Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Model Complexity in an Online Environment

In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification problems in the visual domain (such as recognizing handwriting, faces and human-body images) often require a large number of training examples, which may become available over a long training period. This motivates the development of scalable and adaptive systems which are able to continue learning at any stage and which can efficiently learn from large amounts of data, in an on-line manner. Previous approaches to on-line learning in visual classification have used a fixed parametric model, and focused on continuously improving the model parameters as more data becomes available. Here we propose a new framework which enables online learning algorithms to adjust the complexity of the learned model to the amount of the training data as more examples become available. Since in online learning the training set expands over time, it is natural to allow the learned model to become more complex during the course of learning instead of confining the model to a fixed family of a bounded complexity. Formally, we use a set of parametric classifiers y = hαθ (x) where y is the class and x the observed data. The parameter α controls the complexity of the model family. For a fixed α, the training examples are used for the optimal setting of θ. When the amount of data becomes sufficiently large, the value of α is increased, and a more complex model family is used. For evaluation of the proposed approach, we implement an online Support Vector Machine with increasing complexity, and evaluate in a task of handwritten character recognition on the MNIST database.
Protocol for Simulation of Falls in Watersports Activities

Falls are a frequent cause of unintentional injuries. The development of a water sports fall detection algorithm usually requires a dataset collection of fall events that are difficult to replicate in a laboratory environment. To address that problem, this article proposes a simulated boat falls protocol that can be used to record a dataset of falls for various water sports. A dataset of 296 samples comprised in 129 falls and in 167 non-fall events was gathered using the protocol. The data collection was made with 3 different smartphones and with 1 external IMU. A fall detection algorithm was trained with the dataset with machine learning techniques and tested over the same dataset and real sailing data. The algorithm achieved 99.9% of accuracy, 99% of specificity and 100% of sensitivity when tested in the dataset and detected the only fall that occurred in one hour and a half of a real sailing activitiy.
Automatic Document Logo Detection

Automatic logo detection and recognition continues to be of great interest to the document retrieval community as it enables effective identification of the source of a document. In this paper, we propose a new approach to logo detection and extraction in document images that robustly classifies and precisely localizes logos using a boosting strategy across multiple image scales. At a coarse scale, a trained Fisher classifier performs initial classification using features from document context and connected components. Each logo candidate region is further classified at successively finer scales by a cascade of simple classifiers, which allows false alarms to be discarded and the detected region to be refined. Our approach is segmentation free and lay-out independent. We define a meaningful evaluation metric to measure the quality of logo detection using labeled groundtruth. We demonstrate the effectiveness of our approach using a large collection of real-world documents.
Cannabinoids Promote Oligodendrocyte Progenitor Survival: Involvement of Cannabinoid Receptors and Phosphatidylinositol-3 Kinase/Akt Signaling

Cannabinoids exert pleiotropic actions in the CNS, including the inhibition of inflammatory responses and the enhancement of neuronal survival after injury. Although cannabinoid receptors are distributed widely in brain, their presence has not been investigated previously in oligodendrocytes. This study examined the expression of cannabinoid type 1 (CB1) receptors in rat oligodendrocytes in vivo and in culture and explored their biological function. Expression of CB1 receptors by oligodendrocytes was demonstrated immunocytochemically in postnatal and in adult white matter as well as in oligodendrocyte cultures. Reverse transcription-PCR and Western blotting further confirmed the presence of CB1 receptors. Oligodendrocyte progenitors undergo apoptosis with the withdrawal of trophic support, as determined by TUNEL assay and caspase-3 activation, and both the selective CB1 agonist arachidonyl-2′-chloroethylamide/(all Z)-N-(2-cycloethyl)-5,8,11,14-eicosatetraenamide (ACEA) and the nonselective cannabinoid agonists HU210 and (+)-Win-55212-2 enhanced cell survival. To investigate intracellular signaling involved in cannabinoid protection, we focused on the phosphatidylinositol-3 kinase (PI3K)/Akt pathway. HU210, (+)-Win-55212-2, and ACEA elicited a time-dependent phosphorylation of Akt. Pertussis toxin abolished Akt activation, indicating the involvement of Gi/Go-protein-coupled receptors. The CB1 receptor antagonist SR141716A partially inhibited Akt phosphorylation in response to HU210 and (+)-Win-55212-2 and abolished the effects of ACEA. Trophic support deprivation downregulated Akt activity, and cannabinoids recovered phospho-Akt levels. Inhibition of PI3K abrogated the survival action and the recovery of Akt activity in response to cannabinoids. SR141716A prevented only the protection conferred by ACEA. Nevertheless, SR141716A and the selective CB2 receptor antagonist SR144528 in combination inhibited the prosurvival action of HU210, which is in accordance with the finding of CB2 receptor expression by oligodendroglial cells. These data identify oligodendrocytes as potential targets of cannabinoid action in the CNS.
An Empirical Investigation of Personality Traits of Software Testers

Software testing is the process of an execution based investigation of some aspects of the software's quality. The efficiency of the process depends on the methods and technologies used, but crucially also on the human testers. Software testers typically attempt to anticipate and expose ways software may be defective, a fundamentally different task set to those of other software development practitioners. This raises the question of whether the personality of software testers may be different to other people involved in software development. To test this hypothesis, we collected personality profiles using the big five factor model of around 200 software development practitioners. Analysis of this data indicates that software testers are significantly higher on the conscientiousness factor than other software development practitioners, while other factors remain broadly consistent.
Intelligent Interaction Support for E-learning

Bi-directional communication, which enhances the effectiveness of education through question-and-answer and/or discussion sessions, is important in asynchronous e-Learning systems. Assignments are also important for e-Learning programs to motivate students and track their learning. However, assigned reports in e-Learning programs are submitted online, and it is very easy for students to plagiarize by pasting text from other reports or web sites. This chapter introduces intelligent interaction support systems including an intelligent bulletin board system (iBBS) and a plagiarism-detecting assignment management system (pAMS). Both systems provide text-based intelligent functions that apply natural language processing and support smooth communication and a fair evaluation of e-Learning classes. First, for iBBS, a function for the automated detection of important posting notifications (IPNs) is introduced. The IPN function performs information filtering; however, it is not feasible to estimate the statistics of word occurrences, which are required for conventional information filtering techniques, prior to opening a discussion. Nevertheless, since the subjects of discussion in e-Learning classes are always related to the class topic, the ontology is consistent throughout the discussion. A concept-based vector space model, which can virtually capture ontological information from a collection of documents, is therefore effective for the IPN function. Second, pAMS, an assignment management system with plagiarism detection, is introduced. Students electronically submit their assigned reports using pAMS and the system stores the submitted documents. The system then compares each of submitted reports to other reports and web pages, and detects identical or similar parts of text in the reports. The system then makes the teacher aware of any potential plagiarism. This chapter describes the method for identifying the similar parts of two reports, even if one has been skillfully modified from the other. In addition, the method of retrieving original source materials is introduced. Finally, the evaluation results using a limited set of reports are described. Intelligent Interaction Support for e-Learning 95
A Two-Stage Method for Active Learning of Statistical Grammars

Active learning reduces the amount of manually annotated sentences necessary when training state-of-the-art statistical parsers. One popular method, uncertainty sampling, selects sentences for which the parser exhibits low certainty. However, this method does not quantify confidence about the current statistical model itself. In particular, we should be less confident about selection decisions based on low frequency events. We present a novel two-stage method which first targets sentences which cannot be reliably selected using uncertainty sampling, and then applies standard uncertainty sampling to the remaining sentences. An evaluation shows that this method performs better than pure uncertainty sampling, and better than an ensemble method based on bagged ensemble members only.
Acquired port wine stains:clinical and psychological assessment and response to pulsed dye laser therapy

Nineteen patients are presented with acquired port wine stains. Acquired port wine stains are uncommon vascular lesions with the appearance of a congenital port wine stain but onset after birth. This is the largest group of patients reported to date. The acquired port wine stain was on the head and neck in 17 patients and in six this was associated with preceding trauma. Psychological assessments in adult patients showed similar morbidity to that seen in patients with congenital port wine stains. This morbidity was improved with successful laser treatment. Pulsed dye laser therapy resulted in complete clearance of the lesion in six (46%) of 13 patients treated. Two further patients had an excellent response.
Output-sensitive results on convex hulls, extreme points, and related problems

AbstractWe use known data structures for ray-shooting and linear-programming queries to derive new output-sensitive results on convex hulls, extreme points, and related problems. We show that thef-face convex hull of ann-point setP in a fixed dimensiond≥2 can be constructed in $$0\left( {n log f + \left( {nf} \right)^{1 - 1/\left( {\left[ {d/2} \right] + 1} \right)} \log ^{0\left( 1 \right)} n} \right)$$ time; this is optimal if $$f = 0\left( {n^{1/\left[ {d/2} \right]} /\log ^K n} \right)$$ for some sufficiently large constantK. We also show that theh extreme points ofP can be computed in $$0\left( {n log^{0\left( 1 \right)} h + \left( {nh} \right)^{1 - 1/\left( {\left[ {d/2} \right] + 1} \right)} \log ^{0\left( 1 \right)} n} \right)$$ time. These results are then applied to produce an algorithm that computes the vertices of all the convex layers ofP inO(n2−γ) time for any constant $$\gamma< 2/\left( {\left[ {d/2} \right]^2 + 1} \right)$$ . Finally, we obtain improved time bounds for other problems including levels in arrangements and linear programming with few violated constraints. In all of our algorithms the input is assumed to be in general position.
POLITECNICO DI TORINO Repository ISTITUZIONALE Gold Mining in a River of Internet Content Traffic /

With the advent of Over-The-Top content providers (OTTs), Internet Service Providers (ISPs) saw their portfolio of services shrink to the low margin role of data transporters. In order to counter this effect, some ISPs started to follow big OTTs like Facebook and Google in trying to turn their data into a valuable asset. In this paper, we explore the questions of what meaningful information can be extracted from network data, and what interesting insights it can provide. To this end, we tackle the first challenge of detecting “user-URLs”, i.e., those links that were clicked by users as opposed to those objects automatically downloaded by browsers and applications. We devise algorithms to pinpoint such URLs, and validate them on manually collected ground truth traces. We then apply them on a three-day long traffic trace spanning more than 19,000 residential users that generated around 190 million HTTP transactions. We find that only 1.6% of these observed URLs were actually clicked by users. As a first application for our methods, we answer the question of which platforms participate most in promoting the Internet content. Surprisingly, we find that, despite its notoriety, only 11% of the user URL visits are coming from Google Search.
Theory and modelling of light-matter interactions in photonic crystal cavity systems coupled to quantum dot ensembles

Photonic crystal microcavity quantum dot lasers show promise as high quality-factor, low threshold lasers, that can be integrated on-chip, with tunable room temperature operations. However, such semiconductor microcavity lasers are notoriously difficult to model in a self-consistent way and are primarily modelled by simplified rate equation approximations, typically fit to experimental data, which limits investigations of their optimization and fundamental light-matter interaction processes. Moreover, simple cavity mode optical theory and rate equations have recently been shown to fail in explaining lasing threshold trends in triangular lattice photonic crystal cavities as a function of cavity size, and the potential impact of fabrication disorder is not well understood. In this thesis, we develop a simple but powerful numerical scheme for modelling the quantum dot active layer used for lasing in these photonic crystal cavity structures, as an ensemble of randomly positioned artificial two-level atoms. Each two-level atom is defined by optical Bloch equations solved by a quantum master equation that includes phenomenological pure dephasing and an incoherent pump rate that effectively models a multi-level gain system. Light-matter interactions of both passive and lasing structures are analyzed using simulation defined tools and post-simulation Green function techniques. We implement an active layer ensemble of up to 24,000 statistically unique quantum dots in photonic crystal cavity simulations, using a self-consistent finite-difference time-domain method. This method has the distinct advantage of capturing effects such as dipole-dipole coupling and radiative decay, without the need for any phenomenological terms, since the time-domain solution self-consistently captures these effects. Our analysis demonstrates a powerful ability to connect with recent experimental trends, while remaining completely general in its set-up; for example, we do not invoke common approximations such as the rotating-wave or slowly-varying envelope approximations, and solve dynamics with zero a priori knowledge. i
Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.
The role of molecular genetics in sculpting the future of integrative biogeography

We review the expanding role of molecular genetics in the emergence of a vibrant and vital integrative biogeography. The enormous growth over the past several decades in the number and variety of molecular-based phylogenetic and population genetics studies has become the core information used by biogeographers to reconstruct the causal connections between historical evolutionary and ecological attributes of taxa and biotas, and the landscapes and seascapes that contain them. A proliferation of different approaches, sequences, and genomes have provided for the integration of a `biogeography of the Late Neogene' with other Earth and biological sciences under the rubrics of phylogeography, landscape genetics, and phylochronology. Approaches designed explicitly to take advantage of unique properties of molecular genetic information have led to the re-emergence of dispersal as an analytically tractable process that historical biogeographers can now use, along with vicariance, to reconstruct the geographical context of diversification. Concomitant with the expanding amount of information available, molecular data sets often provide for estimates of lineage divergence dates, and analytical tools for doing so continue to improve. The comparability of molecular-based estimates of phylogenetic and population genetic histories across non-related taxa has stimulated deployment of new methods to test for spatial and temporal congruence across co-distributed taxa and ecosystems, and thus increased rigour in hypothesis-testing. We illustrate how a molecular genetics framework has provided robust and novel reconstructions of historical biogeographical pattern and process in three different systems, and finish with some thoughts on the role a molecular genetic-based biogeography will play in predicting alternative futures of biodiversity.
Repair between Dose Fractions: A Simpler Method of Analyzing and Reporting Apparently Biexponential Repair1

Abstract Fowler, J. F. Repair between Dose Fractions: A Simpler Method of Analyzing and Reporting Apparently Biexponential Repair. Radiat. Res. 158, 141–151 (2002). Increasing numbers of animal experiments in situ are reporting that repair of sublethal radiation damage in vivo slows down with time, usually described as two components of (monoexponential) repair. For repair of DNA strand breaks, plotting the reciprocal of proportion unrepaired as a function of time yielded straight lines. Two processes have been suggested as causing this: (1) a second-order process (bimolecular) instead of first-order (exponential) and (2) a skewed distribution of monoexponential rates. The present paper investigates whether such plots of hyperbolic or reciprocal repair are relevant for laboratory animal tissue results. Published repair data were reanalyzed from laboratory animal experiments that employed split doses or two fractions per day. Graphs are presented of the reciprocal proportion of damage remaining as a function of the interval between the two doses. If the reciprocal model applies, the graphs would be straight lines. Different animal data showed no inconsistency with straight reciprocal plots. These reciprocal plots describe well with one parameter τ, the first half-time, repair curves previously thought to be “biexponential”, and to require three parameters. Straight reciprocal plots mean that in a constant time interval τ the unrepaired damage falls from 1 to ½, then from ½ to ⅓, then ⅓ to ¼, etc. A much larger proportion of damage would therefore remain unrepaired at several half-times than is estimated by current mono- or biexponential models. The practical implications for clinical radiotherapy are important.
Brain Activation by Peptide Pro-Leu-Gly-NH2 (MIF-1)

MIF-1 (Pro-Leu-Gly-NH2) is a tripeptide for which the therapeutic potential in Parkinson's disease and depression has been indicated by many studies. However, the cellular mechanisms of action of MIF-1 are not yet clear. Here, we show the specific brain regions responsive to MIF-1 treatment by c-Fos mapping, and determine the kinetics of cellular signaling by western blotting of pERK, pSTAT3, and c-Fos in cultured neurons. The immunoreactivity of c-Fos was increased 4 hours after MIF-1 treatment in brain regions critically involved in the regulation of mood, anxiety, depression, and memory. The number of cells activated was greater after peripheral treatment (intravenous delivery) than after intracerebroventricular injection. In cultured SH-SY5Y neuronal cells, c-Fos was induced time- and dose-dependently. The activation of cellular c-Fos was preceded by a transient increase of mitogen-activated protein kinase pERK but a reduction of phosphorylated Signal Transducer and Activator of Transcription (pSTAT3) initially. We conclude that MIF-1 can modulate multiple cellular signals including pERK, and pSTAT3 to activate c-Fos. The cellular activation in specific brain regions illustrates the biochemical and neuroanatomical basis underlying the therapeutic effect of MIF-1 in Parkinson's disease and depression.
Aircraft routing under the risk of detection

The deterministic problem for finding an aircraft's optimal risk trajectory in a threat environment has been formulated. The threat is associated with the risk of aircraft detection by radars or similar sensors. The model considers an aircraft's trajectory in three‐dimensional (3‐D) space and represents the aircraft by a symmetrical ellipsoid with the axis of symmetry directing the trajectory. Analytical and discrete optimization approaches for routing an aircraft with variable radar cross‐section (RCS) subject to a constraint on the trajectory length have been developed. Through techniques of Calculus of Variations, the analytical approach reduces the original risk optimization problem to a vectorial nonlinear differential equation. In the case of a single detecting installation, a solution to this equation is expressed by a quadrature. A network optimization approach reduces the original problem to the Constrained Shortest Path Problem (CSPP) for a 3‐D network. The CSPP has been solved for various ellipsoid shapes and different length constraints in cases with several radars. The impact of ellipsoid shape on the geometry of an optimal trajectory as well as the impact of variable RCS on the performance of a network optimization algorithm have been analyzed and illustrated by several numerical examples. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006