Miracle-dz/UniFAR-FTUs huggingface dataset:
Histograms of Oriented Gradients

A useful question to ask of an image is whether it contains one or more instances of a certain object: a person, a face, a car, and so forth. Algorithms that answer this question are called object detectors. They often work by asking the same question in turn of every possible rectangle in the image that might possibly tightly bound one of the instances of interest. For instance, when detecting a pedestrian walking by a camera, one could start with a window that is 128 pixels tall and 64 pixels wide: The 2 to 1 aspect ratio of such a rectangle is a rough compromise between the aspect ratio of a person viewed from the front and one viewed from the side with legs fully extended during a step. The actual size of the window—using multiples of 64 is a mere convenience—reflects the assumption that if the image of the person is significantly smaller than that then resolution might be insufficient to detect it reliably, so it is not even worth trying. The person could of course occupy a bigger part of the image. Because of this, one analyzes not only the original image, but also those in a pyramid of images: A 128 by 64 rectangle at a coarser level of the pyramid corresponds to a larger rectangle in the original image (see Figure 1). The object detector then slides such a window over every image of the pyramid, perhaps in increments of a few pixels—the increment is called the stride of the detector—and computes a feature at every position, that is, a vector of numbers that describes the window’s contents. The feature is fed to the classifier, which tells whether the window contains a pedestrian. The answer is likely to be positive for a set of windows that overlap the person’s image, so the detector then chooses a single pixel to represent each connected set of positive classifier outputs. This scheme generalizes the correlation approach we saw in an earlier note: In that instance, the feature is a
Leadership Program Effects on Student Learning: The Case of the Greater New Orleans School Leadership Center

Demonstrating the contribution of leadership development programs to student learning has become a problem of considerable recent interest to both school reformers and those attempting to improve how school leaders are prepared. This article describes an innovative approach to improving school leadership developed by the Greater New Orleans School Leadership Center. Longitudinal evidence from a four-and-a-half-year external evaluation of the effects of the center program on schools and students is summarized. Promising evidence of program effects are reported using state-collected achievement data and measures of student engagement with school collected specifically for the study in each leader's school. The article highlights lessons from this case that may be of general value for others engaged in leadership preparation initiatives and their evaluation.
Russian WordNet From UML-notation to Internet / Intranet Database Implementation

This paper deals with development of the first public web version of Russian WordNet and future parallel English-Russian and multiligual web versions of WordNet. It describes usage of Russian and English-Russian lexical language resources and software to process WordNet for Russian language and design of a database management systems for efficient storage and retrieval of various kinds of lexical information needed to process WordNet. Relevant aspects of the UML data models, XML format and related technologies are surveyed. The pilot Internet/Intranet version of described system based on Oracle 9i DBMS and Java technology is published at: http://www.pgups. ru/WebWN/wordnet.uix.
Distance-based Speed Functions for Level Set Methods in Image Segmentation

Level set methods are a well-known means for the segmentation of objects in image data. In this paper, we discuss a class of easy to calculate terms based on distance measures for integration into the level set speed function. We will give examples for the use of these distance-based terms as a stopping criterion in the absence of reliable image features and as an acceleration force for the propagating front to connect parts of a single object. A comparison of the results with a level set segmentation not using any distance measures will be given to demonstrate the improvement of segmentation results. 1 Level Set Speed Functions Level set methods have become popular for many domains of science as diverse as computer graphics, simulation of crystal growth or fluid mechanics. In image processing they are often used for segmentation. Because of their implicit definition level sets are independent of the number of degrees of freedom of the object of interest as well as the number of dimensions of the underlying data. Level Sets describe the propagation process of a closed curve C ∈Rn. This curve C is represented as the zero level set of a higher dimensional function Φ ∈Rn+1 given by
Full medical support for intracerebral hemorrhage

Objective: This study tested the hypothesis that patients without placement of new do-not-resuscitate (DNR) orders during the first 5 days after intracerebral hemorrhage (ICH) have lower 30-day mortality than predicted by the ICH Score without an increase in severe disability at 90 days. Methods: This was a prospective, multicenter, observational cohort study at 4 academic medical centers and one community hospital. Adults (18 years or older) with nontraumatic spontaneous ICH, Glasgow Coma Scale score of 12 or less, who did not have preexisting DNR orders were included. Results: One hundred nine subjects were enrolled. Mean age was 62 years; median Glasgow Coma Scale score was 7, and mean hematoma volume was 39 cm3. Based on ICH Score prediction, the expected overall 30-day mortality rate was 50%. Observed mortality was substantially lower at 20.2%, absolute average difference 29.8% (95% confidence interval: 21.5%–37.7%). At 90 days, 27.1% had died, 21.5% had a modified Rankin Scale score = 5 (severe disability). A good outcome (modified Rankin Scale score 0–3) was achieved by 29.9% and an additional 21.5% fell into the moderately severe disability range (modified Rankin Scale score = 4). Conclusions: Avoidance of early DNR orders along with guideline concordant ICH care results in substantially lower mortality than predicted. The observed functional outcomes in this study provide clinicians and families with data to determine the appropriate goals of treatment based on patients' wishes.
Nonlinear state-dependent Riccati equation control of a quadrotor UAV

Small quadrotor UAVs represent a very interesting class of small flying robots because of their ability to fly in- and outdoor. Therefore, these vehicles have an enormous potential for near-area surveillance and exploration. However, especially indoor flight is a difficult task for vehicle control which has to stabilize the desired velocity vector and the required attitude of the quadrotor. This paper mainly describes the development of such a nonlinear vehicle control system based on state-dependent Riccati equations (SDRE). The controller is integrated in an overall mission system concept for UAVs.
Perturbations of fusion frames and the effect on their canonical dual

Measurements in real world applications are often corrupted by noise due to exterior influences. It is therefore convenient to investigate perturbations of the utilized measurement systems. In the present paper, we consider small perturbations of fusion frames in Hilbert spaces and study the effect on the canonical duals of original and perturbed fusion frame. It turns out that the duals are stable under these perturbations.
Desorption of Lipases Immobilized on Octyl-Agarose Beads and Coated with Ionic Polymers after Thermal Inactivation. Stronger Adsorption of Polymers/Unfolded Protein Composites

Lipases from Candida antarctica (isoform B) and Rhizomucor miehei (CALB and RML) have been immobilized on octyl-agarose (OC) and further coated with polyethylenimine (PEI) and dextran sulfate (DS). The enzymes just immobilized on OC supports could be easily released from the support using 2% SDS at pH 7, both intact or after thermal inactivation (in fact, after inactivation most enzyme molecules were already desorbed). The coating with PEI and DS greatly reduced the enzyme release during thermal inactivation and improved enzyme stability. However, using OC-CALB/RML-PEI-DS, the full release of the immobilized enzyme to reuse the support required more drastic conditions: a pH value of 3, a buffer concentration over 2 M, and temperatures above 45 °C. However, even these conditions were not able to fully release the thermally inactivated enzyme molecules from the support, being necessary to increase the buffer concentration to 4 M sodium phosphate and decrease the pH to 2.5. The formation of unfolded protein/polymers composites seems to be responsible for this strong interaction between the octyl and some anionic groups of OC supports. The support could be reused five cycles using these conditions with similar loading capacity of the support and stability of the immobilized enzyme.
Signatures of Quantum-like Chaos in Dow Jones Index

Dow Jones Index time series exhibit irregular or fractal fluctuations on all time scales from days, months to years. The apparently irregular (nonlinear) fluctuations are selfsimilar as exhibited in inverse power law form for power spectra of temporal fluctuations. Inverse power law form for power spectra of fractal fluctuations in space or time is generic to all dynamical systems in nature and is identified as self-organized criticality. Selfsimilarity implies long-range space-time correlations or non-local connections. It is important to quantify the total pattern of fractal fluctuations for predictability studies, e.g., weather and climate prediction, stock market trends, etc. The author has developed a general systems theory for universal quantification of the observed inverse power law spectra in dynamical systems. The model predictions are as follows. (1) The power spectra of fractal fluctuations follow the universal and unique inverse power law form of the statistical normal distribution. (2) The nonlocal connections or long-range correlations in space or time exhibited by the fractal fluctuations are signatures of quantum-like chaos in dynamical systems. (3) The apparently irregular geometry of the fractal fluctuations forms the component parts of a unified whole precise geometrical pattern of the logarithmic spiral with quasiperiodic Penrose tiling pattern for the internal structure. Conventional power spectral analyses will resolve the logarithmic spiral pattern as an eddy continuum with progressive increase in eddy phase angle. (4) Continuous periodogram power spectral analyses of normalised daily, monthly and annual Dow Jones Index for the past 100-years show that the power spectra follow the universal inverse power law form of the statistical normal distribution in agreement with model prediction. The fractal fluctuations of the non-stationary Dow Jones Index time series therefore exhibit signature of quantum-like chaos on all time scales from days to years.
Improved speaker independent lip reading using speaker adaptive training and deep neural networks

Recent improvements in tracking and feature extraction mean that speaker-dependent lip-reading of continuous speech using a medium size vocabulary (around 1000 words) is realistic. However, the recognition of previously unseen speakers has been found to be a very challenging task, because of the large variation in lip-shapes across speakers and the lack of large, tracked databases of visual features, which are very expensive to produce. By adapting a technique that is established in speech recognition but has not previously been used in lip-reading, we show that error-rates for speaker-independent lip-reading can be very significantly reduced. Furthermore, we show that error-rates can be even further reduced by the additional use of Deep Neural Networks (DNN). We also find that there is no need to map phonemes to visemes for context-dependent visual speech transcription.
Are fast explorers slow reactors? Linking personality type and anti-predator behaviour

Response delays to predator attack may be adaptive, suggesting that latency to respond does not always reflect predator detection time, but can be a decision based on starvation–predation risk trade-offs. In birds, some anti-predator behaviours have been shown to be correlated with personality traits such as activity level and exploration. Here, we tested for a correlation between exploration behaviour and response latency time to a simulated fish predator attack in a fish species, juvenile convict cichlids (Amatitlania nigrofasciata). Individual focal fish were subjected to a standardized attack by a robotic fish predator while foraging, and separately given two repeated trials of exploration of a novel environment. We found a strong positive correlation between exploration and time taken to respond to the predator model. Fish that were fast to explore the novel environment were slower to respond to the predator. Our study therefore provides some of the first experimental evidence for a link between exploration behaviour and predator-escape behaviour. We suggest that different behavioural types may differ in how they partition their attention between foraging and anti-predator vigilance.
YouTube: big data analytics using Hadoop and map reduce

We live today in a digital world a tremendous amount of data is generated by each digital service we use. This vast amount of data generated is called Big Data. According to Wikipedia, Big Data is a word for large data sets or compositions that the traditional data monitoring application software is pitiful to compress [5]. Extensive data cannot be used to receive data, store data, analyse data, search, share, transfer, view, consult, and update and maintain the confidentiality of information. Google's streaming services, YouTube, are one of the best examples of services that produce a massive amount of data in a brief period. Data extraction of a significant amount of data is done using Hadoop and MapReduce to measure performance. Hadoop is a system that offers consistent memory. Storage is provided by HDFS (Hadoop Distributed File System) and MapReduce analysis. MapReduce is a programming model and a corresponding implementation for processing large data sets. This article presents the analysis of Big Data on YouTube using the Hadoop and MapReduce techniques.
A Semantic Design Pattern Language for Complex Event Processing

Complex Event Processing (CEP) is an emerging enabling technology to achieve relevant situational knowledge from distributed systems in real-time or almost realtime. It gains more and more attention due to the industry hype and momentum regarding situation-aware computing and service-orientation. Support for CEP is considered to become a critical success factor for many businesses, especially regarding the recent failures of many industrial ventures due to insufficient situation-awareness. However, first experiences in using the CEP technology and setting up CEP applications have shown that the potential adopters have major problems in understanding and adequately designing and implementing successful CEP solutions. CEP engineering remains a laborious trial and error process with slow development and change cycles.
Bridging the educational research‐teaching practice gap

External representations (ERs), such as diagrams, animations, and dynamic models are vital tools for communicating and constructing knowledge in biochemistry. To build a meaningful understanding of structure, function, and process, it is essential that students become visually literate by mastering key cognitive skills that are essential for interpreting and visualizing ERs. In this article, first we describe a model of seven factors influencing students' ability to learn from ERs. Second, we use this model and relevant literature to identify eight cognitive skills central to visual literacy in biochemistry. Third, we present simple examples of tasks as a foundation for designing more sophisticated and complex items for assessing and developing students' visual literacy. We conclude that visual literacy is fundamental to the development of sound conceptual understanding and it is crucial to develop visual skills in parallel with meaningful learning outcomes in all biochemistry curricula.
Joint segmentation and activity discovery using semantic and temporal priors

We introduce a hierarchical nonparametric topic modeling approach to infer activity routines from context sensor data streams based on a distance dependent Chinese restaurant process (ddCRP). Our approach does not require labeled data at any stage. Neither does our approach depend on time-invariant sliding windows to sample context word statistics. Our activity discovery approach builds on the idea that context words occurring within one activity are semantically similar, whereas context words of different activities are less similar. Context word streams are segmented into supersamples and then semantic and temporal features are obtained to construct a segmentation prior that relates supersamples via its context words. Our hierarchical model uses the segmentation prior and ddCRP to group supersamples and the Chinese restaurant process (CRP) to discover activities. We evaluate our approach using the Opportunity dataset that contains activities of daily living. Besides being nonparametric, our ddCRP based model outperforms both, classic parametric latent Dirichlet allocation (LDA) and the nonparametric Chinese restaurant franchise (CRF). We conclude that ddCRP+CRP is an adequate approach for fully unsupervised activity discovery from context sensor data.
Bounding The Power of Combination Network Coding in Undirected Networks

We refer to network coding schemes in which information flows propagate along a combination network topology as combination network coding (CNC). CNC and its variations are the first network coding schemes studied in the literature, and so far still represent arguably the most imp ortant class of known structures where network coding is nontrivial. Our main goal in this paper is to seek a thoroug h nderstanding on the power of CNC in undirected networks, by proving a tight bound on its potential both in im proving multicast throughput (the coding advantage) and in reducing multicast cost under a linear link flow cost mo del (the cost advantage). Towards this goal, we prepare three necessary results. First, we show that the cos t advantage of CNC is upper-bounded by 98 under the uniform link cost setting. Second, we show that achieving a l arger cost advantage is impossible by considering an arbitrary instead of uniform link cost configuration. Third , we show that in a given network topology, for any form of network coding, the coding advantage under arbitrary lin k capacity configurations is always upper-bounded by the cost advantage under arbitrary link cost configurations . Combining the three results together, we conclude that the potential for CNC to improve throughput and to reduce rou ting cost are both upper-bounded by 98 . The bound is tight since it is achieved in specific networks. This resul t can be viewed as a natural step towards improving the bound of2 proved for the coding advantage of general multicast networ k c ding.
Color correction through region matching leveraged by point correspondences

This paper addresses the problem of region-based color correction. Our solution begins with the image segmentation by marker-controlled watershed transformation, which is faster and produces more uniform regions with better adherence to object boundaries than the segmentation in previous works. Next, regions between two images are matched using point feature correspondences which are invariant to geometric transformation and illumination changes. Finally, the color-corrected image is generated from the color transfer functions of corresponding regions. We demonstrate the results of this approach using different sets of images.
Incorporating Background Knowledge into Text Classification

It has been shown that prior knowledge and information are organized according to categories, and that also background knowledge plays an important role in classification. The purpose of this study is first, to investigate the relationship between background knowledge and text classification, and second, to incorporate this relationship in a computational model. Our behavioral results demonstrate that participants with access to background knowledge (experts), overall performed significantly better than those without access to this knowledge (novices). More importantly, we show that experts rely more on relational features than surface features, an aspect that bagof-words methods fail to capture. We then propose a computational model for text classification which incorporates background knowledge. This model is built upon vector-based representation methods and achieves significantly more accurate results over other models that were tested.
Combining Relational and Attributional Similarity for Semantic Relation Classification

We combine relational and attributional similarity for the task of identifying instances of semantic relations, such as PRODUCT-PRODUCER and ORIGINENTITY, between nominals in text. We use no pre-existing lexical resources, thus simulating a realistic real-world situation, where the coverage of any such resource is limited. Instead, we mine the Web to automatically extract patterns (verbs, prepositions and coordinating conjunctions) expressing the relationship between the relation arguments, as well as hypernyms and co-hyponyms of the arguments, which we use in instance-based classifiers. The evaluation on the dataset of SemEval-1 Task 4 shows an improvement over the state-ofthe-art for the case where using manually annotated WordNet senses is not allowed.
Towards a Unified Account of PersonSplits

Different phenomena are known to exhibit sensitivity to the person/animacy (P/A) features of clausal arguments. One example is P/A-based split-ergativity. While individual languages exhibiting such patterns differ in where along Silverstein’s (1976) animacy hierarchy the split occurs, there is a universal trend for DPs lower on the hierarchy to trigger the ergative pattern, and ones that are higher to trigger the nominative pattern. Another, less well-known, example of grammatical sensitivity to P/A features is auxiliary selection in Romance. In certain Romance dialects, auxiliary alternations are constrained by P/A features of the subject (D’Alessandro & Roberts 2010, henceforth DR and that the phasehood of this projection causes the the aforementioned bifurcation of the clause into two case/agreement domains. In the domain of aspect and P/A-split ergativity, the splits are not the result of special rules of case assignment, but rather the effect of bifurcating a transitive clause into two case domains with a single argument in each. Finally, with respect to auxiliary selection, this same phase boundary blocks incorporation into be (Freeze 1992; Kayne 1993) in the presence of 1st/2nd person, preventing have from arising. We thus offer a unified account of aspect-based and P/A-based split ergativity, which also extends to person-conditioned auxiliary selection.