Miracle-dz/UniFAR-FTUs huggingface dataset:
Low complexity and hardware-friendly spectral modular multiplication

The Schönhage-Strassen Algorithm (SSA) is an asymptotically fast multiplication algorithm with the complexity of O(l log l log log l) where l is the operand size. It outperforms other multiplication algorithms when l is large enough. One possible usage of such long integer multiplication is for cryptography. Innovated from SSA, the Interleaved Spectral Montgomery Modular Multiplication (ISM3) algorithm is proposed to accelerate the modular multiplication. ISM3 algorithm primarily interleaves the Montgomery modular multiplication algorithm between time and spectral (frequency) domain. We show that the tasks in each step of the proposed algorithm have little data dependency, and hence, extremely suitable for hardware implementation. We present the parallel ISM3 architecture and implement it on Xilinx Virtex-II and Virtex-6 FPGAs. Experimental results show that our 3838-bit ISM3 is faster than the previous Montgomery multiplier. Moreover, our design can complete a 7678-bit modular multiplication in 3398 cycles in 17.98 μs on a Virtex-6 device.
Regulation of bat echolocation pulse acoustics by striatal dopamine

SUMMARY The ability to control the bandwidth, amplitude and duration of echolocation pulses is a crucial aspect of echolocation performance but few details are known about the neural mechanisms underlying the control of these voice parameters in any mammal. The basal ganglia (BG) are a suite of forebrain nuclei centrally involved in sensory-motor control and are characterized by their dependence on dopamine. We hypothesized that pharmacological manipulation of brain dopamine levels could reveal how BG circuits might influence the acoustic structure of bat echolocation pulses. A single intraperitoneal injection of a low dose (5 mg kg–1) of the neurotoxin 1-methyl-4-phenylpyridine (MPTP), which selectively targets dopamine-producing cells of the substantia nigra, produced a rapid degradation in pulse acoustic structure and eliminated the bat's ability to make compensatory changes in pulse amplitude in response to background noise, i.e. the Lombard response. However, high-performance liquid chromatography (HPLC) measurements of striatal dopamine concentrations revealed that the main effect of MPTP was a fourfold increase rather than the predicted decrease in striatal dopamine levels. After first using autoradiographic methods to confirm the presence and location of D1- and D2-type dopamine receptors in the bat striatum, systemic injections of receptor subtype-specific agonists showed that MPTP's effects on pulse acoustics were mimicked by a D2-type dopamine receptor agonist (Quinpirole) but not by a D1-type dopamine receptor agonist (SKF82958). The results suggest that BG circuits have the capacity to influence echolocation pulse acoustics, particularly via D2-type dopamine receptor-mediated pathways, and may therefore represent an important mechanism for vocal control in bats.
LOW COMPLEXITY PTS FOR PAPR REDUCTION IN OFDM

Partial transmit sequence (PTS) is a promising technique for reducing the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. In Conventional PTS, multiple candidate signals are generated and the optimal signal with the lowest PAPR is selected for transmission. One drawback of Conventional PTS is that parts of the candidate signals are strongly correlated so as to degrade the PAPR reduction performance. The other is the high complexity. In this paper, A new PTS is proposed in this paper to deal with these drawbacks of C-PTS. The candidates can be generated through cyclically Left and Right shifting each subblock sequence in time domain and combining them. Theoretical analysis and simulation results shows that the new scheme can achieve higher PAPR reduction.
Polish WordNet on a Shoestring

A project to create a Polish WordNet is under way. Rather than localise the English WordNet, we are constructing the lexical network from scratch, in two phases. First, we have established the linguistic principles, among them a list of semantic relations with detailed diagnostic tests. We have also implemented a client software tool that records the lexicographers’ decisions in a central database. A core WordNet, populated with around 10,000 most frequent lexemes in the IPI PAN Corpus, will be a fully functional resource for Natural Language Processing in Polish. In the second phase, the enhanced software tool will detect candidate semantic relations in a much larger corpus, based on statistical methods of grouping words by semantic similarity. Lexicographers will review and approve such candidate relations.
Inferring Semantic Roles Using Sub-Categorization Frames and Maximum Entropy Model

In this paper, we propose an approach for inferring semantic role using sub-categorization frames and maximum entropy model. Our approach aims to use the sub-categorization information of the verb to label the mandatory arguments of the verb in various possible ways. The ambiguity between the assignment of roles to mandatory arguments is resolved using the maximum entropy model. The unlabelled mandatory arguments and the optional arguments are labelled directly using the maximum entropy model such that their labels are not one among the frame elements of the sub-categorization frame used. Maximum entropy model is preferred because of its novel approach of smoothing. Using this approach, we obtained an F-measure of 68.14% on the development set of the data provided for the CONLL-2005 shared task. We show that this approach performs well in comparison to an approach which uses only the maximum entropy model.
Evaluation of a Compact Hybrid Brain-Computer Interface System

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.
Sound Source Tracking and Formation using Normalized Cuts

The goal of computational auditory scene analysis (CASA) is to create computer systems that can take as input a mixture of sounds and form packages of acoustic evidence such that each package most likely has arisen from a single sound source. We formulate sound source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. It measures both the total dissimilarity between the different groups as well as the total similarity within groups. We describe how this formulation can be used with sinusoidal modeling, a common technique for sound analysis, manipulation and synthesis. Several examples showing the potential of this approach are provided.
Simple iteration-optimal distributed optimization

We propose a consensus-based distributed optimization algorithm for minimizing separable convex objectives. Each node only knows one component of the objective function, and so the nodes must coordinate in order to find a global minimizer. The proposed algorithm has an error rate which is no more than O(1/√T) after T iterations, matching the best possible rate. To achieve this, the algorithm requires multiple rounds of consensus per iteration, where the number of consensus rounds depends on the structure of the underlying communication topology through the spectral gap. Consequently, the amount of computation required by the proposed approach is less that of distributed optimization methods in the literature, while the total amount of communication is not increased.
Identification of Comparable Argument-Head Relations in Parallel Corpora

We present the machine learning framework that we are developing, in order to support explorative search for non-trivial linguistic configurations in low-density languages (languages with no or few NLP tools). The approach exploits advanced existing analysis tools for high-density languages and word-aligned multi-parallel corpora to bridge across languages. The goal is to find a methodology that minimizes the amount of human expert intervention needed, while producing high-quality search and annotation tools. One of the main challenges is the susceptibility of a complex system combining various automatic analysis components to hard-to-control noise from a number of sources. We present systematic experiments investigating to what degree the noise issue can be overcome by (i) exploiting more than one perspective on the target language data by considering multiple translations in the parallel corpus, and (ii) using minimally supervised learning techniques such as co-training and self-training to take advantage of a larger pool of data for generalization. We observe that while (i) does help in the training individual machine learning models, a cyclic bootstrapping process seems to suffer too much from noise. A preliminary conclusion is that in a practical approach, one has to rely on a higher degree of supervision or on noise detection heuristics.
Exploring Contexts and Actions in Knowledge Processes

This paper presents an approach to automatically discovering the contexts that a knowledge worker is working in and the different types of actions the knowledge worker is executing across contexts. We formulate the two scenarios as two feature selection variations of a clustering problem. The proposed approach is evaluated using realworld data that capture knowledge worker workflow. We evaluate context discovery on gold-standard contexts and action discovery on predictive power of process models, constructed from those actions. The obtained results suggest that we are able to successfully enrich the obtained events log data with additional context and action metadata.
A Pair of Reciprocally Inhibitory Histaminergic Sensory Neurons Are Activated within the Same Phase of Ingestive Motor Programs inAplysia

Previous studies have shown that each buccal ganglion inAplysia contains two B52 neurons, one in each hemiganglion. We now show that there are two B52 neurons in a single buccal hemiganglion and four cells in an animal. We also show that the B52 neurons are histamine-immunoreactive and use reverse phase HPLC to show that the histamine-immunoreactive substance is authentic histamine. Previous studies have shown that the B52 neurons make numerous inhibitory synaptic connections with neurons active during the radula closing/retraction phase of ingestive motor programs. A computational model of the Aplysia feeding central pattern generator has, therefore, suggested that the B52 neurons play a role in terminating closing/retraction. Consistent with this idea we show that both B52 neurons fire at the beginning of radula opening/protraction. We also show that both B52 neurons are sensory neurons. They are depolarized when a flap of connective tissue adjacent to the buccal commissural arch is stretched. During ingestive feeding this is likely to occur at the peak of closing/retraction as opening/protraction begins. In the course of this study we compare the two ipsilateral B52 neurons and show that these cells are virtually indistinguishable; e.g., they use a common neurotransmitter, make the same synaptic connections, and are both sensory as well as premotor neurons. Nevertheless we show that the B52 neurons are reciprocally inhibitory. Our results, therefore, strikingly confirm theoretical predictions made by others that neurons that inhibit each other will not necessarily participate in antagonistic phases of behavior.
Theoretical Basics for Developing Business Information Systems for Jointly Managing Uncertainty, Communication, and Trust in Supply Chains

REQUIRED) Uncertainty is a major problem in any supply chain. Inventory and excess capacities used to cope with uncertainty are significant cost drivers. Advanced information systems have been employed to support information exchange along the supply chain and were successful in synchronizing supply and demand and downsizing inventories. However, close cooperation requires mutual trust as a basis. Current information systems do not specifically address this issue. This paper outlines the relation of uncertainty to communication and trust and sketches approaches for information systems to enable integrated process-oriented cooperation and trust management.
Growth , Output Gaps and Economic Reforms : Evidence from New Zealand by

We present two alternative, but complementary, approaches to evaluating New Zealand’s economic performance over a period which included numerous economic reforms. Using both index-number and econometric techniques, we decompose nominal GDP growth for New Zealand into components of interest (price changes, productivity growth and changes in factor utilisation) for 1967-1996. We use a similar approach to decompose estimates of the output gap over the same period. We also estimate the impact of terms-of-trade and productivity changes on aggregate welfare. Our results indicate that changes in domestic prices account for about four-fifths of the growth in New Zealand’s nominal GDP, while capital accumulation and employment growth are the most important factors determining real-output growth. Variations of the domestic price level around its long-run trend contributed most heavily to changes in the (nominal) output gap. We find that the contributions to aggregate welfare of productivity growth and changes in the terms of trade were no different in the preand post-reform years.
A single case study of a family-centred intervention with a young girl with cerebral palsy who is a multimodal communicator.

BACKGROUND This paper describes the impact of a family-centred intervention that used video to enhance communication in a young girl with cerebral palsy. This single case study describes how the video-based intervention worked in the context of multimodal communication, which included high-tech augmentative and alternative communication (AAC) device use. This paper includes the family's perspective of the video intervention and they describe the impact of it on their family. METHODS This single case study was based on the premise that the video interaction guidance intervention would increase attentiveness between participants during communication. It tests a hypothesis that eye gaze is a fundamental prerequisite for all communicative initiatives, regardless of modality in the child. Multimodality is described as the range of communicative behaviours used by the child and these are coded as AAC communication, vocalizations (intelligible and unintelligible), sign communication, nodding and pointing. Change was analysed over time with multiple testing both pre and post intervention. Data were analysed within INTERACT, a computer software to analyse behaviourally observed data. Behaviours were analysed for frequency and duration, contingency and co-occurrence. RESULTS Results indicated increased duration of mother's and girl's eye gaze, increased frequency and duration in AAC communication by the girl and significant change in frequency [χ(2) (5, n = 1) = 13.25, P < 0.05] and duration [χ(2) (5, n = 1) = 12.57, P < 0.05] of the girl's multimodal communicative behaviours. Contingency and co-occurrence analysis indicated that mother's eye gaze followed by AAC communication was the most prominent change between the pre- and post-intervention assessments. CONCLUSIONS There was a trend for increased eye gaze in both mum and girl and AAC communication in the girl following the video intervention. The family's perspective concurs with the results.
A Computer Aided Diagnosis System for Lung Cancer based on Statistical and Machine Learning Techniques

lung Cancer is believed to be among the primary factors for death across the world. Within this paper, statistical and machine learning techniques are employed to build a computer aided diagnosis system for the purpose of classifying lung cancer. The system includes preprocessing phase, feature extraction phase, feature selection phase and classification phase. For feature extraction, wavelet transform is used and for feature selection, two-step statistical techniques are applied. Clustering-K-nearest-neighbor classifier is employed for classification. The Japanese Society of Radiological Technology’s standard dataset of lung cancer has been utilized to evaluate the system. The dataset has 154 nodule regions (abnormal) - where 100 are malignant and 54 are benign - and 92 non-nodule regions (normal). An Accuracy of 99.15% and 98.70 % for classification have been achieved for normal versus abnormal and benign versus malignant respectively, this substantiate the capabilities of the approach presented in this paper.
PROBE-GK: Predictive robust estimation using generalized kernels

Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.
An Intelligent Text Extraction and Navigation System

We present sppc, a high-performance system for intelligent text extraction and navigation from German free text documents. The main purpose of sppc is to extract as much linguistic structure as possible for performing domain-specific processing. sppc consists of a set of domain-independent shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. All extracted information is represented uniformly in one data structure (called the text chart) in a highly compact and linked form in order to support indexing and navigation through the set of solutions. German text processing includes (among others) compound processing, high performance named entity recognition and chunk parsing based on a divide-and-conquer strategy. sppc has a good performance (6160 words per second on standard PC environments) and high linguistic coverage.
Energy-Price-Driven Request Dispatching for Cloud Data Centers

Cloud services make use of data center resources so that hosted applications can utilize them as needed. To offer a large amount of computational resources, cloud service providers manage tens of geographically distributed data centers. Since each data center is made up of hundreds of thousands of physical machines, energy consumption is a major concern for cloud service providers. The electric cost imposes significant financial overheads on those companies and pushes up the price for the cloud users. This paper presents an energy-price-driven request dispatcher that forwards client requests to data centers in an electric-cost-saving way. In our technique, mapping nodes, which are used as authoritative DNS servers, forward client requests to data centers in which the electric price is relatively lower. We additionally develop a policy that gradually shifts client requests to electrically cheaper data centers, taking into account application latency requirements and data center loads. Our simulation-based results show that our technique can reduce electric cost by 15% more than randomly dispatching client requests.
Fixing Tournaments for Kings, Chokers, and More

We study the tournament fixing problem (TFP), which asks whether a tournament organizer can rig a single-elimination (SE) tournament such that their favorite player wins, simply by adjusting the initial seeding. Prior results give two perspectives of TFP: on the one hand, deciding whether an arbitrary player can win any SE tournament is known to be NP-complete; on the other hand, there are a number of known conditions, under which a player is guaranteed to win some SE tournament. We extend and connect both these lines of work. We show that for a number of structured variants of the problem, where our player is seemingly strong, deciding whether the player can win any tournament is still NP-complete. Dual to this hardness result, we characterize a new set of sufficient conditions for a player to win a tournament. Further, we give an improved exact algorithm for deciding whether a player can win a tournament.
Simulated Annealing: A Heuristic for Parallel Stochastic Optimization

An overview of physical annealing and simulated annealing methods is presented. The target audience includes computational analysts and software engineers who are considering simulated annealing for the solution of a combinatorical optimization problem. Several important implementation issues are discussed including decompositions for parallel programming. Special emphasis is given to the often ignored perspective of thermodynam-
STABILITY RESULTS FOR NETWORKED CONTROL SYSTEMS SUBJECT TO PACKET DROPOUTS

Abstract Stability and performance of networked control systems has been a recent area of interest in the control literature. The inclusion of a shared communication network between plant and controller inevitably leads to occasional random data loss. We provide novel results relating to the probability of such a system being globally asymptotically stable or input to state stable.
Towards improving web service repositories through semantic web techniques

The success of the Web services technology has brought topicsas software reuse and discovery once again on the agenda of software engineers. While there are several efforts towards automating Web service discovery and composition, many developers still search for services via online Web service repositories and then combine them manually. However, from our analysis of these repositories, it yields that, unlike traditional software libraries, they rely on little metadata to support service discovery. We believe that the major cause is the difficulty of automatically deriving metadata that would describe rapidly changing Web service collections. In this paper, we discuss the major shortcomings of state of the art Web service repositories and, as a solution, we report on ongoing work and ideas on how to use techniques developed in the context of the Semantic Web (ontology learning, mapping, metadata based presentation) to improve the current situation.