Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Risk under Interval Uncertainty: Sequential and Parallel Algorithms

In traditional econometrics, the quality of an individual investment-and of the investment portfolio-is characterized by its expected return and its risk (variance). For an individual investment or portfolio, we can estimate the future expected return and a future risk by tracing the returns x1,…, x(subscript n) of this investment (and/or similar investments) over the past years, and computing the statistical characteristics based on these returns. The return (per unit investment) is defined as the selling price of the corresponding financial instrument at the end of, e. g. , a one-year period, divided by the buying price of this instrument at the beginning of this period. It is usually assumed that we know the exact return values x1,…, x(subscript n). In practice, however, both the selling and the buying prices unpredictably fluctuate from day to day-and even within a single day. These minute-by-minute fluctuations are rarely recorded; what we usually have recorded is the daily range of prices. As a result, we can only find the range [x(subscript i)); (average)x(subscript i))] of possible values of the return xi. In this case, different possible values of xi lead, in general, to different values of the expected return E and of the risk V. In such situations, we are interested in producing the intervals of possible values of E and V. In the paper, we describe algorithms for producing such interval estimates. The corresponding sequential algorithms, however, are reasonably complex and time-consuming. In financial applications, it is often very important to produce the result as fast as possible. One way to speed up computations is to perform these algorithms in parallel on several processors, and thus, to speed up computations. In this paper, we show how the algorithms for estimating variance under interval uncertainty can be parallelized.
Lightweight authentication protocol used in Wireless Sensor Network

Indoor positioning system is implemented using sensors that use Wi-Fi triangulation to compute the position of a mobile device in a given area. This group of sensors placed in an unattended environment form a Wireless Sensor Network (WSN). These nodes in WSN are more vulnerable to passive and active attacks than wired sensors due to limitations in resources, unrestrained environments and their broadcasting nature. Security will be a significant factor in such applications where data integrity is most important. In this paper, a new and secure protocol based on number theory concepts and congruence equations has been introduced to provide authentication between the sensor nodes and Database Server in a Wireless Network. The proposed protocol uses Fermat Number Transform (FNT) and Chinese Remainder Theorem (CRT) to enable secure communication. The protocol will be using its own encryption and decryption algorithm to reduce the computational complexity involved in existing algorithms. It results in minimum memory utilization, instant authentication and it withstands Cloning attack, Replay attack, DoS attack and Man-in-the-middle attack. Comparison of the existing protocol with the proposed protocol is done on the basis of prevention of various attacks and memory efficiency.
Robust Trust Mechanisms for Monitoring Aggregator Nodes in Sensor Networks

Sensor nodes are often deployed in large numbers to monitor extended sensor fields. In such scenarios data aggregation plays a crucial role on summarizing data forwarded to base stations in sensor networks. As sensor networks are commonly deployed in open and unattended areas, they are vulnerable to physical tampering as well as remote attacks. Existing security techniques focus primarily on methods used by an aggregator node to monitor and detect compromised behavior by nodes that report data to it. We propose a novel mechanism, combining techniques from statistics and artificial intelligence, by which nodes reporting to an aggregator node can monitor whether the latter is reporting incorrect aggregated values. In our framework, nodes are arranged in a hierarchy. We develop a reputation management scheme in which each child node keeps a reputation value of its parent. For each data reporting event, a node is privy only to the data value it reported and the aggregated value forwarded by its parent node. Each node then calculates the probability of the parent reporting correctly aggregated values over an epoch of events by adapting a statistical hypothesis testing scheme. This probability is used to incrementally update the trustworthiness of the parent node using a learning scheme. When the trustworthiness of a node falls below a threshold it can no longer be trusted with the aggregation task and should be reallocated or eliminated from the network. We evaluate the robustness of our adaptation of a couple of statistical hypothesis testing schemes and analyze their applicability for different types of malicious behaviors by compromised sensor nodes.
Eligibility traces through colored noises

The Gaussian Process Temporal Differences (GPTD) framework initiated statistical modeling of value function approximation. It was followed by the close Kalman Temporal Differences (KTD) approach. Both methods share the same drawback: they provide biased estimates of the value function when transitions of the system to be controlled are stochastic. A colored noise model has been introduced to cope with this problem in the GPTD framework, which actually leads to a Monte-Carlo estimate of the value function. In this paper, we generalize this colored noise model using ideas close to eligibility traces and apply it to the KTD framework. This allows removing the bias when the so-called eligibility factor is set to one, and decreasing it when this factor is strictly between zero and one. The proposed algorithm is experimented on the simple Boyan chain in order to study the effect of the eligibility factor. As KTD generalizes GPTD in the sense that it allows taking into account nonlinear parameterizations, we also propose an experiment combining the new algorithm with a neural network.
“Food Supply chain Management sustainability: A Review”

The National Food Security Bill 2013 which was passed by way of an Ordinance in the parliament is no small feat undertaken by the Government of India. This bill promises subsidized food grains to nearly 68% of India’s 1.2 Billion populations at a cost of nearly USD 4 Billion. The bill aims to provide quality food grains and nutritional security, to under privileged children, women and poorer sections of the society, at affordable prices. Indeed a very noble and ambitious plan but lots of supply chain issues will have to be addressed on a war footing by the government. The need to improve food quality and reduce food waste leads to increased attention for the development of Food Supply Chain Management, which considers natural characteristics of food supply chains besides traditional supply chain management objectives such as cost and responsiveness. Growing consciousness of society towards sustainable development brings additional objectives to the food supply chains and the fast evolution of sustainable development results in the development of a new fast-growing concept: Sustainability in Food Supply Chain Management. In response to these developments, researchers have developed various decision support tools that can be used for handling progressively increasing complexity along the alteration process from supply chain management to food supply chain management and now to sustainable food supply chain management. This study, reviewed literature on food supply chain management to identify supply chain key issues and relevant quantitative modelling challenges. The reviewed papers show that obtaining sustainability together with considering natural characteristics of food supply chains, there is a gap between the needs of the practice and the current available methods. This study also presented improvement opportunities for future research by revealing the above missing points.
Gesture Control for Use in Automobiles

Gesture interfaces are gaining relevance for human-machine communication, since it is expected that they make interaction more intuitive. Particularly vision based approaches are widely preferred. This paper describes a novel vision based real-time gesture recognition system, designed for operating in an automotive environment. It is used within an application for retrieving traffic news and e-mails from a message storage. Image processing and pattern matching techniques, specially adapted to the complex environmental conditions, represent the systems basics. more comfortable natural input. Hence we prototypically developed a vision based real-time recognition system, to operate a message storage by gestures in an automotive environment. The demonstrator exists as a laboratory setup and as a mobile version in an experimental vehicle. The next section describes the technical requirements and some preliminary considerations. Afterwards, the image acquisition and processing schemes are presented. Then the functionality of the chosen application is explained, before the last section gives a final discussion and outlook.
Measures of parallelism at compile time

We analyze the characteristics of the innermost loops in 9 programs of the Perfect benchmark suite, 60 Linpack subroutines and a subset of the BLAS subroutines. We have generated their dependence graphs and have evaluated at compile time the inherent parallelism of these graphs. The evaluation of the usefulness of some parallelizing techniques has been predicted by modeling the technique over the dependence graph and evaluating the parallelism after the transformation. <<ETX>>
FOR OPTIMIZING SIMULATED ANNEALING ARE EFFECTIVE

The method of simulated annealing was used to get a heuristic solution for the minimum length word equivalent to a given word in the braid groups (a known NP-complete problem). The simulated annealing paradigm with a simple cooling schedule leaves five parameters up to the user to choose that were chosen empirically based on performance experiments as is the usual practise. After this, a downhill simplex method was developed to further optimize these critical parameters and a quality improvement of up to 26.1% was observed. This additional improvement made the algorithm competitive, on average, with custom designed heuristics for this problem. The conclusions going beyond the present combinatorial problem are: (1) Fine-tuning of cooling schedule parameters is critical for the solution quality in simulated annealing, (2) downhill simplex methods (as opposed to Newton’s method, for example) are well-suited for this task and (3) significant quality improvement is possible even for a simple cooling schedule.
A Double TRPtych: Six Views of Transient Receptor Potential Channels in Disease and Health

At the 2008 Annual Meeting of the Society for Neuroscience, a Mini-Symposium entitled “Contributions of TRP Channels to Neurological Disease” included talks from six heads of newly established laboratories, each with a unique research focus, model system, and set of experimental tools. Some of the questions addressed in these talks include the following. What is the role of transient receptor potential (TRP) channels in pain perception? How do normally functioning TRP channels contribute to cell death pathways? What are the characteristics of TRPpathies, disease states that result from overactive or underactive TRP channels? How are TRP channels regulated by signal transduction cascades? This review summarizes recent results from those laboratories and provides six perspectives on the subject of TRP channels and disease.
Taurine Induces Proliferation of Neural Stem Cells and Synapse Development in the Developing Mouse Brain

Taurine is a sulfur-containing amino acid present in high concentrations in mammalian tissues. It has been implicated in several processes involving brain development and neurotransmission. However, the role of taurine in hippocampal neurogenesis during brain development is still unknown. Here we show that taurine regulates neural progenitor cell (NPC) proliferation in the dentate gyrus of the developing brain as well as in cultured early postnatal (P5) hippocampal progenitor cells and hippocampal slices derived from P5 mice brains. Taurine increased cell proliferation without having a significant effect on neural differentiation both in cultured P5 NPCs as well as cultured hippocampal slices and in vivo. Expression level analysis of synaptic proteins revealed that taurine increases the expression of Synapsin 1 and PSD 95. We also found that taurine stimulates the phosphorylation of ERK1/2 indicating a possible role of the ERK pathway in mediating the changes that we observed, especially in proliferation. Taken together, our results demonstrate a role for taurine in neural stem/progenitor cell proliferation in developing brain and suggest the involvement of the ERK1/2 pathways in mediating these actions. Our study also shows that taurine influences the levels of proteins associated with synapse development. This is the first evidence showing the effect of taurine on early postnatal neuronal development using a combination of in vitro, ex-vivo and in vivo systems.
Abdominal morphometric data acquisition using depth sensors

The treatment of eating disorders is now part of the priority actions of the public health policy. For several years, nutritionists have been using tools relying on new digital technologies, able to provide new diagnostic elements. In this paper, we propose a complete methodology to acquire, analyse morphological data and establish typologies with low cost consumer electronics devices. We use a Microsoft® Kinect™ like peripheral to capture abdominal measurements. For each individual, we calculate two profiles in the sagittal and transverse planes respectively. The extracted quantitative information is then analyzed to build typologies of abdominal morphology.
ISCR Elements: Novel Gene-Capturing Systems of the 21st Century?

SUMMARY “Common regions” (CRs), such as Orf513, are being increasingly linked to mega-antibiotic-resistant regions. While their overall nucleotide sequences show little identity to other mobile elements, amino acid alignments indicate that they possess the key motifs of IS91-like elements, which have been linked to the mobility ent plasmids in pathogenic Escherichia coli. Further inspection reveals that they possess an IS91-like origin of replication and termination sites (terIS), and therefore CRs probably transpose via a rolling-circle replication mechanism. Accordingly, in this review we have renamed CRs as ISCRs to give a more accurate reflection of their functional properties. The genetic context surrounding ISCRs indicates that they can procure 5′ sequences via misreading of the cognate terIS, i.e., “unchecked transposition. ” Clinically, the most worrying aspect of ISCRs is that they are increasingly being linked with more potent examples of resistance, i.e., metallo-β-lactamases in Pseudomonas aeruginosa and co-trimoxazole resistance in Stenotrophomonas maltophilia. Furthermore, if ISCR elements do move via “unchecked RC transposition,” as has been speculated for ISCR1, then this mechanism provides antibiotic resistance genes with a highly mobile genetic vehicle that could greatly exceed the effects of previously reported mobile genetic mechanisms. It has been hypothesized that bacteria will surprise us by extending their “genetic construction kit” to procure and evince additional DNA and, therefore, antibiotic resistance genes. It appears that ISCR elements have now firmly established themselves within that regimen.
Time optimal path following with bounded velocities and accelerations for mobile robots with independently steerable wheels

Mobile robots with independently steerable wheels provide better robustness and efficiency compared to the other types of omnidirectional mobile robots. However, the non-holonomic constraints and singular configurations give rise to several challenging issues in exploiting the high maneuverability features of the robot. Many proposed motion controllers for such robots force the robot to stay outside of bulky regions around its singular points, which in turn limits the robot's dexterity. In this paper, which extends our previous works, we present an online trajectory generation along with a globally stable path following controller that enables the robot to follow any given smooth path and heading function. We show that the control signals extensively simplify the kinematic constraints and are utilized to develop an efficient online “Phase Plane” switching algorithm that bounds the velocities and accelerations of the actuators. Moreover, we show that the algorithm efficiently regulates the velocity of the robot around the singular configurations which allows the robot to realize wide ranges of complex maneuvers. The proposed control algorithm has been tested on iMoro(our four-wheeled independently steerable mobile manipulator), and the presented results show the efficacy of our method.
5'-CpG island promoter hypermethylation of the CAV-1 gene in breast cancer patients of Kashmir.

BACKGROUND Caveolin-1 (CAV-1), encoding the structural component of cellular caveolae, is a suggested tumor suppressor gene involved in cell signalling. Aberrant promoter methylation of CAV-1 is associated with inactivation of expression. We previously observed CAV-1 mutations in breast cancers and therefore devised this study to examine the hypermethylation status of the promoter region of CAV-1 with reference to breast cancer progression and development. METHODS Hypermethylation status of CAV-1 was analyzed by methylation specific PCR. Loss of expression of the CAV-1 gene was further evaluated by semi-quantitative rt-PCR. RESULTS 28/130 (21.5%) breast cancer cases showed promoter hypermethylation with reduced CAV-1 expression levels when compared with adjacent normal breast tissue. CAV-1 gene hypermethylation was significantly related to menopausal status, histopathological grade and age. CONCLUSION The rationale of our study is that CAV-1 gene is transcriptionally repressed in breast cancer cells due to hypermethylation. Our results reveal that promoter hypermethylation and loss of expression of the CAV-1 gene is an important alternative mechanism for inactivation of CAV-1 leading to complete gene silencing.
Fast construction of the HYB index

As shown in a series of recent works, the HYB index is an alternative to the inverted index (INV) that enables very fast prefix searches, which in turn is the basis for fast processing of many other types of advanced queries, including autocompletion, faceted search, error-tolerant search, database-style select and join, and semantic search. In this work we show that HYB can be constructed at least as fast as INV, and often up to twice as fast. This is because HYB, by its nature, requires only a half-inversion of the data and allows an efficient in-place instead of the traditional merge-based index construction. We also pay particular attention to the cache efficiency of the in-memory posting accumulation, an issue that has not been addressed in previous work, and show that our simple multilevel posting accumulation scheme yields much fewer cache misses compared to related approaches. Finally, we show that HYB supports fast dynamic index updates more easily than INV.
Modeling the collagen fibril network of biological tissues as a nonlinearly elastic material using a continuous volume fraction distribution function

Despite distinct mechanical functions, biological soft tissues have a common microstructure in which a ground matrix is reinforced by a collagen fibril network. The microstructural properties of the collagen network contribute to continuum mechanical tissue properties that are strongly anisotropic with tensile-compressive asymmetry. In this study, a novel approach based on a continuous distribution of collagen fibril volume fractions is developed to model fibril reinforced soft tissues as nonlinearly elastic and anisotropic material. Compared with other approaches that use a normalized number of fibrils for the definition of the distribution function, this representation is based on a distribution parameter (i.e. volume fraction) that is commonly measured experimentally while also incorporating pre-stress of the collagen fibril network in a tissue natural configuration. After motivating the form of the collagen strain energy function, examples are provided for two volume fraction distribution functions. Consequently, collagen second-Piola Kirchhoff stress and elasticity tensors are derived, first in general form and then specifically for a model that may be used for immature bovine articular cartilage. It is shown that the proposed strain energy is a convex function of the deformation gradient tensor and, thus, is suitable for the formation of a polyconvex tissue strain energy function.
Error Projection and Uncertainty in the Evaluation of Aggregate Error

At the end of the audit process, auditors evaluate the risk that aggregate financial statement error exceeds materiality. This evaluation is complex in that it requires a consideration of known error, projected error and sampling risk related to various segments of the audit. If the risk of material aggregate error is unacceptably high, the auditor can require the client to make adjustments for known and/or projected errors to reduce audit risk to an acceptable level. Results of an experiment indicate that auditors tend to underestimate the effect of both projected error and uncertainty when evaluating aggregate error. Auditors were provided information about known errors in four accounts and the results of sampling applied to two additional accounts for a hypothetical audit case. They were then asked to indicate the adjustments to the financial statements which would be required before issuing a "clean" audit opinion. Comparisons of auditors' required adjustments with and without explicit error projections and error bounds suggest that auditors do not adequately consider projected error and sampling uncertainty in the absence of explicit projections and error bounds. Further, even after explicit error projections and error bounds were provided, many auditors failed to require adjustments which would have reduced audit risk to acceptable levels.
Hierarchical application of model-predictive control for efficient integration of active buildings into low voltage grids

Active buildings are important contributors in urban power grids and they are expected to be more integrated with renewable energy resources. At the same time, the amount of additional renewable sources that can be integrated in existing urban distribution grids is restricted by technical limitations. This work introduces a novel hierarchical predictive model controller architecture that takes both the power grid and the building domains into account. In order to maximize the hosting capacity of existing grids, considering the role of the buildings' energy performance in the active distribution grid is essential. Minimal grid congestion and maximum self-coverage of building energy demand are two goals that potentially can be traded off efficiently by this approach.