Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Efficient and Improved Hierarchical Clustering With Instance and Cluster Level Constraints

Many clustering applications use the computationally efficient non-hierarchical clustering techniques such as k-means. However, less efficient hierarchical clustering is desirable as by creating a dendrogram the user can choose an appropriate value of k (the number of clusters) and in some domains cluster hierarchies (i.e. clusters within other clusters) naturally exist. In many situations apriori constraints/information are available such as in the form of a small amount of labeled data. In this paper we explore using constraints to improve the efficiency of agglomerative clustering algorithms. We show that just finding feasible (satisfying all constraints) solutions for some constraint combinations is NP-complete and should be avoided. For a given set of constraints we derive upper (kmax) and lower bounds (kmin) on the value of k where feasible solutions exist. This allows a restricted dendrogram to be created but its creation is not straight-forward. For some combinations of constraints, starting with a feasible clustering solution (k = r) and joining the two closest clusters results in a “dead-end” feasible solution which cannot be further refined to create a feasible solution with r − 1 clusters even though kmin < r − 1 < kmax. For such situations we introduce constraint driven hierarchical clustering algorithms that will create a complete dendrogram. When traditional algorithms can be used, we illustrate the use of the triangle inequality and a newly defined γ constraint to further improve performance and use the Markov inequality to bound the expected performance improvement. Preliminary results indicate that using constraints can improve the dendrogram quality.
Heuristic search of multiagent influence space

Multiagent planning under uncertainty has seen important progress in recent years. Two techniques, in particular, have substantially advanced efficiency and scalability of planning. Multiagent heuristic search gains traction by pruning large portions of the joint policy space deemed suboptimal by heuristic bounds. Alternatively, influence-based abstraction reformulates the search space of joint policies into a smaller space of influences, which represent the probabilistic effects that agents' policies may exert on one another. These techniques have been used independently, but never together, to solve larger problems (for Dec-POMDPs and subclasses) than previously possible. In this paper, we take the logical albeit nontrivial next step of combining multiagent A* search and influence-based abstraction into a single algorithm. The mathematical foundation that we provide, such as partially-specified influence evaluation and admissible heuristic definition, enables an investigation into whether the two techniques bring complementary gains. Our empirical results indicate that A* can provide significant computational savings on top of those already afforded by influence-space search, thereby bringing a significant contribution to the field of multiagent planning under uncertainty.
Event-based semantic visualization of trajectory data in urban city with a space-time cube

Visualizing the implicit information of trajectory data is very important for analyzing human activities and is of great value in the decision making process. The objective of our research is to provide a semantic visualization method for trajectory data and the result of analysis. To format the trajectory data, we propose an object-oriented abstract data type (ADT). An event, which represents the human activities in our ADT, is a critical element for analysis enhanced with two special attributes: a semantic-based relationship to extract implicit information; and semantic-based levels of details (LOD) as a standard for the processing of raw data. The proposed visualization concept has been implemented in a prototype system with a space-time cube. Further information is achieved by internal queries making use of semantic attributes and is shown in an advanced user defined space-time cube, which displays richer information. The results of the experimental prototype system suggest that the proposed visualization method is feasible and flexible.
Towards Maximum Utilization of Remained Bandwidth in Defected NoC Links

To maximize the utilization of the available networks-on-chip (NoCs) link bandwidth, partially faulty links with low fault level should be utilized while heavily defected (HD) links should be deactivated and dealt with by means of a fault tolerant routing algorithm. To reach this target, we make the following contributions in this paper: 1) we propose a flit serialization (FS) method to efficiently utilize partially faulty links. The FS approach divides the links into a number of equal width sections, and serializes sections of adjacent flits to transmit them on all fault-free link sections to mitigate the unbalance between the flit size and the actual link bandwidth; 2) we propose the link augmentation with one redundant section as a low cost mechanism to mitigate the FS drawback that a link’s available bandwidth is reduced even if it contains only one faulty wire; and 3) we deactivate HD links when their fault level exceed a certain threshold to diminish congestion caused by HD links. The optimal threshold is derived by comparing the zero load packet transmission latency on the HD links and that on the shortest alternative path. Our proposal is evaluated with synthetic traffic and PARSEC benchmarks. Experimental results indicate that the FS method can achieve lower area*power/saturation_throughput value than all state of the art link fault tolerant strategies. With a redundant section in each link, the NoC saturation throughput can be largely improved than just utilizing FS, e.g., 18% when 10% of the NoC wires are broken. Simulation results we obtained at various wire broken rate configurations indicate that we achieve the highest saturation throughput if 4- or 8-section links with a flit transmission latency longer than four cycles are deactivated.
Study on Different Representation Methods for Subspace Segmentation

With many engineering and science application problems, we must deal with a lot of high-dimensional data, such as videos, images, web documents, text, etc. In the areas of computer vision, image processing and machine learning, high-dimensional data are widespread. However, it is very hard for obtaining meaningful learning and inference from these high-dimensional data directly, the computational complexity of highdimensional data is often exponential. However, under many conditions, highdimensional data lie in low-dimensional data corresponding to some classes of the data. Thus, finding the low-dimensional structure from the high-dimensional data is very important. The aim of subspace segmentation is to cluster data that lie in a union of lowdimensional subspaces. In recent years, based on the research of representation methods, many subspace segmentation algorithms appeared. Although these methods are all effective for handling subspace segmentation problems, they all have advantages and disadvantages. This paper focuses on the performance comparison of different subspace segmentation algorithms currently used in handling subspace segmentation problems and views other conventional methods that can be applied in this field.
Attitude tracking of rigid bodies on the special orthogonal group with bounded partial state feedback

A solution to the attitude tracking problem of rigid bodies with kinematic representation directly on the special orthogonal group SO(3) of rotation matrices is proposed. A dynamic partial state feedback controller is designed to address the case where no angular velocity measurements are available. In addition, the gains in the control design can be tuned in advance to ensure that the torque inputs satisfy arbitrary saturation bounds. Stability conditions are provided based on Lyapunov function analysis and Barbalat's lemma. Simulation results are presented to illustrate the performance of the proposed control scheme.
Scheduling Precedence Graphs in Systems with Interprocessor Communication Times

The problem of nonpreemptively scheduling a set of m partially ordered tasks on n identical processors subject to interprocessor communication delays is studied in an effort to minimize the makespan. A new heuristic, called Earliest Task First (ETF), is designed and analyzed. It is shown that the makespan $\omega _{{\text{ETF}}} $ generated by ETF always satisfies $\omega _{{\text{ETF}}} \leqq ({{2 - 1} / n})\omega _{{\text{opt}}}^{(i)} + C$, where $\omega _{{\text{opt}}}^{(i)} $ is the optimal makespan without considering communication delays and C is the communication requirements over some immediate predecessor-immediate successor pairs along one chain. An algorithm is also provided to calculate C. The time complexity of Algorithm ETF is $O(nm^2 )$.
Classification of Music and Speech in Mandarin News Broadcasts

Audio scene analysis refers to the problem of class ifying segments in a continuous audio stream according to content, e.g. speech versus non-speech, music, ambi ent noise, etc. Techniques that support such autom atic segmentation is indispensable for multimedia information processing. For example, it is a precursor to processes such as indexing of speech segments by automatic speech recognition, automatic story segmentation based on recognition transcript s, speaker diarization, etc. This paper describes our work in the developm ent of a speech/music discriminator for Mandarin broadcast news audio. We formed a high-dimensional feature vector that in cludes LPCC, LPS and STFT coefficients totaling 94 in all. We also experimented with three classifiers - the KNN, SVM and MLP. Experiments based on the Voice of America Mandarin news broadcasts show high classification performance with F-measure=0.98. The SVM also strikes the best balance in terms of classification performance and computation time (re al-time) among the three classifiers. 1
A Precise 2-D Wireless Localization Technique Using Smart Antenna

In recent years, localization using angle of arrival (AOA) has gathered more attention, due to the use of smart antenna techniques for performance improvement in mobile communications. In this paper, we propose a precise two dimensional (2-D) localization approach by using AOA measurements which considers the error due to multipath propagation as well as angle estimation error. Specifically, we propose to use three antenna arrays to provide a fix, each with a unique combination of another antenna array. Thus, three sets of antenna arrays are formed altogether. For each set, we first transform angle information into location coordinates by standard geometry and linearization method is used for getting the initial location estimates. Then the Final Position is estimated from the three initial location estimates by the Precision-Weighted Aggregation approach which considers the errors from measurement and multipath propagation. Finally, the performance of the proposed method is evaluated.
Lateral migration of living cells in inertial microfluidic systems explored by fully three-dimensional numerical simulation

The effects of cell size and deformability on the lateral migration and deformation of living cells flowing through a rectangular microchannel has been numerically investigated and compared with the inertial-microfluidics data on detection and separation of cells. The results of this work indicate that the cells move closer to the centerline if they are bigger and/or more deformable and that their equilibrium position is largely determined by the solvent (cytosol) viscosity, which is much less than the polymer (cytoskeleton) viscosity measured in most rheological systems. Simulations also suggest that decreasing channel dimensions leads to larger differences in equilibrium position for particles of different viscoelastic properties, giving design guidance for the next generation of microfluidic cell separation chips.
Structural-Functional Properties of Identified Excitatory and Inhibitory Interneurons within Pre-Bötzinger Complex Respiratory Microcircuits

We comparatively analyzed cellular and circuit properties of identified rhythmic excitatory and inhibitory interneurons within respiratory microcircuits of the neonatal rodent pre-Bötzinger complex (pre-BötC), the structure generating inspiratory rhythm in the brainstem. We combined high-resolution structural–functional imaging, molecular assays for neurotransmitter phenotype identification in conjunction with electrophysiological property phenotyping, and morphological reconstruction of interneurons in neonatal rat and mouse slices in vitro. This approach revealed previously undifferentiated structural–functional features that distinguish excitatory and inhibitory interneuronal populations. We identified distinct subpopulations of pre-BötC glutamatergic, glycinergic, GABAergic, and glycine-GABA coexpressing interneurons. Most commissural pre-BötC inspiratory interneurons were glutamatergic, with a substantial subset exhibiting intrinsic oscillatory bursting properties. Commissural excitatory interneurons projected with nearly planar trajectories to the contralateral pre-BötC, many also with axon collaterals to areas containing inspiratory hypoglossal (XII) premotoneurons and motoneurons. Inhibitory neurons as characterized in the present study did not exhibit intrinsic oscillatory bursting properties, but were electrophysiologically distinguished by more pronounced spike frequency adaptation properties. Axons of many inhibitory neurons projected ipsilaterally also to regions containing inspiratory XII premotoneurons and motoneurons, whereas a minority of inhibitory neurons had commissural axonal projections. Dendrites of both excitatory and inhibitory interneurons were arborized asymmetrically, primarily in the coronal plane. The dendritic fields of inhibitory neurons were more spatially compact than those of excitatory interneurons. Our results are consistent with the concepts of a compartmental circuit organization, a bilaterally coupled excitatory rhythmogenic kernel, and a role of pre-BötC inhibitory neurons in shaping inspiratory pattern as well as coordinating inspiratory and expiratory activity.
Fast parallel parking for autonomous vehicles using Gompertz curves

A new method for the planning and autonomous execution of a single-trajectory, velocity-independent, parallel-parking manoeuvre for autonomous vehicles is presented. The procedure commences with the identification and pre-selection of a smooth sigmoid trajectory known as the Gompertz curve in parametric format. Trajectory parameters are determined in real-time during the path-planning phase using an optimization scheme in order to generate a candidate path. The optimization scheme takes into account the maximum steering angles that can be physically realized and checks the generated candidate trajectory for collisions. Thereafter, the trajectory is re-parametrized to arc-length format using the cubic interpolation method and the vehicle orientation at every point of the trajectory is deduced. Following that, values of the steering angle(s) are determined. In the final step, the vehicle uses odometry to follows the arc-length parametrized path in reverse in order to park itself in a single-manoeuvre. The proposed method is substantiated through both extensive simulations and real sensor data.
Correct Occlusion Effect in the Optical See-through Immersive Augmented Reality Display System.

This paper proposes the technique for creating the correct occlusion effect in an immersive augmented reality environment with wide field of view. Generally, it is difficult to represent the correct occlusion between virtual objects and real objects in augmented reality displays, based on a semi-transparent mirror. This problem also occurs in the immersive augmented reality display system, named ARView, which we have developed in previous work. To address this problem, we proposed a light projector technique involving projectors which illuminated the surface of real objects using directed illumination which could create shadows occluded by the virtual objects. Notably, this method represented the correct occlusion effect, not only with static objects, but also with moving objects. In this study, we implemented this technique in the ARView system and conducted an experiment to evaluate the effectiveness of this method.
Semantic Feature Analysis Treatment for Aphasic Word Retrieval Impairments: What’s in a Name?

Abstract This article delineates differences among treatment paradigms that have been called semantic feature analysis treatment and reviews the outcomes of these treatment studies regarding improved naming of treated items, maintenance of treatment effects over time, and generalized improvement to untreated items. Differences in outcomes among the treatment paradigms highlight the importance of using different names for different treatment paradigms.
A pplying General Sign Theory to Testing Language ( Verbal ) and Nonverbal A bilities

A general theory of signs shows a deep logical dependency of nonverbal/performance tests on conventional linguistic signs. Two hypotheses follow : H 1: To the extent that nonverbal abilities can be measured, they must be p ositively correlated with primary language abilities . H2: (a) in the early stages of acquiring a second or f oreign language, p rof iciencies in the p rimary (stronger or native) language should correlate more strongly with nonverbal abilities than p rof iciencies in the nonp rimary (weaker, second or f oreign) language; and (b) as p ersons app roach greater p arity between their p rimary and any nonp rimary language, correlations of nonverbal scores with p rof iciencies in the two languages should app roach equality . By contrast, the Cattell-Horn theory (Horn and Cattell, 1966; Horn, 1988 ) predicts about the same level of correlation throughout the course of development and Gardners theory of multiple intelligences (Gardner, 1993) predicts that distinct intelligences should be uncorrelated. All three theories can be tested by examining the simple correlations of language proficiency measures with nonverbal IQ scores in intermediate and advanced nonprimary language learners.
Analytical Modeling of the Link Delay Characteristics for IEEE 802.11 DCF Multi-Rate WLANs

Performance evaluation of IEEE 802.11 DCF has been widely studied under the assumption of a single transmission rate by all competing nodes. In practice, however, stations typically use different transmission rates. This is done to match to the changing channel conditions. Stations with poor channel conditions typically employ lower transmit rates in comparison to stations with better channel conditions. Under such scenarios, the throughput of each station becomes independent of its own transmit rate; rather it gets bounded by the slowest transmitting peer. This has been popularly classified as the performance anomaly problem. This paper provides a simple yet accurate analytical framework to study the link delay characteristics of such multi-rate networks
A Multiple-Ring Model for Cognitive Radio Aggregate Interference

This paper proposes a new, flexible, multiple ring model for the aggregate interference analysis of a Poisson field of cognitive radio transmitters distributed over an annular region. For Rayleigh fading or composite Rayleigh fading and Gamma shadowing environments, the moment generating function of the aggregate interference and the exact and asymptotic analysis of the outage are derived for the model. Typically, the model is most accurate for larger guard distances and lower path loss exponents. However, its parameters can be tuned such that a required accuracy is obtained for a trade-off in complexity.
An improved LPP algorithm

Discriminant Locality Preserving Projection (DLPP) has been successfully used as a dimensionality reduction technique to many classification problems, which incorporate discriminant information into Locality Preserving Projection (LPP) to improve recognition rate. However, in order to avoid small sample size problem, DLPP needs to reduce dimensions, which will lose some important discriminative information. Direct Linear Discriminant Analysis (DLDA) can solve the problem by diagonalization. Inspired by DLDA, we propose a novel method of improvement algorithm, which incorporate DLDA into LPP. Compared with DLPP and LPP, this algorithm not only preserves more effective discriminative information, but also solves the small sample size problem in dimensionality reduction. It also improves light sensitivity when distinguish an uneven illumination image. The modified LPP algorithm achieves better result than DLPP and LPP in face recognition.
A template based user-teaching system for an Assistive Robot

Demographics issues, characterised by an increasing elderly population, are expected to be a major concern both in Europe and other countries around the world. A proposed cost and care solution to these issues has been suggested that uses assistive robots in ’smarthome’ environments. The deployment of such integrated facilities presents many challenges, one of which concerns the customisation of such systems to meet the needs of the elderly person themselves. One approach is to allow the elderly person to actually teach the robot sufficient behaviours that meet their care requirements. The teaching could equally well be carried out by the elderly person’s relatives or carers. The overriding premise being that teaching is both intuitive and ’non-technical’. As part of a European project investigating these issues we have deployed a commercially available robot in a fully sensorised but otherwise ordinary suburban house, and designed a non-technical teaching system, based on behavioural templates, to achieve this goal. We have evaluated this integrated system within the house with 20 participants in a Human-Robot interaction experiment. Results indicate that participants overall found the interface easy to use, and felt that they would be capable of using it in a real-life situation. There were also some salient individual differences within the sample.
Lipopolysaccharide Induces Inflammatory Hyperalgesia Triggering a TLR4/MyD88-Dependent Cytokine Cascade in the Mice Paw

Inflammatory pain can be triggered by different stimuli, such as trauma, radiation, antigen and infection. In a model of inflammatory pain caused by infection, injection in the mice paw of lipopolysaccharide (LPS), a Toll-like receptor 4 (TLR4) agonist, produces mechanical hyperalgesia. We identify here the TLR4 linked signaling pathways that elicit this response. Firstly, LPS paw injection in wild type (WT) mice produced mechanical hyperalgesia that was not altered in TRIF-/- mice. On the other hand, this response was absent in TLR4 mutant and MyD88 null mice and reduced in TNFR1 null mice. Either an IL-1 receptor antagonist, anti-KC/CXCL1 antibody, indomethacin or guanethidine injection also lessened this response. Moreover, LPS-induced time dependent increases in TNF-α, KC/CXCL1 and IL-1β expression in the mice paw, which were absent in TLR4 mutant and MyD88 null mice. Furthermore, in TNFR1 deficient mice, the LPS-induced rises in KC/CXCL1 and IL-1β release were less than in their wild type counterpart. LPS also induced increase of myeloperoxidase activity in the paw skin, which was inhibited in TLR4 mutant and MyD88 null mice, and not altered in TRIF-/- mice. These results suggest that LPS-induced inflammatory pain in mice is solely dependent on the TLR4/MyD88 rather than the TLR4/TRIF signaling pathway. This pathway triggers pronociceptive cytokine TNF-α release that in turn mediates rises in KC/CXCL1 and IL-1β expression. Finally, these cytokines might be involved in stimulating production of directly-acting hyperalgesic mediators such as prostaglandins and sympathomimetic amine.