Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised vs. unsupervised learning of spectro temporal speech features

To overcome limitations of purely spectral speech features we previously introduced Hierarchical Spectro-Temporal (H IST) features. We could show that a combination of H IST and standard features does reduce recognition errors in clean and in noise. The HIST features consist of two hierarchical layers where the corresponding filter functions are learned in a data driven way. In this paper we investigate how different learning methodologies applied to the learning of the filters on the second layer influence the performance. We compare Non-negative Matrix Factorization (NMF), Non-negative Sparse Coding (NSC), and Weight Coding (WC) on a noisy digit recognition task. NMF and NSC are unsupervised learning algorithms whereas WC also includes class specific information in the learning process. Additionally we investigate how a mismatch between the database used for learning the features and the one employed for training and testing the recognition system influences the performance.
Logarithmic Sobolev inequality for the inhomogeneous zero range process

We prove that the logarithmic Sobolev constant for the inhomogeneous symmetric nearest neighbour zero range process on a cube of size N^d grows as N^2. We apply this result to the inhomogeneous process which arises in the study of the homogeneous version of the zero range interacting particle system with colours.
Physical-aware topology construction and importance of underlying topological information in provider-independent overlays

Overlay networks have been viewed as one of promising intermediary solutions to provide a supplement with strengthened performance to the existing “best efforts” Internet. Previous researches have shown that overlays constructed taking into consideration of the underlying network topology information perform better than those oblivious to those information. In this paper, we first propose a novel heuristic algorithm to solve the provider-independent overlay construction issue given accurate underlying topological information. However, exact physical topology is usually shielded from the Internet users especially in multiple-domain scenarios and difficult to obtain. One of the issues is the Anonymous Routers (AR) presence in the Internet Thus only inaccurate underlying topologies can be obtained. Even if AR resolution algorithms are implemented, there is no guarantee that it can infer an accurate topology. Therefore, we take a step further to study the impact of inaccurate underlying topological information on overlay topology construction. Initial results obtained show that the availability and accuracy of underlying topology information has impact on its effectiveness in constructing a good overlay topology and cautions are needed when taking advantage of the acquired inaccurate underlying topology information.
Exploiting Context in HCI Design for Mobile Systems

The last five years has seen a shift in the nature of mobile computers. The development of increasingly powerful laptop computer systems has been mirrored by the production of a range of small computational devices. The increased prominence of these devices outlined a number of distinct research challenges. These challenges have tended to focus on extending the utility of these devices using new forms of interaction; techniques to overcome display limitations or improvements in the general ergonomics of these devices. The merging of these devices with existing telecommunication services and the production of devices that offer connections to other systems presents yet another set of research challenges in terms of the development of cooperative multi-user applications.
Scalable and Secure Architectures for Online Multiplayer Games

Networked games have rapidly evoved from small 4-8 person, one-time play games to large-scale persistent games involving thousands of participants. However, most of these games have centralized client-server architectures which create significant robustness and scalability bottlenecks. This thesis proposes a new modular distributed architecture for interactive multiplayer games. This architecture, called Colyseus, consists of three independent, interacting components: Object Placement, Object Discovery and Replica Synchronization. This modularization enables a variety of optimizations which are used critically for load-balancing, for supporting a wide range of consistency requirements and adhering to the tight latency constraints of fast-paced games. This allows to prevent computational and bandwidth hot-spots in the system. Finally, in order to make the system deployable in a peer-to-peer setting, I propose security mechanisms to detect cheating and punish cheating players. I expect these techniques to be applicable to a wide range of multi-party collaborative applications as well.
Questions as a generative strategy for knowledge transfer and problem solving

QUESTIONS AS A GENERATIVE STRATEGY FOR KNOWLEDGE TRANSFER AND PROBLEM SOLVING Brett Howard Cook-Snell Old Dominion University, 2015 Director: Dr. Ginger S. Watson Consistent with generative learning theory, Grabowski (1996) suggests the use of questions may serve as an effective generative strategy for learning. However, the learning effects o f questions have produced conflicting results (Bulu & Pedersen, 2010; Chen & Bradshaw, 2007; Choi, Land, & Turgeon, 2005; Chou & Liang, 2009; Davis & Linn, 2000; Domisch & Sperling, 2008; Ge & Land, 2003). Similarly, there are five basic challenges inherent in question research (Andre, 1979). These include lack of intentional and consistent question design, lack of detail making it difficult to replicate studies, lack of control groups against which to measure differences, aggregation of results only while omitting a question-by-question analysis, and results based upon the learners’ ability to recall information versus near and far transfer (Andre, 1979). O f importance for this study is the latter. Knowledge transfer is a major contributor to problem-solving (Jonassen, 201 lb; Mayer & Wittrock, 1996). This study assessed the use o f domain-specific, domain-general, and combined-domain-general specific question types when compared to a control group of no questions as a generative strategy promoting knowledge acquisition, retention, and transfer in support of solving wellstructured and ill-structured problems while controlling for the methodological concerns of Andre (1979). The domain of the instruction was interpersonal communications and was delivered as a web-based course in two instructional units along with pretest and posttest assessment, unit assessments, and a role-play simulation using an automated agent to measure far transfer problem solving. Results from the study suggested no significant differences between treatment groups for knowledge acquisition, retention, or near problem-solving transfer. The data did suggest significant differences in far problem-solving transfer for the treatment group receiving domain-specific questions only. Further, measures o f central tendency suggest domain-specific questions may produce slightly greater gains in performance over domain-general only and combined domain-specific/domain-general questions.
Multi2Rank: Multimedia Multiview Ranking

Multimedia retrieval is a search and ranking task defined over multiple modalities. These modalities include speech, image, and text, which provide different views of the multimedia object. Queries to a multimedia retrieval system often take the form of a text only query and return a ranked result set which combines these multiple views. The text only query includes multiple phrases which identify features of a specific view. This multiview problem presents a challenge in mapping these phrases into the correct view feature space. A second challenge for the multimedia retrieval system is in building a ranking model which considers the unique feature space of each view. In this paper, we propose a hierarchical multimedia multiview rank learning model, called Multi2Rank, to overcome the challenges of this unique ranking problem. The first layer of our model uses natural language processing techniques to identify view specific phrases and output a ranked mapping of the phrases into their respective views. Next, we model the individual feature space for each multimedia view and create a view specific model using gradient boosted regression trees. The ranked set from each unique view is then passed to the final layer of the hierarchy, where the model generates a final ranked result set. We show that our multiview rank learning approach is effective by evaluating the methods using a large Internet video repository, queries, and ground truth, from the TRECVid evaluations.
PhotoTima: Visual Exploration of Photos with Spatiotemporal References

Nowadays, photos associated with spatiotemporal references are available widely on computers as well as on the Internet. There exist several tools for the exploration of photos with regard to their geographical and/or temporal structures. However, communicating spatial and temporal contents of photos remains a challenging task. In this paper, we follow Peuquet’s triad framework in visually combining space, time, and descriptive contents of photos to deal with that task. We address the visual exploration of spatiotemporal photos to be communicated by an integrated design of different abstraction levels in space, time, and image displaying. The tool PhotoTima is introduced for the exploration of Flickr photos in connection with their timestamps, geo-coordinates, and user-generated tags.
Quantum bootstrapping via compressed quantum Hamiltonian learning

A major problem facing the development of quantum computers or large scale quantum simulators is that general methods for characterizing and controlling are intractable. We provide a new approach to this problem that uses small quantum simulators to efficiently characterize and learn control models for larger devices. Our protocol achieves this by using Bayesian inference in concert with Lieb–Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. We also show that the Lieb–Robinson velocity is epistemic for our protocol, meaning that information propagates at a rate that depends on the uncertainty in the system Hamiltonian. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8 qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data. Finally, we provide upper bounds for the Fisher information that show that the number of experiments needed to characterize a system rapidly diverges as the duration of the experiments used in the characterization shrinks, which motivates the use of methods such as ours that do not require short evolution times.
An image-based rugosity mesostructure for rendering fine haptic detail

Rendering a surface’s fine haptic features (or mesostructure) by precise geometric modeling in dense triangle meshes requires special structures, equipment, and unrealistically high sampling rates needed for perceiving detailed, rugged models. Some approaches simulate haptic texture at a lower processing cost, but at the expense of fidelity of perception. We propose a method for rendering local heightfield maps (or displacement textures) and corresponding normals to model mesostructure into much simpler triangle meshes, relying in fast collision detections followed by the rendering of surface detail by modulating force response using 2D texture of heightfield altitudes and precomputed normals. We compare our method against a force mapping implementation for rendering/perceiving the same models with textures using normal maps. The proposed technique allows for real time accurate perception of fine 3D surface detail for given models with low processing costs and undiminished sampled rates. Experimental results and user testing evaluations are presented to show the goodness of the approach, and actually determining practical threshholds in magnitude and frecuency between modeling surface with small triangles or mesostructured ones.
The Role of Textual Graph Patterns in Discovering Event Causality

We present a novel method for discovering causal relations between events encoded in text. In order to determine if two events from the same sentence are in a causal relation or not, we first build a graph representation of the sentence that encodes lexical, syntactic, and semantic information. From such graph representations we automatically extract multiple graph patterns (or subgraphs). The patterns are sorted according to their contribution to the expression of intra-sentential causality between events. To decide whether a pair of events is in a causal relation, we employ a binary classifier that uses the graph patterns. Our experimental results indicate that capturing causal event relations using graph patterns outperforms existing methods. DOI: 10.4018/978-1-60960-741-8.ch019
Performance of the fast subspace-based localization methods

Source localization is based on the spectral matrix algebraic properties. Propagator, and Ermolaev-Gershman (EG)noneigenvector algorithms exhibit a low computational load. Propagator is based on the spectral matrix partitioning. EG algorithm obtains an approximation of noise subspace using an adjustable power parameter of the spectral matrix and choosing a threshold value. These algorithms are efficient in non-noisy or high signal to noise ratio (SNR) environments. However both algorithms shall be improved. Propagator is not robust to noise; EG algorithm requires the knowledge of a threshold value between largest and smallest eigenvalues, which are not available as eigendecomposition is not performed. In this paper, we aim at demonstrating the usefulness of QR and LU factorizations of the spectral matrix for these methods. Experiments show that the modified propagator and EG algorithms based on factorized spectral matrix lead to better localization results, compared to the existing methods.
THE FUSS ALGORITHM: A FAST UNIVERSAL SELF-TUNED SAMPLER WITHIN GIBBS

Bayesian inference often requires efﬁcient numerical approximation algorithms such as sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods. The Gibbs sampler is a well-known MCMC technique widely applied in several ﬁelds (e.g., machine learning, ﬁnance, etc.). In the application of the Gibbs sampler one needs to efﬁciently generate values from univariate full-conditional distributions. In this work, we present a simple, self-tuned and extremely efﬁcient MCMC algorithm which produces virtually independent samples from univariate target densities. The proposal density used is self-tuned and tailored to the speciﬁc target, but it is not adaptive. Indeed, the proposal is adjusted during an initialization stage following a simple procedure. As a consequence, there is no “fuss” about convergence or tuning, and the execution of the algorithm is remarkably sped up. Although it can be used as a stand-alone algorithm to sample from a generic univariate distribution, the proposed approach is particularly suited for its use within a Gibbs sampler, especially when sampling from spiky multi-modal distributions. Hence, we call it FUSS (Fast Universal Self-tuned Sampler). Numerical experiments on several data sets show its good performance in terms of speed and estimation accuracy.
A Prosurvival Function for the p75 Receptor Death Domain Mediated via the Caspase Recruitment Domain Receptor-Interacting Protein 2

In addition to promoting cell survival, neurotrophins also can elicit apoptosis in restricted cell types. Recent results indicate that nerve growth factor (NGF) can induce Schwann cell death via engagement of the p75 neurotrophin receptor. Here we describe a novel interaction between the p75 receptor and receptor-interacting protein 2, RIP2 (RICK/CARDIAK), that accounts for the ability of neurotrophins to choose between a survival-versus-death pathway. RIP2, an adaptor protein with a serine threonine kinase and a caspase recruitment domain (CARD), is highly expressed in dissociated Schwann cells and displays an endogenous association with p75. RIP2 binds to the death domain of p75 via its CARD domain in an NGF-dependent manner. The introduction of RIP2 into Schwann cells deficient in RIP2 conferred NGF-dependent nuclear transcription factor-κB (NF-κB) activity and decreased the cell death induced by NGF. Conversely, the expression of a dominant-negative version of RIP2 protein resulted in a loss of NGF-induced NF-κB induction and increased NGF-mediated cell death. These results indicate that adaptor proteins like RIP2 can provide a bifunctional switch for cell survival or cell death decisions mediated by the p75 neurotrophin receptor.
Orthogonal Evolutionary Algorithm and its Application in Circuit Design

During the space electronic system in carries out the exploratory mission in the deep space, it maybe faced with kinds of violent natural environment, to electric circuit's performance, the volume, the weight and the stability proposed a higher request, the traditional circuit design method already more and more with difficulty satisfied this kind of request. In this paper, proposed a new orthogonal evolutionary algorithm which uses in the electronic system circuit optimization design and through the experiment proved, the algorithm obtains the circuit structure to surpass the tradition circuit design method. For the case studies, this means has proved to be efficient and the experiment results show that the new means have got the better results. Streszczenie. W artykule zaproponowano nowy ortogonalny algorytm ewolucyjny pozwalający na optymalizacje projektowania systemow elektronicznych. Eksperymenty dowiodly ze alorytm jest znacznie skuteczniejszy od tradycyjnych metod projektowania. (Ortogonalny algorytm ewolucyjny i jego zastosowanie do projektowania obwodow).
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data

We consider the problem of efficiently approximating and encoding high-dimensional data sampled from a probability distribution $\rho$ in $\mathbb{R}^D$, that is nearly supported on a $d$-dimensional set $\mathcal{M}$ - for example supported on a $d$-dimensional Riemannian manifold. Geometric Multi-Resolution Analysis (GMRA) provides a robust and computationally efficient procedure to construct low-dimensional geometric approximations of $\mathcal{M}$ at varying resolutions. We introduce a thresholding algorithm on the geometric wavelet coefficients, leading to what we call adaptive GMRA approximations. We show that these data-driven, empirical approximations perform well, when the threshold is chosen as a suitable universal function of the number of samples $n$, on a wide variety of measures $\rho$, that are allowed to exhibit different regularity at different scales and locations, thereby efficiently encoding data from more complex measures than those supported on manifolds. These approximations yield a data-driven dictionary, together with a fast transform mapping data to coefficients, and an inverse of such a map. The algorithms for both the dictionary construction and the transforms have complexity $C n \log n$ with the constant linear in $D$ and exponential in $d$. Our work therefore establishes adaptive GMRA as a fast dictionary learning algorithm with approximation guarantees. We include several numerical experiments on both synthetic and real data, confirming our theoretical results and demonstrating the effectiveness of adaptive GMRA.
A framework for rapid-prototyping of context based ubiquitous computing applications

Ubiquitous computing application scenarios include clients and sentry nodes in a smart space, where clients request for services from the sentries. Providing services is subject to context, which is information derived from the system and environment. These client-sentry interactions and the evolving context of the system dictate the behavior of the application. We provide a framework for rapid-prototyping of such applications. It includes a language to specify policies, a data structure for representing them, and a model for execution. A simulation environment exploiting already existing tools is provided
Role of the Mis12 Complex in Kinetochore Assembly @bullet Petrovic Et Al. Jcb Supplemental Material

Figure S1. Additional properties of the MIS12C–NDC80C–KNL1 interaction. (A) Size-exclusion chromatography elution profiles and SDS-PAGE analysis of MIS12C NSL1-258 (top), SPC24 104–197 –SPC25 99–224 (middle), and their stoichiometric combination (bottom). A stoichiometric MIS12C NSL1-258 –SPC24 104–197 – SPC25 99–224 complex forms, showing that the short SPC24–SPC25 construct is sufficient to bind MIS12C. (B) Pellet (P) and supernatant (S) partitions of taxol-stabilized microtubule binding assays performed with growing concentrations of MIS12C. MIS12C does not cosediment with microtubules, nor does it increase the affinity of NDC80C for microtubules. At the concentrations of NDC80C and salt used in this assay, 60% of NDC80C is found in the microtubule pellet. The same ratio of NDC80C in the pellet and supernatant fractions is observed in the presence of MIS12C, suggesting that the latter does not increase the binding affinity of NDC80C for microtubules. (C) Size-exclusion chromatography elution profiles and SDS-PAGE analysis of re-combinant NDC80C expressed in, and purified from, insect cells (top), KNL1 2106–2316 (middle), and their stoichiometric combination (bottom). No binding was observed. (A and C) Dashed gray lines and numbers indicate elution markers in the size-exclusion chromatography experiments and their molecular masses (in kilodaltons), respectively.
Randomizing DEA Efficiency Scores with Beta Distribution

Since the original publication on Data Envelopment Analysis (DEA) by Charnes et al. (1978), a considerable amount of research publications have appeared in decision science literature, a significant portion of which focusing on efficiency and productivity in the banking sector. A comprehensive survey of bank efficiency studies could be found in Fethi and Pasiouras (2010). They have examined bank branch efficiencies in more than 30 studies over the period 1998-2009. A shortcoming of DEA modeling that has come under criticism is that input/output data in DEA are treated as deterministic, but in reality, these data are contaminated with statistical noise, and therefore, efficiency score of each decision making unit (DMU) does not reflect reality. In this chapter, we propose a method to randomize efficiency scores by treating each score as an order statistic of an underlying Beta distribution. Then, we apply this method to a small set of banks from Sri Lanka and show how to do the randomization. To our knowledge, to date, very few authors have discussed similar randomization in an efficiency study.
A development of class e converter circuit for loosely coupled inductive power transfer system

This paper presents an Inductive Power Transfer (IPT) system design using a Class E converter circuit. The Class E converter is used to drive a nonlinear load and theoretically it offers 100% efficiency. To be specifically, the performance of IPT system at 1MHz operating frequency and 9V DC supply voltage is analyzed. Voltage doubler rectifier and Darlington circuit are proposed in this paper to maximize output power. Moreover, to ensure the resonant inductive coupling in IPT system,capacitor compensation is also proposed in this work. Based on the experimental results, the output power with the capacitor compensation circuit is 1.6W at 15 mm air gap distance is better than the circuit without a capacitor compensation.