Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of breast tissues into mass and non-mass by means of the micro-genetic algorithm, phylogenetic trees, LBP and SVM

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.
Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation

Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.
IGF1R levels in the brain negatively correlate with longevity in 16 rodent species

The insulin/insulin-like growth factor signaling (IIS) pathway is a major conserved regulator of aging. Nematode, fruit fly and mouse mutants with reduced IIS signaling exhibit extended lifespan. These mutants are often dwarfs leading to the idea that small body mass correlates with longevity within species. However, when different species are compared, larger animals are typically longer-lived. Hence, the role of IIS in the evolution of life history traits remains unresolved. Here we used comparative approach to test whether IGF1R signaling changes in response to selection on lifespan or body mass and whether specific tissues are involved. The IGF1R levels in the heart, lungs, kidneys, and brains of sixteen rodent species with highly diverse lifespans and body masses were measured via immunoblot after epitope conservation analysis. We report that IGF1R levels display strong negative correlation with maximum lifespan only in brain tissue and no significant correlations with body mass for any organ. The brain-IGF1R and lifespan correlation holds when phylogenetic non-independence of data-points is taken into account. These results suggest that modulation of IGF1R signaling in nervous tissue, but not in the peripheral tissues, is an important factor in the evolution of longevity in mammals.
The Dust and Gas Around β Pictoris

We have obtained Spitzer IRS 5.5-35 μm spectroscopy of the debris disk around β Pictoris. In addition to the 10 μm silicate emission feature originally observed from the ground, we also detect the crystalline silicate emission bands at 28 and 33.5 μm. This is the first time that the silicate bands at wavelengths longer than 10 μm have ever been seen in the β Pictoris disk. The observed dust emission is well reproduced by a dust model consisting of fluffy cometary and crystalline olivine aggregates. We searched for line emission from molecular hydrogen and atomic [S I], Fe II, and Si II gas but detected none. We place a 3 σ upper limit of <17 M⊕ on the H2 S(1) gas mass, assuming an excitation temperature of Tex = 100 K. This suggests that there is less gas in this system than is required to form the envelope of Jupiter. We hypothesize that some of the atomic Na I gas observed in Keplerian rotation around β Pictoris may be produced by photon-stimulated desorption from circumstellar dust grains.
Network-aware search in social tagging applications: instance optimality versus efficiency

We consider in this paper top-k query answering in social applications, with a focus on social tagging. This problem requires a significant departure from socially agnostic techniques. In a network- aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose algorithms that have the potential to scale to current applications. While the problem has already been considered in previous literature, this was done either under strong simplifying assumptions or under choices that cannot scale to even moderate-size real-world applications. We first revisit a key aspect of the problem, which is accessing the closest or most relevant users for a given seeker. We describe how this can be done on the fly (without any pre- computations) for several possible choices -- arguably the most natural ones -- of proximity computation in a user network. Based on this, our top-k algorithm is sound and complete, addressing the applicability issues of the existing ones. Moreover, it performs significantly better in general and is instance optimal in the case when the search relies exclusively on the social weight of tagging actions. To further address the efficiency needs of online applications, for which the exact search, albeit optimal, may still be expensive, we then consider approximate algorithms. Specifically, these rely on concise statistics about the social network or on approximate shortest-paths computations. Extensive experiments on real-world data from Twitter show that our techniques can drastically improve response time, without sacrificing precision.
Impact of Tumor Size and Tracer Uptake Heterogeneity in 18F-FDG PET and CT Non–Small Cell Lung Cancer Tumor Delineation

The objectives of this study were to investigate the relationship between CT- and 18F-FDG PET–based tumor volumes in non–small cell lung cancer (NSCLC) and the impact of tumor size and uptake heterogeneity on various approaches to delineating uptake on PET images. Methods: Twenty-five NSCLC cancer patients with 18F-FDG PET/CT were considered. Seventeen underwent surgical resection of their tumor, and the maximum diameter was measured. Two observers manually delineated the tumors on the CT images and the tumor uptake on the corresponding PET images, using a fixed threshold at 50% of the maximum (T50), an adaptive threshold methodology, and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Maximum diameters of the delineated volumes were compared with the histopathology reference when available. The volumes of the tumors were compared, and correlations between the anatomic volume and PET uptake heterogeneity and the differences between delineations were investigated. Results: All maximum diameters measured on PET and CT images significantly correlated with the histopathology reference (r > 0.89, P < 0.0001). Significant differences were observed among the approaches: CT delineation resulted in large overestimation (+32% ± 37%), whereas all delineations on PET images resulted in underestimation (from −15% ± 17% for T50 to −4% ± 8% for FLAB) except manual delineation (+8% ± 17%). Overall, CT volumes were significantly larger than PET volumes (55 ± 74 cm3 for CT vs. from 18 ± 25 to 47 ± 76 cm3 for PET). A significant correlation was found between anatomic tumor size and heterogeneity (larger lesions were more heterogeneous). Finally, the more heterogeneous the tumor uptake, the larger was the underestimation of PET volumes by threshold-based techniques. Conclusion: Volumes based on CT images were larger than those based on PET images. Tumor size and tracer uptake heterogeneity have an impact on threshold-based methods, which should not be used for the delineation of cases of large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor in such cases. For an accurate delineation of PET volumes in NSCLC, advanced image segmentation algorithms able to deal with tracer uptake heterogeneity should be preferred.
Adaptive control of wing rock system in uncertain environment using contraction theory

Contraction theory is used to study stability based on differential and incremental behaviour of trajectories of a system with respect to each other. Application of contraction theory provides a platform to analyze the exponential stability of nonlinear systems. This paper considers the design of a control law for wing rock system using contraction theory principles. An adaptive control design approach based on contraction theory is proposed for controlling the dynamics of such systems for the cases with and without uncertainty in parameters. An adaptive control law along with parameter updation law is derived for uncertain wing rock system to achieve convergence of trajectories of actual system to that of desired system. The analysis made in the paper leads to quite simple results avoiding complexities involved with Lyapunov method. Finally numerical simulations are presented to justify the effectiveness of the proposed controller.
Impact of Natural and Artificial Barriers to Dispersal on the Population Structure of Bobcats

Abstract We investigated population structure and genetic diversity for bobcats (Lynx rufus) in Michigan, USA, which are distributed throughout the upper peninsula (UP) and the northern half of the lower peninsula (LP) of Michigan. Specifically, we assessed the influence of natural and artificial barriers to dispersal on the genetic population structure of the bobcat across Michigan, as well as in each peninsula. We used 5 microsatellite markers and the statistical package STRUCTURE to identify populations and assign individuals to their population of origin. STRUCTURE identified one population in each peninsula, indicating that the UP and LP are genetically isolated by the Straits of Mackinac which divide the UP and LP. Despite a greater density of roads in the LP, we found no evidence that they have led to intra-peninsular population structure. Our results suggest that, from a genetic standpoint, management agencies do not need to be concerned about the fragmenting effects of roads when producing management plans for bobcats.
Language diarization for code-switch conversational speech

This paper examines the process of language diarization, the process to perform language segmentation and recognition, in a code-switched speech. Towards this task, we have developed a 63 hours conversational code-switch corpus recorded from Singapore/Malaysia speakers. We show that code-switching can occur frequently and the average language interval may be as short as one second. As such, language diarization is challenging task. To process such short segments, we propose a language diarization system using long term context feature across several phone-based segments and the combination of acoustics and phonotactic information. We achieved a frame error rate of 14.7% for language diarization performance on a Mandarin-English code-switch corpus. To evaluate our system, we measured the language recognition performance on monolingual segments extracted from the code-switch corpus against published techniques of LID systems - we obtained a relative equal error rate reduction of 5.2%, 13.8%, 15.1% and 17.9% on speech durations of 0.1 to 0.5 sec. , 0.5 to 1 sec. , 1 to 3 sec. and 3 to 9 sec respectively.
Ensembles of Neural Networks for Soft Classification of Remote Sensing Images

In recent years, the remote-sensing community has became very interested in applying neural networks to image classification and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classification algorithm can be regarded as a “panacea”. In this paper, we propose the use of “ensembles” of neural networks as an alternative approach based on the exploitation of the complementary characteristics of different neural classifiers. Classification results provided by neural networks contained in these ensembles are “merged” according to statistical combination methods. In addition, the use of ensembles formed by neural and statistical classifiers is considered. Experimental results on a multisensor remotesensing data set are reported that point out that the use of neural networks ensembles can constitute a valid alternative to the development of new neural classifiers “more complex” than the present ones. In particular, we show that the combination of results provided by statistical and neural algorithms provides classification accuracies better than the ones obtained by single classifiers after long “designing” phases.
Learning Bayesian Network Classifiers with Labeled and Unlabeled Data

Classifiers based on Bayesian networks are usually learned with a fixed structure or a small subset of possible structures. In the presence of unlabeled data this strategy can be detrimental to classification performance, when the assumed classifier structure is incorrect. In this paper we present a classification driven learning method for Bayesian network classifiers that is based on Metropolis-Hastings sampling. We first show that this learning method outperforms existing approaches for fully labeled datasets. We then show that the method is successful in dealing with unlabeled data. Provided we have abundant unlabeled data, the learning method can process scarce labeled data to produce classifiers that attain performance comparable to classifiers learned with large amounts of fully labeled data.
Learning Speech as Acoustic Sequences with the Unsupervised Model, TOM

Most connectionist systems do not involve the temporal dimension. However, some neural networks attempt to take time into account either inside or outside the network. Speech recognition is a stochastic problem that involves a dynamic parameter. The recognitionof a unit of speech depends on the contextual information i.e., the parts around it. Our approach consists of interpreting the speech signal as a sequence of acoustic informationand designing a neural network, called TOM or Temporal Organization Map, in order to learn these sequences. The basis of TOM is a self-organized map containing super-units which are closer to the cortical column model than the MacCulloch and Pitts formal neuron. These super-units can intrinsically differentiate the stimuli according to the contextual information. Temporal distortion is taken into account by the network and the results obtained with this system, which does not include a priori knowledge on the speech signal, are interesting. We present an application to French and English spoken digit recognition.
Approximation-aware scheduling on heterogeneous multi-core architectures

The high performance demand of embedded systems along with restrictive thermal design power (TDP) constraint have lead to the emergence of the heterogenous multi-core architectures, where cores with the same instruction-set architecture but different power-performance characteristics provide new opportunities for energy-efficient computing. Heterogeneity introduces challenges in scheduling the tasks to the appropriate cores and selecting the frequency assignment of each core. In this paper, we introduce an approximation-aware scheduling framework for soft real-time tasks on the heterogeneous multi-core architectures. We consider multiple versions of a task obtained by introducing approximation in the computation to provide different levels of quality of service (QoS) versus performance tradeoffs. The additional choice of approximation allows us more flexibility in meeting the performance and TDP constraints while maximizing QoS per unit of energy.
Prioritized shaping of models for solving DEC-POMDPs

An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to converge and is empirically better, in terms of solution quality and planning time, than the existing prioritization scheme for some problems.
Guidelines for the management of spontaneous intracerebral hemorrhage: A statement for healthcare professionals from a special writing group of the Stroke Council, American Heart Association.

Intracerebral hemorrhage (ICH) is more than twice as common as subarachnoid hemorrhage (SAH) and is much more likely to result in death or major disability than cerebral infarction or SAH.1 Although >315 randomized clinical therapeutic trials for acute ischemic stroke and 78 trials for SAH were complete or ongoing (oral communication, Cochrane Collaboration, May 16, 1995) as of 1995, only the results of 4 small randomized surgical trials (353 total patients)2 3 4 5 and 4 small medical trials (513 total patients)6 7 8 9 of ICH had been published. In these small randomized studies, neither surgical nor medical treatment has been shown conclusively to benefit patients with ICH. Advancing age and hypertension are the most important risk factors for ICH.10 11 12 13 14 15 ICH occurs slightly more frequently among men than women and is significantly more common among young and middle-aged blacks than whites of similar ages.10 16 Reported incidence rates of ICH among Asian populations are also higher than those reported for whites in the United States and Europe. Pathophysiological change in small arteries and arterioles due to sustained hypertension is generally regarded as the most important cause of ICH.11 12 14 17 18 Cerebral amyloid angiopathy is increasingly recognized as a cause of lobar ICH in the elderly.19 20 21 22 23 Other causes of ICH include vascular malformations, ruptured aneurysms, coagulation disorders, use of anticoagulants and thrombolytic agents, hemorrhage into a cerebral infarct, bleeding into brain tumors, and drug abuse.10 Of the estimated 37 000 Americans who experienced an ICH in 1997, 35% to 52% were dead at 1 month; half of the deaths occurred within the first 2 days.1 17 24 Only 10% of patients were living independently at 1 month; 20% were independent …
Long-Term Treatment of Native LDL Induces Senescence of Cultured Human Endothelial Cells

The study aimed to evaluate whether the treatment of primary cultured human endothelial cells with native low-density lipoprotein (nLDL) could induce their senescence and to uncover some of the putative mechanisms involved. For this purpose, human umbilical vein endothelial cells (HUVECs) were subcultured and/or continuously cultured with nLDL (0, 2, 5, and 10 μg protein/mL), for up to 9 days. The results indicated that nLDL inhibited the proliferation of HUVECs by arresting the cell cycle at G1 phase. The G1-arrested cells showed increase in cytosolic senescence-associated-β-galactosidase (SA-β-Gal) activity, a biomarker of cellular senescence. The causative factor of the cellular senescence was nLDL itself and not oxidized LDL (oxLDL), since blocking LDL receptor (LDLR) with the anti-LDLR antibody opposed the nLDL-induced increase of SA-β-Gal activity and decrease of cellular proliferation. In addition, nLDL-induced cellular senescence by inhibiting the phosphorylation of pRb (G1 arrest) via p53 as well as p16 signal transduction pathways. G1 phase arrest of the senescent cells was not overcome by nLDL removal from the culture medium. Moreover, the nLDL-treated cells produced reactive oxygen species (ROS) dose- and time-dependently. These results suggested, for the first time, that long-term treatment of nLDL could induce the premature senescence of endothelial cells.
Runtime Attestation for IAAS Clouds

We present the RIC (Runtime Attestation for Iaas Clouds) system which uses timing-based attestation to verify the integrity of a running Xen Hypervisor as well as the guest virtual machines running on top of it. As part of the RIC system we present a novel attestation technique which includes not only the guest operating system’s static code and read-only data sections but also the guest OS’ dynamically loadable kernel modules. These attestations are conducted periodically at run-time to provide a stronger guarantee of correctness than that offered by load-time verification techniques. A system such as RIC can be used in cloud computing scenarios to verify the environment in which the cloud services ultimately run. Furthermore we offer a method to decrease the performance impact that this process has on the virtual machines that run the cloud services since these services often have very strict performance and availability requirements. This scheme effectively extends the root of trust on the cloud machines from the Xen hypervisor upward to include the guest OS that runs within each virtual machine. This work represents an important step towards secure cloud computing platforms which can help cloud providers offer new services that require higher levels of security than are possible in cloud data centers today.