994 resultados para Fundamental Classification


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Gender profiling is a fundamental task that helps CCTV systems to
provide better service for intelligent surveillance. Since subjects being detected
by CCTVs are not always cooperative, a few profiling algorithms are proposed
to deal with situations when faces of subjects are not available, among which
the most common approach is to analyze subjects’ body shape information. In
addition, there are some drawbacks for normal profiling algorithms considered
in real applications. First, the profiling result is always uncertain. Second, for a
time-lasting gender profiling algorithm, the result is not stable. The degree of
certainty usually varies, sometimes even to the extent that a male is classified
as a female, and vice versa. These facets are studied in a recent paper [16] using
Dempster-Shafer theory. In particular, Denoeux’s cautious rule is applied for
fusion mass functions through time lines. However, this paper points out that if
severe mis-classification is happened at the beginning of the time line, the result
of applying Denoeux’s rule could be disastrous. To remedy this weakness,
in this paper, we propose two generalizations to the DS approach proposed in
[16] that incorporates time-window and time-attenuation, respectively, in applying
Denoeux’s rule along with time lines, for which the DS approach is a special
case. Experiments show that these two generalizations do provide better results
than their predecessor when mis-classifications happen.

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This paper, which was published as a chapter of a Festskrift for Professor Ruth Nielsen, analyses Article 23 CFREU, the new provision on gender equality. It argues that Article 23 adds to the notion of gender equality in EU law, and not only allows, but also demands positive action measures if necessary to ensure equality between women and men. The provision also demands that positive action measures are suitable to achieve their aim. This implies that the EU legislator has to adapt positive action measure to the specific needs of the sector. The paper offers a critique of the proposal to introduce women quotas in board rooms, as proposed by the EU Commission in late 2012. It argues that the Commission unimaginatively copied rules developed for the German public service into a different sector, although these rules have not proven particularly efficient even in the public service. Consequently, a proposal that is demanding, but adapted to the sector should be developed.

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The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

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Breast cancer remains a frequent cause of female cancer death despite the great strides in elucidation of biological subtypes and their reported clinical and prognostic significance. We have defined a general cohort of breast cancers in terms of putative actionable targets, involving growth and proliferative factors, the cell cycle, and apoptotic pathways, both as single biomarkers across a general cohort and within intrinsic molecular subtypes.

We identified 293 patients treated with adjuvant chemotherapy. Additional hormonal therapy and trastuzumab was administered depending on hormonal and HER2 status respectively. We performed immunohistochemistry for ER, PR, HER2, MM1, CK5/6, p53, TOP2A, EGFR, IGF1R, PTEN, p-mTOR and e-cadherin. The cohort was classified into luminal (62%) and non-luminal (38%) tumors as well as luminal A (27%), luminal B HER2 negative (22%) and positive (12%), HER2 enriched (14%) and triple negative (25%). Patients with luminal tumors and co-overexpression of TOP2A or IGF1R loss displayed worse overall survival (p=0.0251 and p=0.0008 respectively). Non-luminal tumors had much greater heterogeneous expression profiles with no individual markers of prognostic significance. Non-luminal tumors were characterised by EGFR and TOP2A overexpression, IGF1R, PTEN and p-mTOR negativity and extreme p53 expression.

Our results indicate that only a minority of intrinsic subtype tumors purely express single novel actionable targets. This lack of pure biomarker expression is particular prevalent in the triple negative subgroup and may allude to the mechanism of targeted therapy inaction and myriad disappointing trial results. Utilising a combinatorial biomarker approach may enhance studies of targeted therapies providing additional information during design and patient selection while also helping decipher negative trial results.

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Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

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Flow processing is a fundamental element of stateful traffic classification and it has been recognized as an essential factor for delivering today’s application-aware network operations and security services. The basic function within a flow processing engine is to search and maintain a flow table, create new flow entries if no entry matches and associate each entry with flow states and actions for future queries. Network state information on a per-flow basis must be managed in an efficient way to enable Ethernet frame transmissions at 40 Gbit/s (Gbps) and 100 Gbps in the near future. This paper presents a hardware solution of flow state management for implementing large-scale flow tables on popular computer memories using DDR3 SDRAMs. Working with a dedicated flow lookup table at over 90 million lookups per second, the proposed system is able to manage 512-bit state information at run time.

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The Magellanic Clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused by dust. We have searched the Spitzer Space Telescope data archive for all Infrared Spectrograph (IRS) staring-mode observations of the Small Magellanic Cloud (SMC) and found that 209 Infrared Array Camera (IRAC) point sources within the footprint of the Surveying the Agents of Galaxy Evolution in the Small Magellanic Cloud (SAGE-SMC) Spitzer Legacy programme were targeted, within a total of 311 staring-mode observations. We classify these point sources using a decision tree method of object classification, based on infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information. We find 58 asymptotic giant branch (AGB) stars, 51 young stellar objects, 4 post-AGB objects, 22 red supergiants, 27 stars (of which 23 are dusty OB stars), 24 planetary nebulae (PNe), 10 Wolf-Rayet stars, 3 H II regions, 3 R Coronae Borealis stars, 1 Blue Supergiant and 6 other objects, including 2 foreground AGB stars. We use these classifications to evaluate the success of photometric classification methods reported in the literature.

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CCTV (Closed-Circuit TeleVision) systems are broadly deployed in the present world. To ensure in-time reaction for intelligent surveillance, it is a fundamental task for real-world applications to determine the gender of people of interest. However, normal video algorithms for gender profiling (usually face profiling) have three drawbacks. First, the profiling result is always uncertain. Second, the profiling result is not stable. The degree of certainty usually varies over time, sometimes even to the extent that a male is classified as a female, and vice versa. Third, for a robust profiling result in cases that a person’s face is not visible, other features, such as body shape, are required. These algorithms may provide different recognition results - at the very least, they will provide different degrees of certainties. To overcome these problems, in this paper, we introduce an Dempster-Shafer (DS) evidential approach that makes use of profiling results from multiple algorithms over a period of time, in particular, Denoeux’s cautious rule is applied for fusing mass functions through time lines. Experiments show that this approach does provide better results than single profiling results and classic fusion results. Furthermore, it is found that if severe mis-classification has occurred at the beginning of the time line, the combination can yield undesirable results. To remedy this weakness, we further propose three extensions to the evidential approach proposed above incorporating notions of time-window, time-attenuation, and time-discounting, respectively. These extensions also applies Denoeux’s rule along with time lines and take the DS approach as a special case. Experiments show that these three extensions do provide better results than their predecessor when mis-classifications occur.

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Inbreeding depression is most pronounced for traits closely associated with fitness. The traditional explanation is that natural selection eliminates deleterious mutations with additive or dominant effects more effectively than recessive mutations, leading to directional dominance for traits subject to strong directional selection. Here we report the unexpected finding that, in the butterfly Bicyclus anynana, male sterility contributes disproportionately to inbreeding depression for fitness (complete sterility in about half the sons from brother-sister matings), while female fertility is insensitive to inbreeding. The contrast between the sexes for functionally equivalent traits is inconsistent with standard selection arguments, and suggests that trait-specific developmental properties and cryptic selection play crucial roles in shaping genetic architecture. There is evidence that spermatogenesis is less developmentally stable than oogenesis, though the unusually high male fertility load in B. anynana additionally suggests the operation of complex selection maintaining male sterility recessives. Analysis of the precise causes of inbreeding depression will be needed to generate a model that reliably explains variation in directional dominance and reconciles the gap between observed and expected genetic loads carried by populations. This challenging evolutionary puzzle should stimulate work on the occurrence and causes of sex differences in fertility load.