new scheduling technique that efficiently handles the cyclic dependences. The key techniques are grouping all the mutually-dependent recurrence cycles into a strongly connected component (SCC), and scheduling the input data flow graph (DFG) based on SCCs. Since grouping removes all the recurrence cycles from DFG, the resulting SCC graph becomes a form of directed acyclic graph (DAG) in which the scheduler can track the total order of SCCs. While processing SCCs one by one, our intra-SCC scheduler analyzes the dependences between every pair of two different operations inside of SCC and produces the schedule of them. Thanks to the well-structured form of the SCC-based graph, we obtain more efficient schedule compared to the previous CGRA scheduling algorithm [2]. The experimental results show that the proposed technique enhances the performance of recurrence-dominant loops up to 3.5X and raises the success rate of modulo-scheduling compared to the previous CGRA scheduling algorithm [2].
Ways of grounding imagination

This paper discusses and evaluates use of different participatory design methods in relation to addressing the challenge of grounding imagination. It presents reflections on the use of three participatory design methods, deployed in the WorkSpace project: future laboratories, in-situ prototyping experiments and bricolage. The analysis examines how the methods differ, and how they complement one another, in relation to supporting the process of grounding imagination. The paper introduces 'future laboratories' as a participatory design method, specifically aiming at promoting interdisciplinary collaboration and grounded imagination.
Autophagy during Proliferation and Encystation in the Protozoan Parasite Entamoeba invadens

ABSTRACT Autophagy is one of the three systems responsible for the degradation of cytosolic proteins and organelles. Autophagy has been implicated in the stress response to starvation, antigen cross-presentation, the defense against invading bacteria and viruses, differentiation, and development. Saccharomyces cerevisiae Atg8 and its mammalian ortholog, LC3, play an essential role in autophagy. The intestinal protozoan parasite Entamoeba histolytica and a related reptilian species, Entamoeba invadens, possess the Atg8 conjugation system, consisting of Atg8, Atg4, Atg3, and Atg7, but lack the Atg5-to-Atg12 conjugation system. Immunofluorescence imaging revealed that polymorphic Atg8-associated structures emerged in the logarithmic growth phase and decreased in the stationary phase and also increased in the early phase of encystation in E. invadens. Immunoblot analysis showed that the increase in phosphatidylethanolamine-conjugated membrane-associated Atg8 was also accompanied by the emergence of Atg8-associated structures during the proliferation and differentiation mentioned above. Specific inhibitors of class I and III phosphatidylinositol 3-kinases simultaneously inhibited both the growth of trophozoites and autophagy and also both encystation and autophagy in E. invadens. These results suggest that the core machinery for autophagy is conserved and plays an important role during proliferation and differentiation in Entamoeba.
Quality control of embossed hologramsby measuring gloss and color values

Embossed holograms are increasingly often used for the decorative refinement of printed products. So far, there has not been much scientific research about quality control of embossed holograms as well as research about the influence of the background color on the quality of embossed holograms. For this purpose, hologram samples with different background colors were produced in a laboratory setup, using the principle of UV embossing. At first, the quality of the samples was evaluated through a visual experiment. The conceptual design of that experiment was part of this research. Second, gloss and color values were measured with conventional hand-held measuring instruments used by the graphics industry. Color measurements were conducted with an X-Rite MA98 multi-angle spectrophotometer and measurements of gloss values with a BYK micro-TRI-gloss gloss meter. The comparison of the results of the visual experiment and the color measurement led to the conclusion that conventional color measuring instruments can evaluate the influence of the background color on the quality of embossed holograms. It was found out that calculating the color difference ΔE*ab between background color and sample can be used to recreate the results of the visual experiment, whereas the samples’ chroma C*ab is not suitable for evaluating the influence of the background color. The number of provided measuring geometries is a limitation of this approach. Moreover, the comparison of the results of the visual experiment and the gloss measurements showed that conventional gloss meters cannot evaluate the influence of the background color. However, conclusions concerning the UV embossing process can be drawn from a sample’s gloss. This suggests the usage of gloss measurement for process control.
Optimizing similarity based visualization in content based image retrieval

In any CBIR system, visualization is important, either to show the final result to the user or to form the basis for interaction. Advanced systems use 2D similarity based visualization which shows not only the information of one image itself but also the relations between images. A problem in interactive 2D visualization is the overlap between the images displayed. This obviously reduces the search capability. Simply spreading the images on the screen space will not preserve the relations between them. In this paper, we propose a visualization scheme which reduces the overlap as well as preserves the general distribution of the images displayed. Results show that an effective balance between display of structures and limited overlap can be achieved
Skeleton Point Trajectories for Human Daily Activity Recognition

Automatic human action annotation is a challenging problem, which overlaps with many computer vision fields such as video-surveillance, human-computer interaction or video mining. In this work, we offer a skeleton based algorithm to classify segmented human-action sequences. Our contribution is twofold. First, we offer and evaluate different trajectory descriptors on skeleton datasets. Six short term trajectory features based on position, speed or acceleration are first introduced. The last descriptor is the most original since it extends the well-known bag-of-words approach to the bag-of-gestures ones for 3D position of articulations. All these descriptors are evaluated on two public databases with state-of-the art machine learning algorithms. The second contribution is to measure the influence of missing data on algorithms based on skeleton. Indeed skeleton extraction algorithms commonly fail on real sequences, with side or back views and very complex postures. Thus on these real data, we offer to compare recognition methods based on image and those based on skeleton with many missing data.
Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.
Earth science.

• Geology (p. 2) Geology for Transfer AS-T Degree Geology investigates our planet Earth from diverse perspectives, spanning from the smaller scale study of its building blocks (minerals) to the larger scale study of its dynamism (Theory of Plate Tectonics), as well as its history and the relationship between the physical environment and the evolution of life. This is accomplished by viewing the planet as a system, where there are interactions between the lithosphere, the atmosphere, the hydrosphere and the biosphere. Within this context, students develop strong critical thinking, problem solving, communication and technical skills to work in a relatively large set of career fields beyond that of Geology, such as Hydrology, Geophysics, Oceanography, Meteorology and Environmental Science. The required curricular sequence also provides the necessary skills in the physical sciences and mathematics required for junior standing at baccalaureate institutions. Thus, future Earth Scientists acquire both a global and sequential way to approach topics, with an emphasis on the interdisciplinary nature of the field and also enhance their expertise through hands-on laboratory work, readying themselves for the applied nature of the workforce (industry, consulting and government agencies).
Spatial Normalization of Reverse Phase Protein Array Data

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.
Plasma radiometabolite correction in dynamic PET studies: Insights on the available modeling approaches

Full kinetic modeling of dynamic PET images requires the measurement of radioligand concentrations in the arterial plasma. The unchanged parent radioligand must, however, be separated from its radiometabolites by chromatographic methods. Thus, only few samples can usually be analyzed and the resulting measurements are often noisy. Therefore, the measurements must be fitted with a mathematical model. This work presents a comprehensive analysis of the different models proposed in the literature to describe the plasma parent fraction (PPf) and of the alternative approaches for radiometabolite correction. Finally, we used a dataset of [11C]PBR28 brain PET data as a case study to guide the reader through the PPf model selection process.
Comparative analysis of the antioxidant and anticancer activities of chestnut inner shell extracts prepared with various solvents.

BACKGROUND The chestnut inner shell (CIS) has long been used as a medicinal herb for strengthening the antioxidant in the cosmetic industry. However, little is known about the antioxidant and anticancer effects of the CIS. RESULTS The antioxidant and anticancer effects of CIS extracts (CISEs) were investigated by the use of various methods and cancer cell lines, respectively. The total polyphenol content of CISEs using ethanol, methanol, butanol, ethyl acetate and water were 53.30, 43.98, 32.16, 26.79 and 11.53 mg gallic acid equivalents g(-1), respectively. The CISEs using ethanol and methanol exhibited high antioxidant activities in the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity, β-carotene bleaching and ferric reducing ability of plasma assays; the effects were equivalent to those of butylated hydroxytoluene. All CISEs at 2.5 mg mL(-1) were shown to have a cytotoxic effect over 50%, and the CISE using ethyl acetate at 0.6 mg mL(-1) was proved to have 90% cytotoxic effect against the tested cancer cells. CONCLUSION The ethanol and methanol CISEs had potent antioxidant effects, and the ethyl acetate CISE had the highest cytotoxicity. These results suggest that CISEs could be used as functional ingredients for antioxidant and anticancer effects in foods as extraction solvents.
A filtered backprojection algorithm with characteristics of the iterative landweber algorithm.

PURPOSE In order to eventually develop an analytical algorithm with noise characteristics of an iterative algorithm, this technical note develops a window function for the filtered backprojection (FBP) algorithm in tomography that behaves as an iterative Landweber algorithm. METHODS Based on the formulation of the iterative Landweber algorithm, a frequency domain window function is derived for each iteration of the Landweber algorithm. The resultant window function has an index k, emulating the characteristics of the Landweber algorithm at the kth iteration. The window function is used to modify the ramp filter in the FBP algorithm. RESULTS Computer simulations show that the windowed FBP algorithm with window function index k and the iterative Landweber algorithm iteration number k give similar reconstructions in terms of resolution and noise. CONCLUSIONS Analytical FBP algorithms are able to provide similar results to the iterative Landweber algorithm if the ramp filter in the FBP algorithm is modified by a set of specially designed window functions.
Activity-Dependent Change in AMPA Receptor Properties in Cerebellar Stellate Cells

High-frequency synaptic stimulation is thought to cause a rapid and lasting change in the expression of GluR2 subunit-containing AMPA receptors (AMPARs) at synapses in cerebellar stellate cells. We examined whether spontaneous synaptic activity affects the expression of GluR2-containing synaptic AMPARs and whether the change in AMPAR subtypes alters their Ca2+permeability and kinetic properties. We used intracellular spermine, which blocks GluR2-lacking receptors at depolarized potentials, to distinguish the presence of GluR2. In most cells, the spontaneous EPSC frequency was low, and evoked EPSCs displayed inwardly rectifyingI–V relationships, indicative of the presence of GluR2-lacking AMPARs. However, in cells that displayed a higher rate of spontaneous synaptic activity, EPSCs gave linearI–V plots, suggesting the presence of GluR2-containingAMPARs. This is consistent with the idea that spontaneous synaptic activity increased the expression of GluR2-containing AMPARs at synapses. The Ca2+permeability of AMPARs that gave inwardly rectifying currents in outside-out patches from TTX-treated cells was six times higher than in control cells that gave linear or outwardly rectifyingI–V plots. However, increased spontaneous synaptic activity did not significantly alter the EPSC decay time. Furthermore, the decay time course ofEPSCs mediated by GluR2-containing receptors was not different from that mediated by a mixed population of receptors at the same synapse. Our results suggest that the level of spontaneous synaptic activity can determine the subunit composition of postsynaptic receptors at this synapse. The activity-induced expression of GluR2-containing receptors significantly reduced the Ca2+ permeability of AMPARs in stellate cells but did not slow the decay time course of synaptic currents.
Robot Phase Entrainment on Quadruped CPG Controller

Central Pattern Generators are used in several kinds of robot locomotion, from swimming and flying, to bipeds, quadrupeds and hexapods. It is thought that this approach can yield better results in dynamical and natural environments. In this work we expand a previous quadruped locomotion controller and propose a method to couple the step cycle phase onto the locomotor CPG of a quadruped robot, creating a feedback pathway to coordinate the phases of each leg to the phase of the step cycle. This approach is tested in a simulated quadruped robot and the performed locomotion is evaluated. Results demonstrate that the proposed phase coupling synchronizes the swing step phase of ipsilateral legs to the respective step phase of the cycle and show an improvement in stability of the performed walk gait.
A Characterization of Single-Link Search in Learning Belief Networks

One alternative to manual acquisition of belief networks from domain experts is automatic learning of these networks from data. Common algorithms for learning belief networks employ a single-link lookahead search. It is unclear, however, what types of domain models are learnable by such algorithms and what types of models will escape. We conjecture that these learning algorithms that use a single-link search are specializations of a simple algorithm which we call LIM. We put forward arguments that support such a conjecture, and then provide an axiomatic characterization of models learnable by LIM. The characterization coupled with the conjecture identifies models that are definitely learnable and definitely unlearnable by a class of learning algorithms. It also identifies models that are highly likely to escape these algorithms. Research to formally prove the conjecture is ongoing.