Miracle-dz/UniFAR-FTUs huggingface dataset:
Colored Packets with Deadlines and Metric Space Transition Cost

We consider scheduling of colored packets with transition costs which form a general metric space. We design a $1 - O(\sqrt{MST(G) / L})$ competitive algorithm. Our main result is a hardness result of $1 - \Omega(\sqrt{MST(G) / L})$ which matches the competitive ratio of the algorithm for each metric space separately. In particular, we improve the hardness result of Azar at el. 2009 for uniform metric spaces. We also extend our result to weighted directed graphs which obey the triangular inequality and show a $1 - O(\sqrt{TSP(G) / L})$ competitive algorithm and a nearly-matching hardness result. In proving our hardness results we use some interesting non-standard embedding.
A Global Shape Index to Characterize Anterior Lamina Cribrosa Morphology and Its Determinants in Healthy Indian Eyes.

PURPOSE Lamina cribrosa (LC) morphology could be implicated in the progression of glaucoma. To date, no established, quantifiable parameter to assess LC shape in vivo exists. We aim to introduce a new global shape index for the anterior LC (LC-GSI) and to identify associations with ocular factors in a healthy Indian population. METHODS Optical coherence tomography (OCT) scans of the optic nerve head (ONH) were performed on 162 healthy subjects. Optic nerve head structures were delineated and a geometric characterization of anterior LC morphology was obtained by measuring curvature along 180 LC cross sections and representing it as LC-GSI ranging from -1 to +1. Lamina cribrosa depth and curvature were also reported. Linear regression was used to identify factors associated with LC morphology. RESULTS The typical healthy LC had a saddle rut-like appearance, with a central ridge visible in superior-inferior cross sections. A more prominent central ridge (larger LC-GSI) was associated with shorter axial length (P < 0.001), smaller Bruch's membrane opening (BMO) area (P = 0.020), smaller vertical cup-to-disc ratio (VCDR) (P = 0.007), and larger minimum rim width (BMO-MRW) (P = 0.001). A deeper LC was associated with male sex (P < 0.001), shorter axial length (P = 0.003), larger VCDR (P < 0.001), and smaller BMO-MRW (P = 0.002). Age and IOP were not significantly associated with LC morphology in healthy eyes. CONCLUSIONS The LC-GSI is a single index that quantifies overall LC shape in an intuitive way. Ocular determinants of LC-GSI in healthy eyes included risk factors for glaucoma (axial length, VCDR, and BMO-MRW), highlighting the potential role of LC morphological characterization in the diagnosis and monitoring of glaucoma.
Efficient Method for Optimizing Artificial Neural Network Using "Quantum-Based Algorithm"

This paper presents competent Method for Optimizing Artificial Neural Network Using Quantum Based Algorithm. In the evolutionary process, the Quantum bits refined so that the probability of finding the optimal network is increased. The probability representation reduced the non positive impact of the permutation problem and the risk of the potential network. To finds near-optimal connection weights, the technique of subspace search using quantum bits is proposed. The algorithm thus performed a division-by-division exploration in the beginning and, as the candidate subspaces are identified, a randomized search in good subspaces is employed for exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The Propose model Improved QNN performance better than standard QNN based on some bench test function. Application examples on iris and breast cancer are shown the Improved QNN performance better than standard QNN based.
The over-sketching technique for free-hand shape modelling in Virtual Reality

The benefits of the Virtual Reality (VR) technologies in the field of conceptual free form modelling have been investigated for over a decade. Many researchers have paid attention to hand-sketching techniques for shape generation and to the Virtual Sculpting approach for surface modification. What has not yet been investigated sufficiently is modification based on the free-hand drawing approach. This could be more similar to the traditional way designers work, compared to the sculpting metaphor. The research described in this paper helps to fill this gap, defining a new free-form curve modification technique based on the over-sketching approach. This technique has been implemented in a curve modification tool of VEIS (Virtual Environment for Interactive Sketching), a prototype VR system for free form modelling. The tool allows the user to refine a curve by simply substituting a part of it with a new one. It recognizes the user gestures, made with a 3D tracked pen, to determine how the pen strokes modify a part of the existing curve and then automatically merges the new piece with the unchanged parts of curve. Furthermore, by taking advantage of the parent-child relation, a surface can be updated by simply modifying the curve/s employed for its definition.
Scattering by in nite one-dimensional roughsurfacesBy

We consider the Dirichlet boundary value problem for the Helmholtz equation in a non-locally perturbed half-plane. This problem models time-harmonic electromagnetic scattering by a one-dimensional, innnite rough, perfectly conducting surface; the same problem arises in acoustic scattering by a sound-soft surface. In Chandler-Wilde & Zhang (1998a) a radiation condition has been suggested for this problem, a generalisation of the Rayleigh expansion condition for diiraction gratings, and uniqueness of solution has been established. Recently, an integral equation formulation of the problem has also been proposed and, in the special case when the whole boundary is both Lyapunov and a small perturbation of a at boundary, the unique solvability of this integral equation has been shown in Chandler-Wilde & Ross (1996) by operator perturbation arguments. In this paper we study the general case, with no limit on surface amplitudes or slopes, and show that the same integral equation has exactly one solution in the space of bounded and continuous functions for all wavenumbers. As an important corollary we prove that, for a variety of incident elds including the incident plane wave, the Dirichlet boundary value problem for the scattered eld has a unique solution.
Structures of Microbial Communities in Alpine Soils: Seasonal and Elevational Effects

Microbial communities in alpine environments are exposed to several environmental factors related to elevation and local site conditions and to extreme seasonal variations. However, little is known on the combined impact of such factors on microbial community structure. We assessed the effects of seasonal variations on soil fungal and bacterial communities along an elevational gradient (from alpine meadows to a glacier forefield, 1930–2519 m a.s.l.) over 14 months. Samples were taken during all four seasons, even under the winter snowpack and at snowmelt. Microbial community structures and abundances were investigated using Terminal Restriction Fragment Length Polymorphism (T-RFLP) and quantitative PCR (qPCR) of the 16S and 18S rRNA genes. Illumina sequencing was performed to identify key bacterial groups in selected samples. We found that the soil properties varied significantly with the seasons and along the elevational gradient. For example, concentrations of soluble nutrients (e.g., NH4+-N, SO42−-S, PO43−-P) significantly increased in October but decreased drastically under the winter snowpack. At all times, the alpine meadows showed higher soluble nutrient concentrations than the glacier forefield. Microbial community structures at the different sites were strongly affected by seasonal variations. Under winter snowpack, bacterial communities were dominated by ubiquitous groups (i.e., beta-Proteobacteria, which made up to 25.7% of the total reads in the glacier forefield). In the snow-free seasons, other groups (i.e., Cyanobacteria) became more abundant (from 1% under winter snow in the glacier forefield samples to 8.1% in summer). In summary, elevation had a significant effect on soil properties, whereas season influenced soil properties as well as microbial community structure. Vegetation had a minor impact on microbial communities. At every elevation analyzed, bacterial, and fungal community structures exhibited a pronounced annual cycle.
Edge gradients provide evidence of ecological interactions in planktonic thin layers

Physical, optical, and acoustical data from Monterey Bay, California, USA, describing the distributions of phytoplankton, zooplankton, and small fish as well as gradients in the physical habitat were used to examine the interactions between vertically compressed plankton structures (thin layers), their consumers, and the local physical forces they experience. The relationship between upper and lower edge gradients of a layer were used to define layer ‘shape'. The steepness of the vertical gradient on the top vs. the bottom of the plankton layer was correlated to the difference in the relative abundance of consumers above and below the layer. Phytoplankton layer gradients were steeper when more zooplankton were present on one side of the layer vs. the other while zooplankton layers were more diffuse when a greater number of fish were adjacent to one side of the layer than the other. Both layer types showed nearly symmetrical gradients when predators were in low abundance or absent. Predator‐associated differences in phytoplankton and zooplankton layer shape were not correlated with vertical gradients in shear or mixing potential surrounding layers. In the absence of strong physical gradients, grazers can play an important role in structuring plankton thin layers. These observations likely represent one end of a continuum of biological and physical forcing responsible for formation and maintenance of thin plankton layers.
Bring on the weekend – Improving the quality of junior doctor weekend handover

Abstract While it is widely recognised that communication and handover are a fundamental component in providing safe clinical care for hospital patients (1,2.3). The Royal College of Physicians found that the majority of hospital doctors are dissatisfied with the standard of their handovers (4). These findings were mirrored by the junior staff at the Royal United Hospital, who felt that the weekend handover was inadequate, and detrimental to patient safety. A group of eight junior doctors at the Royal United Hospital, Bath utilised The Model For Improvement to systematically analyse and improve various aspects of the weekend handover system. Handover sheets from a subset of wards were assessed to observe direct effects of staged interventions over a nine month period, allowing small-scale testing prior to widespread implementation of a standardised intranet-based weekend handover. The effects of interventions were evaluated using a predesigned scoring system and data was collected continuously throughout the project. Over a nine month period the quality of handovers improved significantly from 76% to 93% (p <0.01): a success which was supported by a 100% improvement in formal feedback collected from hospital doctors and highlighted by the desire of senior staff and directors to implement the system throughout the trust. Using The Model For Improvement a group of junior doctors were able to introduce and develop a standardised weekend handover system that met their requirements. A structured, efficient and auditable system has been successfully produced which improves the quality and safety of patient care.
Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable ?

As most political scientists know, the outcome of the American presidential election can be predicted within a few percentage points (in the popular vote), based on information available months before the election. Thus, the general campaign for president seems irrelevant to the outcome (except in very close elections), despite all the media coverage of campaign strategy. However, it is also well known that the pre-election opinion polls can vary wildly over the campaign, and this variation is generally attributed to events in the campaign. How can campaign events affect people's opinions on whom they plan to vote for, and yet not affect the outcome of the election? For that matter, why do voters consistently increase their rupport for a candidate during his nominating convention, even though the conventions are almost entirely predictable events whose effects can be rationally forecast? In this exploratory study, we consider several intuitively appealing, but ultimately wrong. resolutions to this puzzle and discuss our current understanding of what causes opinion polls to fluctuate while reaching a predictable outcome. Our evidence is based on graphical presentation and analysis of over 67,000 individual-level responses from forty-nine commercial polls during the 1988 campaign and many other aggregate poll results from the 1952-92campaigns. We show that responses to pollsters during the campaign are not generally informed or even, in a sense we describe, 'rational'. In contrast, voters decide, based on their enlightened preferences, as formed by the information they have learned during the campaign, as wel! as basic political cues such as ideology and party identification, which candidate to support eventually. We cannot prove this conclusion, but we do show that it is consistent with the aggregate forecasts and individual-level opinion poll responses. Based on the enlightened preferences hypothesis, we conclude that the news media have an important effect on the outcome of presidential elections not through misleading advertisements. sound bites, or spin doctors, but rather by conveying candidates' positions on important issues.
Geometric inversion approcah for visual curve estimattion

The trade-off between bias and variance is a key issue for statistical learning and estimation. Robust algorithm could be achieved by increasing the bias, such as the circle estimation problem in our paper. Estimating circles under finite sampling data points is an important task in many applications in computer vision area. In this paper, we provide a novel regression method based on the inversion transform. A circle can be translated into a line by the inversion transform, where the inversion centre is a point belonging to the sampling data set. After that, current analysis tools for fitting line can be directly used to the task of fitting the circle. Both experimental results and theoretical analysis show that our method could achieve better performance compared with the Hough transform.
Annai/PMA instrumentation intrusion presentation

Performance monitoring in the Annai tool environment for distributed-memory parallel computing systems is achieved through a flexible combination of different types of instrumentation. Latent instrumentation in the communication library and inserted in executables by the compilation system can be dynamically configured during program execution. Analysis of the subject program and knowledge of the associated intrusion and costs allows Annai to guide the user in selecting appropriate instrumentation. Instrumentation processing and transport costs are also explicitly available as part of Annai/PMA performance analysis and visualizations, such that they can be clearly accounted for and their potential impact on program execution considered. While it is not possible to completely eliminate or compensate for instrumentation intrusion, Annai provides an integrated environment where intrusion is explicitly recognized and can be minimized as part of detailed parallel program performance analysis.
Hands, face and joints for multi-modal human-action temporal segmentation and recognition

We present in this paper a new approach for human-action extraction and recognition in a multi-modal context. Our solution contains two modules. The first one applies temporal action segmentation by combining a heuristic analysis with augmented-joint description and SVM classification. The second one aims for a frame-wise action recognition using skeletal, RGB and depth modalities coupled with a label-grouping strategy in the decision level. Our contribution consists of (1) a selective concatenation of features extracted from the different modalities, (2) the introduction of features relative to the face region in addition to the hands, and (3) the applied multilevel frames-grouping strategy. Our experiments carried on the Chalearn gesture challenge 2014 dataset have proved the effectiveness of our approach within the literature.
IPAS: Intelligent protection against silent output corruption in scientific applications

This paper presents IPAS, an instruction duplication technique that protects scientific applications from silent data corruption (SDC) in their output. The motivation for IPAS is that, due to natural error masking, only a subset of SDC errors actually affects the output of scientific codes - we call these errors silent output corruption (SOC) errors. Thus applications require duplication only on code that, when affected by a fault, yields SOC. We use machine learning to learn code instructions that must be protected to avoid SOC, and, using a compiler, we protect only those vulnerable instructions by duplication, thus significantly reducing the overhead that is introduced by instruction duplication. In our experiments with five workloads, IPAS reduces the percentage of SOC by up to 90% with a slowdown that ranges between 1.04x and 1.35x, which corresponds to as much as 47% less slowdown than state-of-the-art instruction duplication techniques.
A hybrid prediction model with F-score feature selection for type II Diabetes databases

The medical data are multidimensional, and are represented by a large number of features. Hundreds of independent features (parameters) in these high dimensional databases need to be simultaneously considered and analyzed, for valuable decision-making information in medical prediction. Most data mining methods depend on a set of features that define the behavior of the learning algorithm and directly or indirectly influence the complexity of the resulting models. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. The aim of this study is to improve the diagnostic accuracy of diabetes disease by selecting informative features of Pima Indians Diabetes Dataset. This study proposes a Hybrid Prediction Model with F-score feature selection approach to identify the optimal feature subset of the Pima Indians Diabetes dataset. The features of diabetes dataset are ranked using F-score and the feature subset that gives the minimal clustering error is the optimal feature subset of the dataset. The correctly classified instances determine the pattern for diagnosis and are used for further classification process. The improved performance of the Support Vector Machine classifier measured in terms of Accuracy of the classifier, Sensitivity, Specificity and Area Under Curve (AUC) proves that the proposed feature approach indeed improves the performance of classification. The proposed prediction model achieves a predictive accuracy of 98.9427 and it is the highest predictive accuracy for diabetes dataset compared to other models in literature for this problem.
Role of Regulatory Cells in Oral Tolerance

The immune system is continuously exposed to great amounts of different antigens from both food and intestinal microbes. Immune tolerance to these antigens is very important for intestinal and systemic immune homeostasis. Oral tolerance is a specific type of peripheral tolerance induced by exposure to antigen via the oral route. Investigations on the role of intestinal immune system in preventing hypersensitivity reactions to innocuous dietary and microbial antigens have been intensively performed during the last 2 decades. In this review article, we discuss how food allergens are recognized by the intestinal immune system and draw attention to the role of regulatory T (Treg) and B (Breg) cells in the establishment of oral tolerance and tolerogenic features of intestinal dendritic cells. We also emphasize the potential role of tonsils in oral tolerance induction because of their anatomical location, cellular composition, and possible usage to develop novel ways of specific immunotherapy for the treatment of allergic diseases.
FACE RECOGNITION USING LOCAL QUANTIZED PATTERNS AND GABOR FILTERS

The problem of face recognition in a natural or artificial environment has received a great deal of researchers’ attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.
Improved adaptive color embedding and recovery using DC level shifting

Several color-to-gray image mapping (CGIM) methods were proposed so far to restore a color image from its corresponding printed grayscale image. They are based on embedding chrominance values into high-frequency subbands of the luminance signal. In the conventional methods, textures often become noticeable in the chrominance-embedded gray image since subsampled chrominance values are embedded directly. As a result, qualities of the chrominance-embedded grayscale images are degraded especially for images with rich textures. In this paper, we propose an improved CGIM method using DC level shifting and directional transform. In the experimental results, our method greatly improves the qualities of both the chrominance-embedded grayscale and the restored color images in comparison with conventional methods.
The Problem of Meaning in AI and Robotics: Still with Us after All These Years

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.