Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Load-Bearing Characteristics of a New Pile Group Foundation for an Offshore Wind Turbine

Because offshore wind turbines are high-rise structures, they transfer large horizontal loads and moments to their foundations. One of the keys to designing a foundation is determining the sensitivities and laws affecting its load-bearing capacity. In this study, this procedure was carried out for a new high-rise cap pile group foundation adapted to the loading characteristics of offshore wind turbines. The sensitivities of influential factors affecting the bearing properties were determined using an orthogonal test. Through a combination of numerical simulations and model tests, the effects of the inclination angle, length, diameter, and number of side piles on the vertical bearing capacity, horizontal bearing capacity, and bending bearing capacity were determined. The results indicate that an increase in the inclination angle of the side piles will increase the vertical bearing capacity, horizontal bearing capacity, and bending bearing capacity. An increase in the length of the side piles will increase the vertical bearing capacity and bending bearing capacity. When the length of the side piles is close to the central pile, the increase is more apparent. Finally, increasing the number of piles will increase the horizontal bearing capacity; however, the growth rate is small because of the pile group effect.
Research on the construction of innovative province, cities and reginal innovations system-a case of Heilongjiang Province

Regional innovation system is the key to maintain competitive advantage and the basis of national innovation system. Especially in the transition of economic conditions, regional innovation environment, innovation subject, innovation input, innovation system all need to be improved to adapt to the demands of the new situation. The literature, quantitative and comparative analysis methods are applied in this paper to discuss the innovation evaluation system of innovation provinces and cities deeply, and evaluate the innovation ability of Heilongjiang Province. The conclusion can be drawn that: innovation policy and environment are very important, and the consciousness and behavior of innovation subject is the key factor, the provincial capital cities should play a leading role to increase the investment of innovation in order to promote the construction and development of the regional innovation system.
A two-fluid model for avalanche and debris flows

Geophysical mass flows—debris flows, avalanches, landslides—can contain O(106–1010) m3 or more of material, often a mixture of soil and rocks with a significant quantity of interstitial fluid. These flows can be tens of meters in depth and hundreds of meters in length. The range of scales and the rheology of this mixture presents significant modelling and computational challenges. This paper describes a depth-averaged ‘thin layer’ model of geophysical mass flows containing a mixture of solid material and fluid. The model is derived from a ‘two-phase’ or ‘two-fluid’ system of equations commonly used in engineering research. Phenomenological modelling and depth averaging combine to yield a tractable set of equations, a hyperbolic system that describes the motion of the two constituent phases. If the fluid inertia is small, a reduced model system that is easier to solve may be derived.
Trace of evolutionary evidence by analyzing clustering information of metabolic pathways in eukaryotes

Alternative pathways in genetic and molecular pathways are suggesting evidence of evolution. By clustering and analyzing the metabolic pathways of 61 species in 5 classes within eukaryotes based on its topology, we obtained an interesting result that implies this evolutionary evidence. After grouping reactions along their connectivity, the results showed that compounds in higher organisms were less clustered into loosely connected groups. This implies that higher organisms have evolved in alternative pathways, giving traces of evolution.
Collaborative knowledge as a service applied to the disaster management domain

Cloud computing offers services which promise to meet continuously increasing computing demands by using a large number of networked resources. However, data heterogeneity remains a major hurdle for data interoperability and data integration. In this context, a knowledge as a service (KaaS) approach has been proposed with the aim of generating knowledge from heterogeneous data and making it available as a service. In this paper, a collaborative knowledge as a service (CKaaS) architecture is proposed, with the objective of satisfying consumer knowledge needs by integrating disparate cloud knowledge through collaboration among distributed KaaS entities. The NIST cloud computing reference architecture is extended by adding a KaaS layer that integrates diverse sources of data stored in a cloud environment. CKaaS implementation is domain-specific; therefore, this paper presents its application to the disaster management domain. A use case demonstrates collaboration of knowledge providers and shows how CKaaS operates with simulation models.
Adaptive Multicast For Dealy Tolerant Network using AOMDV protocol

Many Delay tolerant Network(DTN) application multicast services thus making efficient multicast communication support. In this paper we propose a adaptive multicast in delay tolerqant network to challange the intermittent link connectivity and frequently partiontioned in DTN.In this paper our main aim is to minimize the packet drop problem and we analyze this problem by using NS-2.in this we especially focus on packet drop problem, for minimizing this we use low duty cycle, setting packets are addressed to set of potential receiver and forward by the neighbour that wakes up first and it recieve packet successfully and reduce delay and energy consumption by utilizing the potential of all neighbour.
A missense mutation in Caenorhabditis elegans prohibitin 2 confers an atypical multidrug resistance

Hemiasterlin is a potent antimitotic peptide that interferes with microtubule dynamics at picomolar concentrations in cell culture. The molecule largely eludes P glycoprotein-mediated drug efflux, and an analog is currently being evaluated in clinical trials as cancer chemotherapy. From a nonclonal genetic screen in Caenorhabditis elegans we isolated eight independent mutants resistant to a synthetic hemiasterlin analog. In one recessive mutant, phb-2(ad2154), a point mutation in prohibitin 2 (E130K) protects worms from drug-induced injury. Data indicate that direct binding of hemiasterlin to prohibitin 2 is unlikely. In fact, C. elegans phb-2(ad2154) was also found to be resistant to numerous other drugs that bind tubulin and to camptothecin, yet this mutant was sensitive to nocodazole and phalloidin. Thus, prohibitin 2 is implicated in a previously uncharacterized pathway of multidrug resistance.
Post-Oppositional Pedagogies

Post-oppositional pedagogies developed by feminist, womanist, and social-justice theorists get beyond the limits and binaries of oppositional pedagogies to transform either/or thinking into the acceptance of multiplicity, contradiction, and paradox. Oppositional pedagogical frameworks employ dichotomous either/or epistemologies and generate us-against-them dynamics that pit one person, one group, or one “Truth” against another, and invite antagonism. Post-oppositional frameworks include invitational pedagogies, which introduce students to a wide range of additional perspectives and invite students to self-reflect on their existing beliefs and other dimensions of their worldviews, to examine them from additional points of view.
Automatic Tracking of Indoor Soccer Players Using Videos from Multiple Cameras

Indoor soccer has been of tactical and scientific interest, with applications dedicated to analyze tactical and physiological factors and also physical training. In both cases, the analysis is based on player tracking, done with human supervision. This paper presents an automatic tracking method which shows the trajectories of indoor soccer players during the game and saving skilled labor during the process. For this, we use a predictive filter to model the motion and the observation of multiple stationary cameras, strategically positioned around the court. We associate a particle filter to a robust probabilistic observation model with the measurement in court coordinates. The observation model proposed is based on data fusion across multiple camera coordinates and projected onto the court plane, creating a multimodal and bidirectional probability function, which represents the potential localization of players in the court plane. The probability function uses an appearance model to observe player's location, distinguishing very close players and yielding good weights in the observation model. The experimental results show tracking errors below 70 centimeters in most cases and indicate the potential of the method to help sports teams.
Towards a psycholinguistically motivated algorithm for referring to sets: The role of semantic similarity

This paper explores the role of semantic similarity in content selection and aggregation of expressions referring to sets. Similarity plays a role in ensuring that a referring expression corresponds to a coherent conceptual gestalt. On the basis of corpusbased and experimental evidence we propose an algorithm which (a) separates content selection and aggregation to avoid a combinatorial explosion; (b) uses similarity between entities to prioritise among search alternatives.
Spheres of Influence for More Effective Viral Marketing

What is the set of nodes of a social network that, under a probabilistic contagion model, would get infected if a given node $s$ gets infected? We call this set the sphere of influence of s. Due to the stochastic nature of the contagion model we need to define a notion of "expected" or "typical" cascade: this is a set of nodes which is the closest to all the possible cascades starting from s. We thus formalize the Typical Cascade problem which requires, for a given source node s, to find the set of nodes minimizing the expected Jaccard distance to all the possible cascades from s. The expected cost of a typical cascade also provides us a measure of the stability of cascade propagation, i.e., how much random cascades from a source node s deviate from the "typical" cascade. In this sense source nodes with lower expected costs are more reliable. We show that, while computing the quality of a candidate solution is SPhard, a method based on (1) sampling random cascades and (2) computing their Jaccard Median, can obtain a multiplicative approximation with just O(1) samples. We then devise an index that allows to efficiently compute the sphere of influence for any node in the network. Finally, we propose to approach the influence maximization problem as an instance of set cover on the spheres of influence. Through exhaustive evaluation using real-world networks and different methods of assigning the influence probability to each edge, we show that our approach outperforms in quality the theoretically optimal greedy algorithm.
An LMI approach for sliding mode static output feedback simultaneous stabilisation

A sliding mode controller using only measured output signals for the simultaneous stabilisation of a finite set of uncertain systems is proposed. A noteworthy feature of the approach is that it allows both matched and mismatched uncertainties to be considered. The proposed controller belongs to the class of static output feedback controllers. The synthesis methodology involves solving linear matrix inequality problems. A simulation study is performed to illustrate the design approach and its effectiveness.
Improved Distributed Spanning Tree and Case Study for Replica Location Service

In the traditional distributed spanning tree (DST), randomly selecting representatives makes some nodes become critical nodes, which increase the load of critical nodes and also reduce the fault tolerance of systems. To resolve this problem, an improved DST structure and a searching algorithm were proposed. In this paper, we describe a representative selection rule first, which provides a good load balance and fault tolerance. Next, we define the searching radius to limit the flooding and proof that the time complexity of the searching algorithm is constant level. Finally, we give a case study for replica location service in data grid. Through the experiments with 16 nodes and simulations on overlay networks with larger scale, we present the performances of the structure. The results show that the time complexities of self-adaptive algorithms are logarithmic level, and the performance of the algorithm with searching radius boundary is better than the DST searching algorithm. The improved DST structure can be deployed in data grid to provide a good scalability, load balance, fault tolerance and flexible searching for various applications.
Improving Performance of Rate-Based Transport Protocols in Wireless Environments

Wireless links present higher loss rate than current wired links due to the inherent characteristi cs of wireless transmission. Lossy environments break the fundamental assumption of current transport protocols t hat a loss indicates congestion, which results in a reductio n in throughput. To avoid changes to the infrastructure, we p ropose an end-to-end approach to loss discrimination based on n etwork state estimation at the receiver. Discrimination is achi eved by correlating the short-term jitter history with anomalou s jitter and loss. Through simulation, an experimental rate-based protocol using the discrimination heuristic is compare d to a stock TCP and a TCP modified to deal with wireless los ses. Our experimental results validate the heuristics and show th at better performance can be achieved.
Incorporating Duration Information into I-Vector-Based Speaker Recognition Systems

Most of the existing literature on i-vector-based speaker recognition focuses on recognition problems, where i-vectors are extracted from speech recordings of sufﬁcient length. The ma-jority of modeling/recognition techniques therefore simply ig-nores the fact that the i-vectors are most likely estimated un-reliably when short recordings are used for their computation. Only recently, were a number of solutions proposed in the literature to address the problem of duration variability, all treating the i-vector as a random variable whose posterior distribution can be parameterized by the posterior mean and the posterior covariance. In this setting the covariance matrix serves as a measure of uncertainty that is related to the length of the available recording. In contract to these solutions, we address the problem of duration variability through weighted statistics. We demonstrate in the paper how established feature transformation techniques regularly used in the area of speaker recognition, such as PCA or WCCN, can be modiﬁed to take duration into account. We evaluate our weighting scheme in the scope of the i-vector challenge organized as part of the Odyssey, Speaker and Language Recognition Workshop 2014 and achieve a minimal DCF of 0.280, which at the time of writing puts our approach in third place among all the participating institutions.
1 Weasel : A Mixed-Initiative System to Assist in Military Planning

This paper describes a mixed initiative planning system, called Weasel and its evaluation. Weasel was developed to assist military decision makers in the task of enemy course of action generation. The evaluation assesses Weasel's impact on the decision making performance of two potential user groups. When designing Weasel, we aimed to maximize benefits delivered by the software by focusing support functions on key areas in which expert analysts exhibited difficulties. We also aimed to minimize development, training and maintenance costs by designing displays to reflect expert analysts' representations and relying on human problem solving skills where possible. The goals of the evaluation are to 1) assess whether Weasel increases users' problem solving performance, where performance is measured in terms of overall solution quality, 2) identify the most appropriate user group by assessing whether domain intermediates are helped or hindered more than domain experts, and 3) identify possible negative consequences that may occur when Weasel generates a "brittle" solution. The issues explored in Weasel's development and evaluation are common to many mixed initiative systems.
Recent and future developments in finite element metal forming simulation

After more than 40 years of development, finite element metal forming simulation has reached a high level of maturity. After a short mechanical and thermal introduction, the main scientific and technical developments are briefly described. We consider numerical issues, such as adaptive remeshing or parallel computing; coupling phenomena for a more realistic simulation, such as thermal and metallurgical coupling, with a special emphasis on modeling of microstructure evolution; the use of optimization for forming processes or for parameters identification. Finally the main potential future research fields for the next 10 years are outlined: process stability and stochastic approaches, more effective massively parallel computing and extension of the application to generate the whole "virtual factory".
Measure Semantic Similarity between English Words

Semantic similarity between two words is a fundamental issue for many natural language processing applications. The difficulty lies in that how to develop a computational method that is capable of generating satisfactory results close to what humans would perceive. In this paper, we proposed a novel approach to measure the semantic similarity between two English words by exploring the utility of HowNet, which is a valuable Chinese-English bilingual knowledge base. When compared with a benchmark set by human similarity judgment, the measures achieved by our algorithm are quite sound.
Maximum bit average entropy based binary SIFT (MBAE_BS) for face recognition with pose variation

Pose variation is one of challenging problems in face recognition. Complete Pose Binary SIFT(CPBS) [1]has been proposed to extract binary SIFT from face images of five poses. It could resolve the problem of large pose variation. However, in CPBS, each feature is represented as a bit, it possibly brings about information loss. In this paper, we propose maximum bit average entropy algorithm (MBAE) for binarization of CPBS features. And these features are used for face recognition with pose variation. It preserve at most information with the fewer quantization bits. Firstly, the CPBS features are extracted from the gallery images. Secondly, the CPBS features are binarized according to the maximum bit average entropy adaptively. Finally, the distance between a pair of features is computed based on the weighted hamming distance. The compared experimental results on the CMU-PIE and FERET face databases show that our approach is much better than state-of-the-art algorithms.
HeapVision:Debugging by Interactive Heap Navigation

With the proliferation of online source code repositories such as GitHub and BitBucket, programmers have instant access to countless code examples. With the availability of these resources the focus in software development shifts away from writing code towards understanding source code. Many common algorithms, that see a lot of reuse across different problem domains, involve complex manipulations of data structures and hence the program’s heap (e.g., sorting algorithms). Understanding these manipulations and the effect onto the heap’s structure is a cognitively demanding and complex task. However, despite the importance of such algorithms for many applications, there is little tool support build into current IDEs (Integrated development environment) to help programmers in understanding and debugging such algorithms and data structures. In particular, there currently does not exist any sophisticated tool to visually explore the heap structure and to interactively experience the impact of the algorithms instructions on the heap throughout the execution of the program. In the course of this thesis we have developed a tool called HeapVision, which allows for inspection of and interaction with dynamic visual representations of the heap structure. The interface allows programmers to more easily understand how a data structure and it’s content are modified by a particular sequence of instructions. Furthermore, we have developed a novel pen and touch based interface that allows developers to more directly interact with the running process and to concentrate on the essence of the program. Pen and touch gestures can be used to dynamically change the visual representation of the heap, allowing the user to concentrate on important parts of the heap. We leverage formal program analysis tools in order to automatically abstract away unimportant aspects of the heap, while keeping the parts of a data structure that are being manipulated concrete. In other words we aim to capture the essence of an algorithm’s manipulations so that the user may understand the underlying working principle. Our application can improve the comprehension of algorithms, especially for algorithms that manipulate data structures recursively – such as but not limited to reversing linked lists.
Downlink Throughput Maximization for OFDMA Systems With Feedback Channel Capacity Constraints

In this correspondence, we study the downlink throughput maximization for orthogonal frequency-division multiple-access (OFDMA) systems in the presence of feedback channel capacity constraints. We establish an information-theoretic lower bound on the capacity of feedback channel and build the corresponding test channel that achieves this lower bound. Based on the derived test channel, we formulate two optimization problems that maximize the downlink throughput with quantized channel state information (CSI): i) one problem where throughput is defined with the ergodic throughput and ii) the other problem with the outage throughput, from which we can see the performance limit for given limit feedback channels. We solve the throughput maximization problem through an iterative approach, which achieves the optimal ergodic throughput and the near-optimal outage throughput. Numerical results show that the downlink throughput with a limited feedback of CSI can be close to that with perfect CSI by exploiting correlation properties of downlink CSI for quantization.