Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quantitative Comparison Study of Power-Electronic-Driven Flux-Modulated Machines Using Magnetic Field and Thermal Field Co-Simulation

Low-speed flux-modulated permanent-magnet (PM) machines do not need to conform to the conventional design rule which requires identical number of pole-pairs in both stator and rotor. In flux-modulated machines, special ferromagnetic segments in the airgap are used to modulate the magnetic field. In this paper, a general rule to compare different types of electric machines as well as measures to improve the torque density in these machines are presented. In this paper, the energy conversion capacity of different machines with the same physical size and the same operating temperature-rise are compared. An adaptive-order method for modeling the load-temperature-rise relationship is presented to reduce the computing time for this inverse problem. Three power-electronic-driven PM electric machines, which are, namely, a traditional PM machine, a radial-flux-modulated machine (RFMM), and an axial-flux-modulated machine (AFMM), are analyzed and compared based on their temperature distribution and electromagnetic torque density using magnetic field and thermal field computation. Experimental results of an AFMM prototype are used to validate the temperature-rise which is computed using 3-D finite-element method (3-D FEM).
Pharmacological inhibition of the transcription factor PU.1 in leukemia

The transcription factor PU.1 is often impaired in patients with acute myeloid leukemia (AML). Here, we used AML cells that already had low PU.1 levels and further inhibited PU.1 using either RNA interference or, to our knowledge, first-in-class small-molecule inhibitors of PU.1 that we developed specifically to allosterically interfere with PU.1-chromatin binding through interaction with the DNA minor groove that flanks PU.1-binding motifs. These small molecules of the heterocyclic diamidine family disrupted the interaction of PU.1 with target gene promoters and led to downregulation of canonical PU.1 transcriptional targets. shRNA or small-molecule inhibition of PU.1 in AML cells from either PU.1lo mutant mice or human patients with AML-inhibited cell growth and clonogenicity and induced apoptosis. In murine and human AML (xeno)transplantation models, treatment with our PU.1 inhibitors decreased tumor burden and resulted in increased survival. Thus, our study provides proof of concept that PU.1 inhibition has potential as a therapeutic strategy for the treatment of AML and for the development of small-molecule inhibitors of PU.1.
Attaching Multiple Prepositional Phrases: Generalized Backed-off Estimation

There has recently been considerable interest in the use of lexically-based statistical techniques to resolve prepositional phrase attachments. To our knowledge, however, these investigations have only considered the problem of attaching the first PP, i.e., in a [V NP PP] configuration. In this paper, we consider one technique which has been successfully applied to this problem, backed-off estimation, and demonstrate how it can be extended to deal with the problem of multiple PP attachment. The multiple PP attachment introduces two related problems: sparser data (since multiple PPs are naturally rarer), and greater syntactic ambiguity (more attachment configurations which must be distinguished). We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments.
Automatic Musical Pattern Feature Extraction Using Convolutional Neural Network

Music genre classification has been a challenging yet promising task in the field of music information retrieval (MIR). Due to the highly elu- sive characteristics of audio musical data, retrieving informative and reliable features from audio signals is crucial to the performance of any music genre classi- fication system. Previous work on audio music genre classification systems mainly concentrated on using timbral features, which limits the performance. To address this problem, we propose a novel approach to extract musical pattern features in audio music using convolutional neural network (CNN), a model widely adopted in image information retrieval tasks. Our experiments show that CNN has strong capacity to capture informative features from the variations of musical patterns with minimal prior knowledge pro- vided. is adopted in image information retrieval tasks. Migrat- ing technologies from another research field brings new opportunities to break through the current bottleneck of music genre classification. The proposed musical pat- tern feature extractor has advantages in several aspects. It requires minimal prior knowledge to build up. Once obtained, the process of feature extraction is highly ef- ficient. These two advantages guarantee the scalability of our feature extractors. Moreover, our musical pattern features are complementary to other main-stream feature sets used in other classification systems. Our experiments show that musical data have very similar characteristics to image data so that the variation of musical patterns can be captured using CNN. We also show that the mu- sical pattern features are informative for genre classifica- tion tasks.
Genetic Algorithms and Quantum Computation

Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area. Henceforth, we review some basic concepts in quantum computation and GAs and emphasize their inherent parallelism. Next, we review the application of GAs for learning quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.
A New Approach for Boundary Recognition in Geometric Sensor Networks

We describe a new approach for dealing with the following central problem in the self-organization of a geometric sensor network: Given a polygonal region R, and a large, dense set of sensor nodes that are scattered uniformly at random in R. There is no central control unit, and nodes can only communicate locally by wireless radio to all other nodes that are within communication radius r, without knowing their coordinates or distances to other nodes. The objective is to develop a simple distributed protocol that allows nodes to identify themselves as being located near the boundary of R and form connected pieces of the boundary. We give a comparison of several centrality measures commonly used in the analysis of social networks and show that restricted stress centrality is particularly suited for geometric networks; we provide mathematical as well as experimental evidence for the quality of this measure.
On Kernelization of Supervised Mahalanobis Distance Learners

This paper focuses on the problem of kernelizing an existing supervised Mahalanobis distance learner. The following features are included in the paper. Firstly, three popular learners, namely, "neighborhood component analysis", "large margin nearest neighbors" and "discriminant neighborhood embedding", which do not have kernel versions are kernelized in order to improve their classification performances. Secondly, an alternative kernelization framework called "KPCA trick" is presented. Implementing a learner in the new framework gains several advantages over the standard framework, e.g. no mathematical formulas and no reprogramming are required for a kernel implementation, the framework avoids troublesome problems such as singularity, etc. Thirdly, while the truths of representer theorems are just assumptions in previous papers related to ours, here, representer theorems are formally proven. The proofs validate both the kernel trick and the KPCA trick in the context of Mahalanobis distance learning. Fourthly, unlike previous works which always apply brute force methods to select a kernel, we investigate two approaches which can be efficiently adopted to construct an appropriate kernel for a given dataset. Finally, numerical results on various real-world datasets are presented.
Corpus-Based Extension of Semantic Lexicons in Large Scale

During recent years there has been an increased interest to acquire or extend, on a large-scale, high-quality semantic lexicons. The methodology is usually corpus-driven. It is based on the (re-)use of machine readable resources of various types, and the application of cost effective ways to eliminate the acquistion bottleneck, i.e. derivational morphology, customization of off-the-shelf resources, statistical techniques and shallow parsing. This paper investigates how, and to what extent the ﬂexibility and robustness of a partial parser can be utilized to fully automatically achieve this goal. Our work is based on the observation that members of a semantic group are often surrounded by other members of the same group in text. Given a few category members we use parsed corpora to collect surrounding contexts and try to identify other words that also belong to the same group.
Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences

We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variable-length Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.
On the Decoding and Optimizing Performance of Four-Group Decodable Space-Time Block Codes

A class of rate-one space-time block codes (STBC) allowing the decoding of transmitted symbols into four groups is recently proposed by Yuen, Guan and Tjhung. This code is called four-group decodable STBC (4Gp-STBC). In this paper, the equivalent channel of 4Gp-STBC is derived and a new method to decode 4Gp-STBC based on sphere decoders is presented. Furthermore, the performance of 4Gp-STBC is analyzed. A new signal rotation method is proposed, which performs better than the existing one.
A novel efficient power‐saving MAC protocol for multi‐hop MANETs

Following recent advances in the performance of ad hoc networks, the limited life of batteries in mobile devices poses a bottleneck in their development. Consequently, how to minimize power consumption in the Medium Access Control (MAC) layer of ad hoc networks is an essential issue. The power‐saving mode (PSM) of IEEE 802.11 involves the Timing Synchronization Function to reduce power consumption for single‐hop mobile ad hoc networks (MANETs). However, the IEEE 802.11 PSM is known to result in unnecessary energy consumption as well as the problems of overheating and back‐off time delay. Hence, this study presents an efficient power‐saving MAC protocol, called p‐MANET, based on a Multi‐hop Time Synchronization Protocol, which involves a hibernation mechanism, a beacon inhibition mechanism, and a low‐latency next‐hop selection mechanism for general‐purpose multi‐hop MANETs. The main purposes of the p‐MANET protocol are to reduce significantly the power consumption and the transmission latency. In the hibernation mechanism, each p‐MANET node needs only to wake up during one out of every N beacon interval, where N is the number of beacon intervals in a cycle. Thus, efficient power consumption is achieved. Furthermore, a beacon inhibition mechanism is proposed to prevent the beacon storm problem that is caused by synchronization and neighbor discovery messages. Finally, the low‐latency next‐hop selection mechanism is designed to yield low transmission latency. Each p‐MANET node is aware of the active beacon intervals of its neighbors by using a hash function, such that it can easily forward packets to a neighbor in active mode or with the least remaining time to wake up. As a consequence, upper‐layer routing protocols can cooperate with p‐MANET to select the next‐hop neighbor with the best forwarding delay. To verify the proposed design and demonstrate the favorable performance of the proposed p‐MANET, we present the theoretical analysis related to p‐MANET and also perform experimental simulations. The numerical results show that p‐MANET reduces power consumption and routing latency and performs well in extending lifetime with a small neighbor discovery time. Copyright © 2011 John Wiley & Sons, Ltd.
On the solution-space geometry of random constraint satisfaction problems

For a number of random constraint satisfaction problems, such as random k-SAT and random graph/hypergraph coloring, there are very good estimates of the largest constraint density for which solutions exist. Yet, all known polynomial-time algorithms for these problems fail to find solutions even at much lower densities. To understand the origin of this gap we study how the structure of the space of solutions evolves in such problems as constraints are added. In particular, we prove that much before solutions disappear, they organize into an exponential number of clusters, each of which is relatively small and far apart from all other clusters. Moreover, inside each cluster most variables are frozen, i.e., take only one value. The existence of such frozen variables gives a satisfying intuitive explanation for the failure of the polynomial-time algorithms analyzed so far. At the same time, our results establish rigorously one of the two main hypotheses underlying Survey Propagation, a heuristic introduced by physicists in recent years that appears to perform extraordinarily well on random constraint satisfaction problems.
Improving Spam Detection Based on Structural Similarity

We propose a new spam detection algorithm that uses structural relationships between senders and recipients of email as the basis for spam detection. A unifying representation of users and receivers in the vectorial space of their contacts is constructed, that leads to a natural definition of similarity between them. This similarity is then used to group email senders and recipients into clusters. Historical information about the messages sent and received by the clusters is obtained by forwarding messages to an auxiliary spam detection algorithm and this information is used to reclassify messages. In the framework proposed, our algorithm aims at correcting misclassifications from an auxiliary algorithm. A simulation is performed based on actual data collected from an SMTP server from a large University. We show that our approach is able reduce false positives, produced by the auxiliary classification algorithm, up to about 60%.
GPU-Based Volume Segmentation

Volume segmentation is an important part of any medical image analysis framework used for diagnoses, treatment planning and biomedical modelling and visualisation. Recent advances in modern graphics hardware have made it possible to perform general purpose computing on the GPU. In this paper we survey and analyse the current state-of-the-art of GPU-based volume segmentation algorithms. Limitations of GPU-based implementations due to the architecture of graphics hardware are discussed and guidelines for developing new GPU-based algorithms are given. We conclude with the GPU-based implementation of a hybrid volume segmentation method which makes use of the latest GPU features such as frame buffer objects. We present a framework for the interactive control and visualisation of the segmentation process and we demonstrate the speed advantage achieved by using the GPU.
Quantum bounded query complexity

We combine the classical notions and techniques for bounded query classes with those developed in quantum computing. We give strong evidence that quantum queries to an oracle in the class NP does indeed reduce the query, complexity of decision problems. Under traditional complexity assumptions, we obtain an exponential speedup between the quantum and the classical query complexity of function classes. For decision problems and function classes we obtain the following results: P/sub /spl par///sup NP[2k]//spl sube/EQP/sub /spl par///sup NP[k]/; P/sub /spl par///sup NP[2k+1-2]//spl sube/EQP/sup NP[k]/; FP/sub /spl par///sup NP[2k=1-2]//spl sube/FEQP/sup NP[2k]/; FP/sub /spl par///sup NP/spl sube/FEQP(NP[Olog n)]/. For sets A that are many-one complete for PSPACE or EXP we show that Fp/sup A//spl sube/FEQP/sup A[1]/. Sets A that are many-one complete for PP have the property that FP/sub /spl par///sup A//spl sube/FEQP/sup A[1]/. In general we prove that for any set A there is a set X such that FP/sup A//spl sube/FEQP/sup X[1]/, establishing that no set is superterse in the quantum setting.
Basic Principles of Colloid Science

Chapter 1: What are Colloids? Chapter 2: Why are Colloidal Dispersions Stable? Basic Principles Chapter 3: Why are Colloidal Dispersions Stable? Interparticle Forces Chapter 4: How are Colloidal Dispersions Prepared? Chapter 5: What is the Role of Surface Chemistry? Surface Tension and Adsorption Chapter 6: Some Important Properties of Colloids I Kinetic Properties Chapter 7: Some Important Properties of Colloids II Scattering Radiation Chapter 8: Some Important Properties of Colloids III Rheology Chapter 9: How are Colloidal Dispersions Destroyed? I Aggregation Processes Chapter 10: How are Colloidal Dispersions Destroyed? II Coalescence and Particle Growth Chapter 11: Association Colloids and Self-Assembly Systems Chapter 12: Thin Films, Foams and Emulsions Chapter 13: Gels Chapter 14: The Industrial Importance of Colloids Chapter 15: The Future of Colloid Science
Structure and Ostension in the Interpretation of Discourse Deixis

Abstract This paper examines demonstrative pronouns used as deictics to refer to the interpretation of one or more clauses. Although this usage is frowned upon in style manuals (for example Strunk and White (1959) state that “This. The pronoun this, referring to the complete sense of a preceding sentence or clause, cannot always carry the load and so may produce an imprecise statement”), it is nevertheless very common in written text. Handling this usage poses a problem for Natural Language Understanding systems. The solution I propose is based on distinguishing between what can be pointed to and what can be referred to by virtue of pointing. I argue that a restricted set of discourse segments yield what such demonstrative pronouns can point to in the discourse model and a restricted set of what Nunberg (1979) has called referring functions yield what they can refer to by virtue of that pointing.
An Ultraviolet-Excess Optical Candidate for the Luminous Globular Cluster X-Ray Source in NGC 1851

The intense, bursting X-ray source in the globular cluster NGC 1851 was one of the first cluster sources discovered, but has remained optically unidentified for 25 years. We report here on results from Hubble Space Telescope WFPC2 multicolor images in NGC 1851. Our high spatial resolution images resolve ~200 objects in the 3'' radius Einstein X-ray error circle, 40 times as many as in previous ground-based work. A color-magnitude diagram of the cluster clearly reveals a markedly UV-excess object with B ~ 21, (U - B) ~ -0.9, only 2'' from the X-ray position. The UV-excess candidate is 0. ″12 distant from a second, unremarkable star that is 0.5 mag brighter in B; thus ground-based studies of this field are probably impractical. Three other UV-excess objects are also present among the ~16,000 objects in the surveyed region of the cluster, leaving a ~5% probability that a UV-excess object has fallen in the X-ray error circle by chance. No variability of the candidate is seen in these data, although a more complete study is required. If this object is in fact the counterpart of the X-ray source, previous inferences that some globular cluster X-ray sources are optically subluminous with respect to low-mass X-ray binaries in the field are now strengthened.
Maximizing system lifetime in wireless sensor networks

Maximizing system lifetime in battery-powered wireless sensor networks with power aware topology control protocols and routing protocols has received intensive research. In the past, this problem has been mostly studied from the indirect perspective of energy conservation. Although this leads to solutions that help extend network lifetime, energy conservation is not the same problem as network lifetime maximization. Some researchers have formally studied network lifetime maximization problems, based on the assumption that energy is only consumed by packet transmission. However, it is well known that in many cases energy is significantly consumed during idle periods and overhearing. In this paper, we try to present a survey and formal analysis of a variety of network lifetime maximization problems in different energy consumption models. In particular, we identify different energy consumption models, define a variety of fundamental network lifetime maximization problems in individual energy consumption models, and formally analyze their complexities. Polynomial time algorithms are presented for tractable problems, and NP-hardness proofs are presented for intractable problems.
The Immunological Identification of Brain Proteins on Cellulose Nitrate in Human Demyelinating Disease

Abstract: An immunological technique has been employed to identify proteins, separated in polyacrylamide gels, which show changes in brain samples from cases of multiple sclerosis and subacute sclerosing panencephalitis. Sodium dodecylsulphate‐treated proteins in particulate and soluble fractions were separated in polyacrylamide slab gels, transferred electrophoretically onto cellulose nitrate sheets, incubated with specific antisera and visualized by an immunoperoxidase method. Protein bands showing changes were identified using antisera raised against the myelin basic and Wolfgram proteins, the neurofilament triplet proteins, tubulin and glial fibrillary acidic protein. In addition to the loss of myelin proteins, decreases in the neurofilament proteins and in tubulin were seen in both multiple sclerosis and subacute sclerosing panencephalitis samples. The distribution of glial fibrillary acidic protein polypeptides in the particulate and soluble fractions of plaque samples appeared to vary according to the degree of fibrosis. Changes in the levels of the myelin‐associated glycoprotein, the lower molecular weight component of the Wolfgram protein, albumin and immunoglobulin G, none of which were visualized by protein staining, were also seen. This immunological technique has allowed a closer examination of changes occurring in brain protein spectra in multiple sclerosis and subacute sclerosing panencephalitis.