Miracle-dz/UniFAR-FTUs huggingface dataset:
Bargaining for bribes under uncertainty

A corrupt transaction is often the result of bargaining between the parties involved. This paper models bribery as a double auction where a private citizen and a public o¢ cial strategically interact as the potential buyer and the potential seller of a corrupt service. Individuals di¤er in the internalized moral cost generated by corruption, and may have only imperfect information on others moral cost, i.e. their "corruptibility". This paper investigates the role that imperfect information with respect to the "corruptibility" of one's potential partner in corruption plays in his or her propensity to engage in bribery, and, consequently, the equilibrium level of corruption in a society. We find that corruption is lower when potential bribers and potential bribees are uncertain regarding each other's "corruptibility". This paper provides therefore theoretical support to anti-corruption strategies, such as staff rotation in public offices, aimed at decreasing the social closeness of bribers and bribees.
Estimating Oxygen Uptake During Nonsteady-State Activities and Transitions Using Wearable Sensors

In this paper, we present a method to estimate oxygen uptake (VO2) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state VO2. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from 22 participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state VO2. Indirect calorimetry was used in parallel to obtain VO2 reference. VO2 estimation error during transitions between sedentary, household and walking activities could be reduced by 16% on average using the proposed approach, compared to state of the art methods.
Particle Swarm Optimization: Iteration Strategies Revisited

Particle swarm optimization (PSO) is an iterative algorithm, where particle positions and best positions are updated per iteration. The order in which particle positions and best positions are updated is referred to in this paper as an iteration strategy. Two main iteration strategies exist for PSO, namely synchronous updates and asynchronous updates. A number of studies have discussed the advantages and disadvantages of these iteration strategies. Most of these studies indicated that asynchronous updates are better than synchronous updates with respect to accuracy of the solutions obtained and the speed at which swarms converge. This study provides evidence from an extensive empirical analysis that current opinions that asynchronous updates result in faster convergence and more accurate results are not true.
Characteristic Analysis and Control of a Hybrid Excitation Linear Eddy Current Brake

In this paper, a novel hybrid excitation linear eddy current brake is presented as a braking system for high-speed road and rail vehicles. The presence of the permanent magnets (PMs), whose flux lines in the primary core are oppositely directed with respect to the flux lines by the excitation windings, has the effect of mitigating the saturation of the iron in the teeth of the primary core. This allows the brake to be fed with more intense currents, improving the braking force. First, using the magnetic equivalent circuit method and the layer theory approach, the analytical model of the hybrid excitation linear eddy current brake was developed, which can account for the saturation effects occurring in the iron parts. The saturation effects make the design and control of eddy current brakes more difficult. Second, the relationship between the braking force characteristics and the design parameters were analyzed to provide useful information to the designers of eddy current brakes. Then, the controller of the hybrid excitation linear eddy current brake was designed to control the amplitude of the braking force. Finally, experimental measurements were conducted to verify the validity of the theoretical analysis.
Differential diagnosis of dystonia

Dystonia is a movement disorder characterized by sustained muscle contractions causing twisting and repetitive movements and abnormal postures. Diagnosing dystonia may be difficult, because of variability of dystonia presentation, uncertain recognition of the specific clinical signs, wide etiological spectrum, and coexistence of other movement disorders. The major difficulties in the diagnostic assessment of primary and non‐primary dystonia derive from its confusion with other movement disorders or with a psychogenic disorder. The clinical heterogeneity of dystonia and some examples of misdiagnosis are reviewed here. The movement disorders that can be most commonly taken for dystonia are tremor, Parkinson’s disease, myoclonus, chorea, and tics. Given the occurrence of confounding factors, along with specific genetic and laboratory test, it is of great importance to apply a specific algorithm to recognize the clinical signs of dystonia.
A New Reduced-Complexity ML Joint Detection Algorithm Based Sphere Decoder for LS-CDMA System

A new reduced-complexity joint detection algorithm based on sphere decoder for LS-CDMA system is proposed in this paper. This algorithm can approach ML performance in polynomial complexity. Special zero correlation window property of LS code can reduce complexity of this algorithm greatly. Simulation indicates it is more insensitive to the choice of initial radius. Thus, by making the choice of radius sufficiently large, the ML solution is guaranteed with low complexity, even for large constellations. We then extend this joint detection algorithm to MIMO case and compare it with MMSE, ZF multicode detectors. Numerical results show performance of this algorithm is good even at very high speed such as 160 Km/h, this give us some insights on developing high efficient practical joint detection algorithm for further LS-CDMA system
Creative Transformations : How Generative and Evolutionary Music can Inform Music HCI

James McDermott EvoDesignOpt Group, CSAIL, MIT. 32-D540, Stata Center, Vassar St., Cambridge, MA 02139, USA. http://www.skynet. ie/∼jmmcd jmmcd@csail.mit.edu (Contact author) Dylan Sherry EvoDesignOpt Group, CSAIL, MIT. 32-D540, Stata Center, Vassar St., Cambridge, MA 02139, USA. dsherry@mit.edu Una-May O’Reilly EvoDesignOpt Group, CSAIL, MIT. 32-D534, Stata Center, Vassar St., Cambridge, MA 02139, USA. unamay@csail.mit.edu
Stationary random graphs with prescribed iid degrees on a spatial Poisson process

Let $[\mathcal{P}]$ be the points of a Poisson process on $R^d$ and $F$ a probability distribution with support on the non-negative integers. Models are formulated for generating translation invariant random graphs with vertex set $[\mathcal{P}]$ and iid vertex degrees with distribution $F$, and the length of the edges is analyzed. The main result is that finite mean for the total edge length per vertex is possible if and only if $F$ has finite moment of order $(d+1)/d$.
Effects of Intramolecular Distance between Amyloidogenic Domains on Amyloid Aggregation

Peptide/protein aggregation is implicated in many amyloid diseases. Some amyloidogenic peptides/proteins, such as those implicated in Alzheimer’s and Parkinson’s diseases, contain multiple amyloidogenic domains connected by “linker” sequences displaying high propensities to form turn structures. Recent studies have demonstrated the importance of physicochemical properties of each amino acid contained in the polypeptide sequences in amyloid aggregation. However, effects on aggregation related to the intramolecular distance between amyloidogenic domains, which may be determined by a linker length, have yet to be examined. In the study presented here, we created peptides containing two copies of KFFE, a simple four-residue amyloidogenic domain, connected by GS-rich linker sequences with different lengths yet similar physicochemical properties. Our experimental results indicate that aggregation occurred most rapidly when KFFE domains were connected by a linker of an intermediate length. Our experimental findings were consistent with estimated entropic contribution of a linker length toward formation of (partially) structured intermediates on the aggregation pathway. Moreover, inclusion of a relatively short linker was found to inhibit formation of aggregates with mature fibril morphology. When the results are assimilated, our study demonstrates that intramolecular distance between amyloidogenic domains is an important yet overlooked factor affecting amyloid aggregation.
Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models

The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies with a substantial GPS data set offer insight into the design properties of the proposed framework and algorithms, demonstrating the effectiveness and efficiency of travel cost inferencing.
Has Market Discipline on Banks Improved after the Dodd - Frank Act?

We investigate whether or not market discipline on banking firms changed after the Dodd–Frank Wall Street Reform and Consumer Protection Act (DFA) of 2010. If market discipline is improved, we should see a lower discount for size on yield spreads, particularly for banks identified as too-big-to-fail (TBTF) or systemically important (SIFI). Using secondary market subordinated debt transactions we find that the size discount is reduced by 47% and TBTF discount is reduced by 94% after the DFA. The DFA has been effective in reducing, but not in eliminating the size and TBTF discounts on yield spreads. Market discipline of banks appears to have improved further after the rating criteria changes by Moody’s.
Transliteration Using a Phrase-Based Statistical Machine Translation System to Re-Score the Output of a Joint Multigram Model

The system presented in this paper uses a combination of two techniques to directly transliterate from grapheme to grapheme. The technique makes no language specific assumptions, uses no dictionaries or explicit phonetic information; the process transforms sequences of tokens in the source language directly into to sequences of tokens in the target. All the language pairs in our experiments were transliterated by applying this technique in a single unified manner. The approach we take is that of hypothesis rescoring to integrate the models of two state-of-the-art techniques: phrase-based statistical machine translation (SMT), and a joint multigram model. The joint multigram model was used to generate an n-best list of transliteration hypotheses that were re-scored using the models of the phrase-based SMT system. The both of the models' scores for each hypothesis were linearly interpolated to produce a final hypothesis score that was used to re-rank the hypotheses. In our experiments on development data, the combined system was able to outperform both of its component systems substantially.
A state-space approach to semi-blind signal detection in fast frequency-selective fading MIMO channels

In this paper, we propose a semi-blind state-space based receiver that jointly performs channel estimation and data detection in MIMO systems subject to fast frequency-selective fading. To accomplish these two tasks, we first define state equations representing the dynamics of channel and transmitted signals. Then, we obtain the state vector by concatenating the transmitted signals and the channel coefficients. This choice of state vector leads to a nonlinear observation equation and hence to the use of the extended Kalman filter (EKF) to estimate the states variables. We then develop the EKF and show that the proposed receiver is a generalization of many similar receivers for SISO channels. We also develop a reduced complexity version of the proposed algorithm. Simulation results show the performance gains of the proposed receiver when compared to other commonly used receivers.
In Silico Genome Comparison and Distribution Analysis of Simple Sequences Repeats in Cassava

We conducted a SSRs density analysis in different cassava genomic regions. The information obtained was useful to establish comparisons between cassava's SSRs genomic distribution and those of poplar, flax, and Jatropha. In general, cassava has a low SSR density (~50 SSRs/Mbp) and has a high proportion of pentanucleotides, (24,2 SSRs/Mbp). It was found that coding sequences have 15,5 SSRs/Mbp, introns have 82,3 SSRs/Mbp, 5′ UTRs have 196,1 SSRs/Mbp, and 3′ UTRs have 50,5 SSRs/Mbp. Through motif analysis of cassava's genome SSRs, the most abundant motif was AT/AT while in intron sequences and UTRs regions it was AG/CT. In addition, in coding sequences the motif AAG/CTT was also found to occur most frequently; in fact, it is the third most used codon in cassava. Sequences containing SSRs were classified according to their functional annotation of Gene Ontology categories. The identified SSRs here may be a valuable addition for genetic mapping and future studies in phylogenetic analyses and genomic evolution.
Comparison of two methods for assessment of Microvolt T-wave alternans: Discrete vs continuous T-wave analysis

Micro T-wave alternans (MTWA) is a risk marker for life threatening ventricular tachyarrhythmia. Classically, MTWA is assessed by quantification of beat-by-beat T-wave amplitude alternation. This method requires accurate determination of T-wave peaks (discrete method) to create a sequence of T-wave amplitude series, which undergoes spectral analysis using the fast Fourier transform (FFT) for analysis. The need of precise measurements of T-wave amplitudes turns this method sensitive to noise. To overcome this limitation, a new method was developed, based on Hilbert Transform of the T-wave morphology series (continuous method). To perform this task, a 300 ms window containing the T-wave of every beat was isolated, and concatenated to form an artificial and continuous signal with T-waves. The Hilbert Transform was then applied to calculate the envelope of this signal. The alter-nans was detected over the envelope FFT, as a peak on the frequency that corresponded to half of the main signal frequency. Both methods were tested in 50 ECG signals of Physionet T-Wave Alternans Database, 31 synthesized and 19 real world signals. The comparisons of the methods were carried out by linear correlation test, Wilcoxon test, and Bland-Altman charts, in channels 1 and 2. There was no significant differences between both methods in all tests, either synthesized or real world signals. The novel continuous method to quantify micro T-wave alternans based on T-wave morphology is feasible, accurate and reproducible, and have potential clinical application.
Organizational processes and ontological (in)security: Torture, the CIA and the United States

This paper builds upon previous work that has sought to use ontological security to understand problematic and violent state practices, and how they relate to the securitizing of identity. Yet like much (although not all) work which has utilized it in International Relations theory, the application of ontological security theory (OST) to state ‘drives’ has provided only a superficial unpacking of ‘the state’. Further, while OST scholars have examined environmental or background conditions of ‘late modernity’, and how these conditions facilitate anxiety and uncertainty for agents, the content of such factors can be further explicated by placing OST in conversation with one particular systemic account. Alongside ‘the state’ and ‘late modernity’, the paper therefore explores several complementary sites shaping the ontological security seeking process of, within and around states. The paper reads the 2000s re-embrace of torture by the United States by examining ontological security alongside: (1) the structural level via Laura Sjoberg’s ‘gender–hierarchical’ argument; (2) the routinized organizational processes (via Graham Allison) of the US intelligence community and specifically the Central Intelligence Agency; and (3) the narrated interplay between public opinion and elite discourses.
Neural mechanisms and functional signficance of peri-saccadic response modulation

OF THE THESIS . ii Table of Contents . iv List of Illustrations . vii Introduction . 1 Chapter 1. 1.1 Visual Stability . 2 1.2 Saccadic Suppression . 3 Saccadic suppression in a signal-processing framework . 7 Chapter 2. 2.1 Introduction . 7 2.2 Methods . 9 2.2.1 Participants . 9 2.2.2 Stimuli . 9 2.2.3 Procedure . 12 2.2.4 Analysis . 13 2.3 Results . 14 2.3. 1 Simulation . 14 2.3. 2 Experiments . 19 2.4 Discussion . 26 2.4. 1 Signal-Detection Mechanisms . 27
The Effect of Exogenous Inputs and Defiant Agents on Opinion Dynamics With Local and Global Interactions

Most of the conventional models for opinion dynamics mainly account for a fully local influence, where myopic agents decide their actions after they interact with other agents that are adjacent to them. For example, in the case of social interactions, this includes family, friends, and other immediate strong social ties. The model proposed in this paper embodies a global influence as well where by global we mean that each node also observes a sample of the average behavior of the entire population; e.g., in the social example, people observe other people on the streets, subway, and other social venues. We consider the case where nodes have dichotomous states; examples of applications include elections with two major parties, whether or not to adopt a new technology or product, and any yes/no opinion such as in voting on a referendum. The dynamics of states on a network with arbitrary degree distribution are studied. For a given initial condition, we find the probability to reach consensus on each state and the expected time reach to consensus. To model mass media, the effect of an exogenous bias on the average orientation of the system is investigated. To do so, we add an external field to the model that favors one of the states over the other. This field interferes with the regular decision process of each node and creates a constant probability to lean towards one of the states. We solve for the average state of the system as a function of time for given initial conditions. Then anti-conformists (stubborn nodes who never revise their states) are added to the network, in an effort to circumvent the external bias. We find necessary conditions on the number of these defiant nodes required to cancel the effect of the external bias. Our analysis is based on a mean field approximation of the agent opinions.
Citizen eyewitness images and audience engagement in crisis coverage

Amateur imagery has become an important component of news coverage of distant crisis events, and it plays a decisive role in shaping how audiences respond to crises. In this article, we discuss how the factors of authenticity, affectivity, and ethics play a role in the ways in which citizen images engage or disengage the distant audience. The article is based on 17 focus group interviews in Sweden and Finland that centred on a selection of visual news coverage of the Arab Spring in Syria and Libya – landmark news events in the use of citizen eyewitness images in the Nordic countries. The results indicate that citizen imagery is indeed a potential tool with strongly engaging characteristics, especially in terms of its authenticity and to some degree also its affectivity. However, disengagement may also result, especially due to the interpreted deficiencies in terms of ethics.
New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.
Pseudorecombinants between cloned DNAs of two isolates of cassava latent virus

Summary Infective clones of the Nigerian isolate of cassava latent virus (CLV) have been obtained. The apparent molecular weight of the capsid protein of this isolate is slightly higher than that produced in plants infected with cloned DNAs of the Kenyan isolate of CLV. Pseudorecombinant experiments using heterologous combinations of cloned DNAs have confirmed that the physical properties of the capsid protein are encoded on DNA 1 and at least some determinants of symptom induction are also located on this DNA. Comparison between the nucleotide sequences of the open reading frames encoding the two capsid proteins shows several nucleotide differences which affect the amino acid composition but which do not significantly alter the potential molecular weight of the product. In vitro translation of poly(A)+ RNAs shows that the differences in electrophoretic mobility are not due to differences in host-directed post-translational processing.
Reliability of prognostic models in medicine : current issues and available results

This paper deals with the problem of the general usefulness of results coming from prognostic models in medicine. It reports a collection of available theoretical results on the sample size of the studies taken from Computational Learning theory and basic statistics. It is concluded that a general guideline for publishing Data Mining results in medicine can be derived; moreover it is argued that only few studies published in the literature followed a procedure that may really enable the reader to judge if the results obtained possess a general validity.