Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient object detection using cascades of nearest convex model classifiers

An object detector must detect and localize each instance of the object class of interest in the image. Many recent detectors adopt a sliding window approach, reducing the problem to one of deciding whether the detection window currently contains a valid object instance or background. Machine learning based discriminants such as SVM and boosting are typically used for this, often in the form of classifier cascades to allow more rapid rejection of easy negatives. We argue that “one class” methods - ones that focus mainly on modelling the range of the positive class - are a useful alternative to binary discriminants in such applications, particularly in the early stages of the cascade where one-class approaches may allow simpler classifiers and faster rejection. We implement this in the form of a short cascade of efficient nearest-convex-model one-class classifiers, starting with linear distance-to-affine-hyperplane and interior-of-hypersphere classifiers and finishing with kernelized hypersphere classifiers. We show that our methods have very competitive performance on the Faces in the Wild and ESOGU face detection datasets and state-of-the-art performance on the INRIA Person dataset. As predicted, the one-class formulations provide significant reductions in classifier complexity relative to the corresponding two-class ones.
A Learning Classifier Approach to Tomography

Tomography is an important technique for noninvasive imaging: images of the interior of an object are computed from several scanned projections of the object, covering a range of angles. Traditionally, tomographic reconstruction algorithms have been based on techniques from analysis and linear algebra. In this paper we describe how a particular version of the tomographic reconstruction problem, known as discrete tomography, can be considered as a classification problem. By making this connection between two seemingly unrelated scientific areas, the full machinery of learning classifier theory can be used to solve tomographic problems. The use of classifiers that can be trained from examples for tomography has two main advantages. First, prior knowledge concerning the images of interest can be learned automatically. Second, there are several types of classifiers that can perform the classification task very fast once trained. Real-time reconstruction is one of the main goals in tomographic imaging. Tomography typically deals with huge amounts of high-dimensional data, which makes the corresponding classification task very hard. we describe several steps to reduce the dimensionality of the input data. For one type of classifier, the multilayer perceptron, we provide experimental evidence showing that it can indeed be used for efficient tomographic imaging.
Notice of RetractionThe Evolutionary Game Analysis on the Technological Road Mapping of Low Carbon Economy within a Cluster

In this paper we introduce the evolutionary game method for the further study on the TRM of the low carbon tech within a cluster. After the analysis we find that the revenue of the low carbon tech is less than that of the traditional tech and the social gets the negative consumption surplus and the most of the suppliers select traditional tech, the TRM may have more possibility to carry out original tech pattern. And if the revenue of the low carbon tech is less than that of the traditional tech and the social gets the negative consumption surplus, the most of the suppliers select low carbon tech, this whole cluster may implement the low carbon tech successfully.
Simultaneous determination of Rabeprazole sodium and Domperidone

Three simple and sensitive methods were developed for the determination of a mixture of Rabeprazole sodium RB and Domperidone DP without prior separation. The first method A, isoabsorptive point comprised of measurement the total content of the mixture at their isoabsorptive point, while the content of RB was determined by measuring the first order derivative of its spectra at 231 nm, and the content of DP could be calculated by subtraction. The second method, B was based on the mean centering of ratio spectra, the concentration of RB was determined by measuring the amplitude at 256.4 nm and the concentration of DP was determined by measuring the amplitude at 311.8 nm. The third method C, dual wavelength method, the wavelengths selected for determination of Rabeprazole were 270 nm & 301 nm, whereas, the wavelengths selected for determination of Domperidone were 260 and 297.2 nm. The percentages mean accuracy for RB were 99.74 ± 0.498 % for method (A), 100.52 ± 0.478 % for (B) and 100.43 ± 0.502 % for (C) respectively. And that of DP were 100.28 ± 0.488 % for method (A), 99.24 ± 0.551 % for (B) and 99.95 ± 0.516 % for (C) respectively. The obtained results were statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision.
A New Isolated Auxiliary Current Pump Module for Load Transient Mitigation of Isolated/Nonisolated Step-Up/Step-Down DC–DC Converter

This paper presents a new isolated auxiliary current pump module (ACPM), to improve the dynamic response of dc-dc converters under load transients. The developed isolated ACPM can be used as a generic auxiliary module for both isolated and nonisolated step-up/step-down dc-dc converters. The isolated ACPM is a low-power bidirectional converter, designed with higher bandwidth and operates only during the load transients. The performance of the developed auxiliary power converter has been evaluated in conjunction with an interleaved boost converter as main converter, which suffers from poor dynamic performance due to inherent right half plane zero effect. A suitable control scheme is designed and implemented for interleaved boost converter operating in conjunction with proposed isolated ACPM. Load current injection circuit employed in the proposed control scheme eliminates the requirement of complex nonlinear control algorithms. The proposed control scheme in particular suits for current-mode controlled converters operating along with isolated ACPM. Experimental results for a 250-W 100-kHz interleaved boost converter prototype with ACPM are presented. The peak overshoot/undershoot and the transient recovery time are reduced significantly with the incorporation of developed isolated ACPM.
Macroevolutionary patterns of sexual size dimorphism in copepods

Major theories compete to explain the macroevolutionary trends observed in sexual size dimorphism (SSD) in animals. Quantitative genetic theory suggests that the sex under historically stronger directional selection will exhibit greater interspecific variance in size, with covariation between allometric slopes (male to female size) and the strength of SSD across clades. Rensch's rule (RR) also suggests a correlation, but one in which males are always the more size variant sex. Examining free-living pelagic and parasitic Copepoda, we test these competing predictions. Females are commonly the larger sex in copepod species. Comparing clades that vary by four orders of magnitude in their degree of dimorphism, we show that isometry is widespread. As such we find no support for either RR or for covariation between allometry and SSD. Our results suggest that selection on both sexes has been equally important. We next test the prediction that variation in the degree of SSD is related to the adult sex ratio. As males become relatively less abundant, it has been hypothesized that this will lead to a reduction in both inter-male competition and male size. However, the lack of such a correlation across diverse free-living pelagic families of copepods provides no support for this hypothesis. By comparison, in sea lice of the family Caligidae, there is some qualitative support of the hypothesis, males may suffer elevated mortality when they leave the host and rove for sedentary females, and their female-biased SSD is greater than in many free-living families. However, other parasitic copepods which do not appear to have obvious differences in sex-based mate searching risks also show similar or even more extreme SSD, therefore suggesting other factors can drive the observed extremes.
High Salinity Induces Different Oxidative Stress and Antioxidant Responses in Maize Seedlings Organs

Salinity negatively affects plant growth and causes significant crop yield losses world-wide. Maize is an economically important cereal crop affected by high salinity. In this study, maize seedlings were subjected to 75 mM and 150 mM NaCl, to emulate high soil salinity. Roots, mature leaves (basal leaf-pair 1,2) and young leaves (distal leaf-pair 3,4) were harvested after 3 weeks of sowing. Roots showed the highest reduction in biomass, followed by mature and young leaves in the salt-stressed plants. Concomitant with the pattern of growth reduction, roots accumulated the highest levels of Na+ followed by mature and young leaves. High salinity induced oxidative stress in the roots and mature leaves, but to a lesser extent in younger leaves. The younger leaves showed increased electrolyte leakage (EL), malondialdehyde (MDA), and hydrogen peroxide (H2O2) concentrations only at 150 mM NaCl. Total antioxidant capacity (TAC) and polyphenol content increased with the increase in salinity levels in roots and mature leaves, but showed no changes in the young leaves. Under salinity stress, reduced ascorbate (ASC) and glutathione (GSH) content increased in roots, while total tocopherol levels increased specifically in the shoot tissues. Similarly, redox changes estimated by the ratio of redox couples (ASC/total ascorbate and GSH/total glutathione) showed significant decreases in the roots. Activities of enzymatic antioxidants, catalase (CAT, EC 1.11.1.6) and dehydroascorbate reductase (DHAR, EC 1.8.5.1), increased in all organs of salt-treated plants, while superoxide dismutase (SOD, EC 1.15.1.1), ascorbate peroxidase (APX, EC 1.11.1.11), glutathione-s-transferase (GST, EC 2.5.1.18) and glutathione reductase (GR, EC 1.6.4.2) increased specifically in the roots. Overall, these results suggest that Na+ is retained and detoxified mainly in roots, and less stress impact is observed in mature and younger leaves. This study also indicates a possible role of ROS in the systemic signaling from roots to leaves, allowing leaves to activate their defense mechanisms for better protection against salt stress.
Language Models for Contextual Error Detection and Correction

The problem of identifying and correcting confusibles, i.e. context-sensitive spelling errors, in text is typically tackled using specifically trained machine learning classifiers. For each different set of confusibles, a specific classifier is trained and tuned. In this research, we investigate a more generic approach to context-sensitive confusible correction. Instead of using specific classifiers, we use one generic classifier based on a language model. This measures the likelihood of sentences with different possible solutions of a confusible in place. The advantage of this approach is that all confusible sets are handled by a single model. Preliminary results show that the performance of the generic classifier approach is only slightly worse that that of the specific classifier approach.
A New Method for Classification of Datasets for Data Mining

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Operational experience with the GEM detector assembly lines for the CMS forward muon upgrade

The CMS Collaborationhas been developing large-area Triple-GEM detectors to be installed in the muon endcap regions of the CMS experiment in 2019 to maintain forward muon trigger and tracking performance at the HLLHC. Ten pre-production detectors were built at CERN to commission the first assembly line and the quality controls. These were installed in the CMS detector in early 2017 and are currently participating in the 2017 LHC run. The collaboration has prepared several additional assembly and quality control lines for distributed mass production of 160 GEM detectors at various sites worldwide. During 2017, these additional production sites have been optimizing construction techniques and quality control procedures and validating them against common specifications by constructing additional preproduction detectors. Using the specific experience from one production site as an example, we discuss how the quality controls make use of independent hardware and trained personnel to ensure fast and reliable production. Preliminary results on the construction status of CMS GEM detectors are presented.
LS3: a <u>L</u>inear <u>S</u>emantic <u>S</u>can <u>S</u>tatistic technique for detecting anomalous windows

Often, it is required to identify anomalous windows along a linear path that reflect unusual rate of occurrence of a specific event of interest. Such examples include: determination of places with high number of occurrences of road accidents along a highway, leaks in natural gas transmission pipelines, pedestrian fatalities on roads, etc. In this paper, we propose a <u>L</u>inear <u>S</u>emantic <u>S</u>can <u>S</u>tatistic (LS3) approach to identify such anomalous windows along a linear path. We assume that a linear path is comprised of one-dimensional spatial locations called markers, where each marker is associated with a set of structural and behavioral attributes. We divide the linear path into linear semantic segments such that each semantic segment contains markers associated with similar structural attributes. Our goal is to identify the windows within a semantic segment whose behavioral attributes are anomalous in some sense. We accomplish this by applying the scan statistic to the behavioral attributes of the markers. We have implemented our approach by considering the real datasets of certain highways in New Jersey, USA. Our results validate that LS3 is effective in identifying high traffic accident windows.
Long-term repetition priming and semantic interference in a lexical-semantic matching task: tapping the links between object names and colors

Using a novel paradigm to engage the long-term mappings between object names and the prototypical colors for objects, we investigated the retrieval of object-color knowledge as indexed by long-term priming (the benefit in performance from a prior encounter with the same or a similar stimulus); a process about which little is known. We examined priming from object naming on a lexical-semantic matching task. In the matching task participants encountered a visually presented object name (Experiment 1) or object shape (Experiment 2) paired with either a color patch or color name. The pairings could either match whereby both were consistent with a familiar object (e.g., strawberry and red) or mismatch (strawberry and blue). We used the matching task to probe knowledge about familiar objects and their colors pre-activated during object naming. In particular, we examined whether the retrieval of object-color information was modality-specific and whether this influenced priming. Priming varied with the nature of the retrieval process: object-color priming arose for object names but not object shapes and beneficial effects of priming were observed for color patches whereas inhibitory priming arose with color names. These findings have implications for understanding how object knowledge is retrieved from memory and modified by learning.
Inferring the demographics of search users: social data meets search queries

Knowing users' views and demographic traits offers a great potential for personalizing web search results or related services such as query suggestion and query completion. Such signals however are often only available for a small fraction of search users, namely those who log in with their social network account and allow its use for personalization of search results. In this paper, we offer a solution to this problem by showing how user demographic traits such as age and gender, and even political and religious views can be efficiently and accurately inferred based on their search query histories. This is accomplished in two steps; we first train predictive models based on the publically available myPersonality dataset containing users' Facebook Likes and their demographic information. We then match Facebook Likes with search queries using Open Directory Project categories. Finally, we apply the model trained on Facebook Likes to large-scale query logs of a commercial search engine while explicitly taking into account the difference between the traits distribution in both datasets. We find that the accuracy of classifying age and gender, expressed by the area under the ROC curve (AUC), are 77% and 84% respectively for predictions based on Facebook Likes, and only degrade to 74% and 80% when based on search queries. On a US state-by-state basis we find a Pearson correlation of 0.72 for political views between the predicted scores and Gallup data, and 0.54 for affiliation with Judaism between predicted scores and data from the US Religious Landscape Survey. We conclude that it is indeed feasible to infer important demographic data of users from their query history based on labelled Likes data and believe that this approach could provide valuable information for personalization and monetization even in the absence of demographic data.
It is too Hot: An In-Situ Study of Three Designs for Heating

Smart energy systems that leverage machine learning techniques are increasingly integrated in all aspects of our lives. To better understand how to design user interaction with such systems, we implemented three different smart thermostats that automate heating based on users' heating preferences and real-time price variations. We evaluated our designs through a field study, where 30 UK households used our thermostats to heat their homes over a month. Our findings through thematic analysis show that the participants formed different understandings and expectations of our smart thermostat, and used it in various ways to effectively respond to real-time prices while maintaining their thermal comfort. Based on the findings, we present a number of design and research implications, specifically for designing future smart thermostats that will assist us in controlling home heating with real-time pricing, and for future intelligent autonomous systems.
Comprehensive mutational analysis of BRCA1/BRCA2 for Korean breast cancer patients: evidence of a founder mutation

The BRCA1 and BRCA2 genes are the strongest susceptibility genes identified for breast cancer worldwide. However, BRCA1/BRCA2 have been incompletely investigated due to their large size and the genomic rearrangements that occasionally occur within them. Here we performed a comprehensive mutational analysis for BRCA1/BRCA2 in 206 Korean patients with breast cancer.
Inlined monitors for security policy enforcement in web applications

Improper input validation in Web Applications undermines their security and this may have disastrous consequences for the users. Input data can or cannot be harmful depending on how they are used with regard to the interactions with the clients and the accessed sensitive resources (e.g. databases and files). Existing application frameworks cannot guarantee safe input sanitization with respect to all vulnerabilities. Also, when legacy code is incorporated that was not originally written for the Web, its security hardening is costly and error-prone. We propose a reference monitor inlining approach that treats input injection vulnerabilities as a cross-cutting concern. Our monitors enforce high-level security policies for taint propagation control, by weaving checks and repair actions into the untrusted code. Taint policies are specified into JavaMOP, a programming framework for generating runtime monitors, which are weaved into the application through the automated Aspect Oriented Programming process. When monitor design is guided by preliminary static taint analysis, the incurred overhead can be reduced. Further improvements are feasible through JavaMOP's optimizations. As a proof of concept, we present the design and experimental validation of inlined monitors against SQL injection and cross-site scripting attacks.
Local feature view clustering for 3D object recognition

There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to individual training images. This paper presents a method for combining multiple images of a 3D object into a single model representation. This provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions. The decision of whether to cluster a training image into an existing view representation or to treat it as a new view is based on the geometric accuracy of the match to previous model views. A new probabilistic model is developed to reduce the false positive matches that would otherwise arise due to loosened geometric constraints on matching 3D and non-rigid models. A system has been developed based on these approaches that is able to robustly recognize 3D objects in cluttered natural images in sub-second times.
Stochastic convergence of random search to fixed size Pareto set approximations

This paper presents the first convergence result for random search algorithms to a subset of the Pareto set of given maximum size k with bounds on the approximation quality. The core of the algorithm is a new selection criterion based on a hypothetical multilevel grid on the objective space. It is shown that, when using this criterion for accepting new search points, the sequence of solution archives converges with probability one to a subset of the Pareto set that epsilon-dominates the entire Pareto set. The obtained approximation quality epsilon is equal to the size of the grid cells on the finest level of resolution that allows an approximation with at most k points within the family of grids considered. While the convergence result is of general theoretical interest, the archiving algorithm might be of high practical value for any type iterative multiobjective optimization method, such as evolutionary algorithms or other metaheuristics, which all rely on the usage of a finite on-line memory to store the best solutions found so far as the current approximation of the Pareto set.
Interpreting Assays with Control Flow on Digital Microfluidic Biochips

BioCoder is a C++ library developed at Microsoft Research, India, for the unambiguous specification of biochemical assays. This article describes language extensions to BioCoder along with a compiler and runtime system that translate and execute assays specified using BioCoder on a software simulator. The simulator mimics the behavior of laboratories-on-a-chip (LoCs) based on a droplet actuation technology called electrowetting on dielectric (EWoD). To date, prior compilers targeting similar EWoD devices are limited to assays specified as directed acyclic graphs (DAGs) and cannot handle arbitrary control flow or feedback from the LoC. The framework presented herein addresses these challenges through dynamic interpretation, thereby enlarging the space of assays that can be compiled onto EWoD devices.