Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment

The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn. The approach is a pixel-wise regression model based on convolutional neural networks. To minimize the dependency on the conditions in which the images are taken, the effect of two image normalization techniques is also studied. Results indicate that the model performs satisfactorily on average, presenting low mean absolute and squared errors and high structural similarity index. While no significant differences are found when changing the normalization of the images, the performance is affected by their quality. This suggests that changes in the intensity of the images do not alter the relevant information about the wound, whereas changes in brightness, contrast and sharpness do.
A new rf CMOS mixer with a high performance in 0.18u technology

In this paper, a new highly linear radio-frequency CMOS mixer is presented. The new mixer is derived from the basic Gilbert multiplier. It achieves an outstanding linearity since the new topology does not contain any tail current branches. A CMOS transistor pair is instead applied to the four cross coupled commutating transistors. Simulations of the extracted layout results show a high linearity improvement over the basic Gilbert mixer, with IIP3 of 31 dBm in 0.8u and 20dBm in 0.18u. Simulations also shows 6dB improvement in the mixer's conversion gain in 0.18u over 0.8u, with 3.2mW power consumption. The new mixer's test chip layout and its extracted simulation characteristics in both technologies are presented.
Voluntary Servitude Reconsidered: Radical Politics and the Problem of Self-Domination

3 Introduction . 3 The Powerlessness of Power . 3 Another Politics. 5 The Anarchist Subject . 7 Psychoanalysis and Passionate Attachments . 8 Ego Identification . 9 A Micro-Politics of Liberty . 11 Conclusion: A Politics of Refusal . 12 References. 13
Drawing graphs: methods and models

Graph Drawing and Its Applications. - Drawing Planar Graphs. - Drawing Trees, Series-Parallel Digraphs, and Lattices. - Drawing on Physical Analogies. - Layered Drawings of Digraphs. - Orthogonal Graph Drawing. - 3D Graph Drawing. - Drawing Clusters and Hierarchies. - Dynamic Graph Drawing. - Map Labeling with Application to Graph Drawing. - Software Packages.
Digital Architectures for UWB Beamforming Using 2D IIR Spatio-Temporal Frequency-Planar Filters

A design method and an FPGA-based prototype implementation of massively parallel systolic-array VLSI architectures for 2nd-order and 3rd-order frequency-planar beam plane-wave filters are proposed. Frequency-planar beamforming enables highly-directional UWB RF beams at low computational complexity compared to digital phased-array feed techniques. The array factors of the proposed realizations are simulated and both high-directional selectivity and UWB performance are demonstrated. The proposed architectures operate using 2's complement finite precision digital arithmetic. The real-time throughput is maximized using look-ahead optimization applied locally to each processor in the proposed massively-parallel realization of the filter. From sensitivity theory, it is shown that 15 and 19-bit precision for filter coefficients results in better than 3% error for 2nd- and 3rd-order beam filters. Folding together with Ktimes multiplexing is applied to the proposed beam architectures such that throughput can be traded for K-fold lower complexity for realizing the 2-D fan filter banks. Prototype FPGA circuit implementations of these filters are proposed using a Virtex 6 xc6vsx475t-2ff1759 device. The FPGA-prototyped architectures are evaluated using area (A), critical path delay (T), and metrics AT and AT2. The L2 error energy is used as a metric for evaluating fixed-point noise levels and the accuracy of the finite precision digital arithmetic circuits.
Representation discovery in non-parametric reinforcement learning

Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. Most problems of interest do not satisfy this requirement in their native space, but can be represented in such a way that they do. In this thesis, we show that the ideal representation is one that maps points directly to their values. Existing representation discovery algorithms that have been used in parametric reinforcement learning settings do not, in general, produce such a representation. We go on to present Fit-Improving Iterative Representation Adjustment (FIIRA), a novel framework for function approximation and representation discovery, which interleaves steps of value estimation and representation adjustment to increase the expressive power of a given regression scheme. We then show that FIIRA creates representations that correlate highly with value, giving kernel regression the power to represent discontinuous functions. Finally, we extend kernel-based reinforcement learning to use FIIRA and show that this results in performance improvements on three benchmark problems: Mountain-Car, Acrobot, and PinBall. Thesis Supervisor: Leslie Kaelbling Title: Professor Thesis Supervisor: Tomis Lozano-P6rez Title: Professor
Takealook: personalizing information presentation according to user's interest space

In this paper, we present a system called Takealook, which personalizes information presentation according to individual interests and knowledge of the user. The basic idea is that the system infers a user's interests and personalizes information presentation by processing the user's operation history and the interest space. The user's interest space is structured from the presenter's individual conceptual space and the user's operation history, which is acquired through question-answer interactions between the user and the system. We employ a method similar to collaborative filtering for improving interest spaces.
6LoWPAN: The Wireless Embedded Internet

"It is stunningly thorough and takes readers meticulously through the design, conguration and operation of IPv6-based, low-power, potentially mobile radio-based networking. " Vint Cerf, Vice President and Chief Internet Evangelist, Google This book provides a complete overview of IPv6 over Low Power Wireless Area Network (6LoWPAN) technology In this book, the authors provide an overview of the 6LoWPAN family of standards, architecture, and related wireless and Internet technology. Starting with an overview of the IPv6 Internet of Things, readers are offered an insight into how these technologies fit together into a complete architecture. The 6LoWPAN format and related standards are then covered in detail. In addition, the authors discuss the building and operation of 6LoWPAN networks, including bootstrapping, routing, security, Internet ingration, mobility and application protocols. Furthermore, implementation aspects of 6LoWPAN are covered. Key Features: Demonstrates how the 6LoWPAN standard makes the latest Internet protocols available to even the most minimal embedded devices over low-rate wireless networks Provides an overview of the 6LoWPAN standard, architecture and related wireless and Internet technology, and explains the 6LoWPAN protocol format in detail Details operational topics such as bootstrapping, routing, security, Internet integration, mobility and application protocols Written by expert authors with vast experience in the field (industrial and academic) Includes an accompanying website containing tutorial slides, course material and open-source code with examples (http://6lowpan.net ) 6LoWPAN: The Wireless Embedded Internet is an invaluable reference for professionals working in fields such as telecommunications, control, and embedded systems. Advanced students and teachers in electrical engineering, information technology and computer science will also find this book useful.
A Consistent Neural Network Model for Doubly Constrained Spatial Movement Estimation

Despite its successful applications in mode choice modelling, the Neural Network (NN) Approach is often seen as a poor technique for trip distribution estimation. However, empirical results from this study show that the NN can also be used as a good trip distribution modelling tool, by using appropriate Training Algorithm (TA), which is the most critical property of NN. This study will describe the results of NN approach in work trip distribution estimation with three different TAs, namely Back Propagation (BP), Variable Learning Rate (VLR), and Levenberg-Marquardt (LM). The experiments were conducted 30 times for each TA. The results suggest that both BP and VLR models tend to have overestimated total trip numbers, while LM model generates the closest total trip number to the observed one, with less than five per cent difference. Statistical analysis also suggests that LM model is able to generate a consistent performance, while other models have a great fluctuation. Then, the findings from this study are expected can be used as an important consideration for using NN approach as an alternative simple and robust travel demand modelling tool. Key Word: Neural Network, Training Algorithm, Work trip Distribution
Computational treatment of superlatives

The use of gradable adjectives and adverbs represents an important means of expressing comparison in English. The grammatical forms of comparatives and superlatives are used to express explicit orderings between objects with respect to the degree to which they possess some gradable property. While comparatives are commonly used to compare two entities (e.g., “The blue whale is larger than an African elephant”), superlatives such as “The blue whale is the largest mammal” are used to express a comparison between a target entity (here, the blue whale) and its comparison set (the set of mammals), with the target ranked higher or lower on a scale of comparison than members of the comparison set. Superlatives thus highlight the uniqueness of the target with respect to its comparison set. Although superlatives are frequently found in natural language, with the exception of recent work by (Bos and Nissim, 2006) and (Jindal and Liu, 2006b), they have not yet been investigated within a computational framework. And within the framework of theoretical linguistics, studies of superlatives have mainly focused on semantic properties that may only rarely occur in natural language (Szabolsci (1986), Heim (1999)). My PhD research aims to pave the way for a comprehensive computational treatment of superlatives. The initial question I am addressing is that of automatically extracting useful information about the target entity, its comparison set and their relationship from superlative constructions. One of the central claims of the thesis is that no unified computational treatment of superlatives is possible because of their great semantic complexity and the variety of syntactic structures in which they occur. I propose a classification of superlative surface forms, and initially focus on so-called “ISA superlatives”, which make explicit the IS-A relation that holds between target and comparison set. They are suitable for a computational approach because both their target and comparison set are usually explicitly realised in the text. I also aim to show that the findings of this thesis are of potential benefit for NLP applications such as Question Answering, Natural Language Generation, Ontology Learning, and Sentiment Analysis/Opinion Mining. In particular, I investigate the use of the “Superlative Relation Extractor“ implemented in this project in the area of Sentiment Analysis/Opinion Mining, and claim that a superlative analysis of the sort presented in this thesis, when applied to product evaluations and recommendations, can provide just the kind of information that Opinion Mining aims to identify.
Data Reconciliation in P2P Collaborative Applications

In this paper, we address data reconciliation in peer-to-peer (P2P) collaborative applications. We propose P2P-LTR (Logging and Timestamping for Reconciliation) which provides P2P logging and timestamping services for P2P reconciliation over a distributed hash table (DHT). While updating at collaborating peers, updates are timestamped and stored in a highly available P2P log. During reconciliation, these updates are retrieved in total order to enforce eventual consistency. In this paper, we first give an overview of P2P-LTR with its model and its main procedures. We then present our prototype used to validate P2P-LTR. To demonstrate P2P-LTR, we propose several scenarios that test our solutions and measure performance. In particular, we demonstrate how P2P-LTR handles the dynamic behavior of peers with respect to the DHT.
Formative evaluation of the CASUS authoring system for problem-based learning

CASUS is an authoring system, which should enable physicians to produce problem-based computer learning programs with minimal technical effort and give them sound instructional support. The theoretical background of CASUS are constructivist approaches to learning and instruction, which deal mainly with the question, how to design problem-based learning environments. The paper presents the constructivist concept of CASUS and results of a forma-tive evaluation. Four authors were observed and interviewed while developing a learning case with CASUS. The evaluation pursued mainly the question of which difficulties arise while producing a case with CASUS and which measures are necessary in order to improve the authoring system. Results show that with regard to technical aspects physicians can produce learning cases even without any special prior knowledge. There were far more problems with instructional aspects of the production of cases. Most of these difficulties were very specific to the content of the cases. Therefore, it seems that an appropriate way to design learning cases with high quality, is a cooperation between some authors with different expertise.
Log-MAP decoding of turbo codes and turbo trellis-coded modulation using piecewise-linear approximations of the max* operator

A novel approach for approximating the max operator, used in the Log-MAP decoding algorithm, is presented. In this approximation it is not necessary to use any additional correction term, as previous approximation methods require. Several decoding algorithms are obtained for both turbo and turbo trellis-coded modulation (TTCM) codes with optimal and near-optimal bit error rate (BER) performance depending on a single parameter, namely the number of piecewise-linear (PWL) approximation terms used. The decoding complexity is estimated, showing the practical implementation advantages of the proposed method against the Log-MAP algorithm.
A New Retrieval Model Based on TextTiling for Document Similarity Search

Document similarity search is to find documents similar to a given query document and return a ranked list of similar documents to users, which is widely used in many text and web systems, such as digital library, search engine,etc. Traditional retrieval models, including the Okapi's BM25 model and the Smart's vector space model with length normalization, could handle this problem to some extent by taking the query document as a long query. In practice,the Cosine measure is considered as the best model for document similarity search because of its good ability to measure similarity between two documents. In this paper, the quantitative performances of the above models are compared using experiments. Because the Cosine measure is not able to reflect the structural similarity between documents, a new retrieval model based on TextTiling is proposed in the paper. The proposed model takes into account the subtopic structures of documents. It first splits the documents into text segments with TextTiling and calculates the similarities for different pairs of text segments in the documents. Lastly the overall similarity between the documents is returned by combining the similarities of different pairs of text segments with optimal matching method. Experiments are performed and results show:1) the popular retrieval models (the Okapi's BM25 model and the Smart's vector space model with length normalization)do not perform well for document similarity search; 2) the proposed model based on TextTiling is effective and outperforms other models, including the Cosine measure; 3) the methods for the three components in the proposed model are validated to be appropriately employed.
AN ARGUMENT FOR SOFT LAYERING OF PROTOCOLS

This thesis is about the efficiency of protocol layering. It examines the technique of protocol layering in an abstract way and finds two major sources of inefficiency in protocol implementations which are caused by the imposition on them of a layered structure. The conventional approach to making layered protocol implementations run efficiently -- for avoiding the sources of inefficiency discussed herein -- are all independent of the protocol specification, and thus all decrease the value of the protocol specification as a guide for implementing protocols. We introduce a new means of avoiding the problems of layered protocol implementations which operates within the domain of the protocol specification. We allow an increase in the flow of state information between the layers of a layered protocol implementation in a very controlled manner, so as to decrease the modularity of the protocol architecture as little as possible. The increased flow of information is specified in the protocol specification as a model of all the layered protocols that use the protocol being specified, called the "usage mode. " Since our approach decreases the rigidity of the layered structure without entirely eliminating it, we coin the term "Soft Layering" for the approach.
Komunikasi Partisipatif Masyarakat Industri dalam Mendukung Branding Kota Madiun

Industrial society in madiun city contribute in supporting of Madiun city branding. This proved by the participate position in implementation and utilization of results. Madiun industrial society show the competitive value to global society that usually earn achievement from the exhibition that helds from many various events . This competitive value is suitable with creation of Madiun city branding as 'Kota Gadis' which combinate from three education aspect s. The form of communication participation in supporting branding madiun city is heteroglasia,dialogis, poliponi, dan karnaval
The electroencephalogram as a biometric

This paper examines the effectiveness electroencephalogram (EEG) as a biometric identification of individual subjects in a pool of 40 normal subjects. The EEG's second order statistics are computed using autoregressive models of various order. The coefficients in these models are then evaluated for their biometric potential. Discriminant functions applied to the model coefficients are used to examine the degree to which the subjects in the data pool can be identified. The results indicate that the EEG has significant biometric potential. In this data pool, 100% of subjects are correctly classified when all data is used, and over 80% when the functions are computed from half the data and then applied to the remaining.
DIRECT VOLUME RENDERING VIA 3D TEXTURES

The advent of very fast texture mapping hardware in modern graphics workstations has warranted research into rendering techniques that use texture mapping to full advantage. We have developed a new and easy to implement method for direct volume rendering that produces high-quality images at speeds approaching two orders of magnitude faster than existing techniques, on workstations with hardware support for three-dimensional texture maps. A rectilinear data set is converted into a three-dimensional texture map containing color and opacity information. In the rendering phase, the texture map is then applied to a stack of parallel planes, which effectively cut the texture into many slices. The slices are composited to form an image of the original data set. This paper describes the theory and implementation of this technique.
A novel SVM multi-class classifier based on pairwise coupling

In this paper, a novel algorithm is proposed to tackle multi-class classification problems. For a K-class classification task, an array of K optimal pairwise coupling classifiers (O-PWC) is constructed, each of which is optimal to the corresponding class and provides a reliable probability estimation for that class. The classification accuracy rate is improved while the computational cost does not increase too much. At the same time, a more accurate estimation of posterior probabilities for a given pattern can be acquired. This algorithm is applied to face recognition on an ORL face database. Experimental results reveal that our method is effective and efficient.
Algorithms in moderately exponential time

This thesis studies exponential time algorithms that give optimum solutions to optimization problems which are highly unlikely to be solvable to optimality in polynomial time. Such algorithms are designed and upper bounds on their running times are analyzed, for pro-blems from graph theory, constraint satisfaction, and computational biology. It is studied how restrictions on one computationally hard parameter affect the running time of algorithms for computing another parameter. Algorithms are obtained to compute minimum dominating sets, minimum bandwidth and topological bandwidth layouts, and other hard parameters, in time exponential only in the maximum number of leaves in a spanning tree of a graph. For sparse graphs, algorithms find minimum connected dominating sets in time exponential only in the optimum value. These algorithms are based on kernels of small size, meaning that arbitrary instances are polynomial-time compressible to equivalent instances whose size depends on the parameter alone. Lower and upper bounds on the number and size of certain combinatorial objects are proved. For minimal feedback vertex sets in tournaments, improved bounds on their number and a polynomial-space polynomial-delay algorithm for their enumeration are given. For subcubic planar graphs, fast algorithms finding large induced matchings are given. Fixed-parameter algorithms are presented for maximization problems whose optimum solution value grows as an unbounded function with the instance size. Polynomial kernels are obtained for all permutation constraint satisfaction problems with ternary constraints, and independent set problems in restricted graph classes, when these problems are parameterized above tight lower bounds on their solution value. Polynomial space algorithms with subexponential time requirement are obtained for the minimum triplet inconsistency problem in phylogenetics, and basically all bidimensional problems in sparse graphs such as graphs excluding a fixed minor.