Miracle-dz/UniFAR-FTUs huggingface dataset:
Disaggregating Identities to Study Ethnic Conflict

Abstract Research has shown that competition over resources, overlapping identity cleavages, and weak state institutions constitute important causal determinants of ethnic violence. Critical in identifying those factors have been research innovations that treat ethnic identities as political tools rather than primordial divisions. Thus, theoretical developments that consider ethnicity to be fluid and constructed, data sets that account for the actual political importance of group identities, and new methods that add geographic and psychological information to ethnic labels all constitute important breakthroughs. Yet, the study of ethnic conflict remains constrained by a tendency to treat as constant the political interests of actors in very different identity contexts.
Recurrent Neural Network Regularization

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.
Sentiment Strength Prediction Using Auxiliary Features

With an increasingly large amount of sentimental information embedded in online documents, sentiment analysis is quite valuable to product recommendation, opinion summarization, and so forth. Different from most works on identifying documents' qualitative affective information, this research focuses on the measurement of users' intensity over each sentimental category. Affect indicates positive or negative sentiment, while cognition includes certainty and tentative. Thus, our research can help bridge the cognitive and affective gaps between users and documents. The contributions of this study are twofold: (i) we proposed a neural network-based framework to sentiment strength prediction by convolving hybrid vectors, and (ii) we considered words jointly with a set of linguistic features for enhancing model robustness and adaptiveness. By exploiting the auxiliary features of sentiments from the corpus, the proposed model did not rely on well-established lexicons, and showed its robustness over sparse words. Experiments on six corpora validated the effectiveness of our sentiment strength prediction method.
The Attentional Routing Circuit: A Neural Model of Attentional Modulation and Control of Functional Connectivity

Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data – behavioural, psychophysical, neurophysiological, and neuroanatomical – and that provide experimentally testable predictions. Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects. The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis. The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scaleand position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects. To evaluate the model’s biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation.
The Beaconless Node Velocity-based Stable Path Routing Protocol for Unicasting and Multicasting in Mobile Ad hoc Networks

The high-level contribution of this paper is the development of a beaconless node-velocity-based stable path on-demand unicast routing protocol (NVSP) and its extension for multicasting (NVSP-M) in mobile ad hoc networks (MANETs). During the propagation of the Route-Request (RREQ) messages, every forwarding node includes its current node velocity information in the RREQs. The bottleneck velocity of a path is the maximum of the velocity of an intermediate node on the path. The destination chooses the path with the smallest bottleneck velocity and sends a Route-Reply (RREP) packet on the chosen path. NVSP-M connects the source to each of the multicast receivers through paths with the smallest bottleneck velocity. The lifetime of NVSP routes is 25-35% and 55-75% more than that of the routes chosen by the minimum-hop based Dynamic Source Routing (DSR) protocol in networks of low and high density respectively. The lifetime of routes chosen by NVSP is 60-70% of the route lifetime incurred by the Flow-Oriented Routing Protocol (FORP), the routing protocol observed to determine the sequence of most stable routes. On the other hand, the end-to-end delay per data packet and the energy consumed per packet incurred by NVSP are significantly lower than that of FORP and are lower or equal to that incurred for DSR. We compare the performance of NVSP-M with that of the Bandwidth Efficient Multicast Routing Protocol (BEMRP). Simulation results illustrate NVSP-M yields multicast trees with longer lifetime and lower hop count per source-receiver path; whereas BEMRP yields the minimum number of links per tree.
The Pure Characteristics Demand Model

In this article, we consider a class of discrete choice models in which consumers care about a finite set of product characteristics. These models have been used extensively in the theoretical literature on product differentiation and the goal of this article is to translate them into a form that is useful for empirical work. Most recent econometric applications of discrete choice models implicitly let the dimension of the characteristic space increase with the number of products (they have "tastes for products"). The two models have different theoretical properties, and these, in turn, can have quite pronounced implications for both substitution patterns and for the welfare impacts of changes in the number and characteristics of the goods marketed. After developing those properties, we provide alternative algorithms for estimating the parameters of the pure characteristic model and compare their properties to those of the algorithm for estimating the model with tastes for products. We conclude with a series of Monte Carlo results. These are designed to illustrate: (i) the computational properties of the alternative algorithms for computing the pure characteristic model, and (ii) the differences in the implications of the pure characteristic model from the models with tastes for products. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
Using TMS to study the role of the articulatory motor system in speech perception

Background: The ability to communicate using speech is a remarkable skill, which requires precise coordination of articulatory movements and decoding of complex acoustic signals. According to the traditional view, speech production and perception rely on motor and auditory brain areas, respectively. However, there is growing evidence that auditory-motor circuits support both speech production and perception. Aims: In this article we provide a review of how transcranial magnetic stimulation (TMS) has been used to investigate the excitability of the motor system during listening to speech and the contribution of the motor system to performance in various speech perception tasks. We also discuss how TMS can be used in combination with brain-imaging techniques to study interactions between motor and auditory systems during speech perception. Main contribution: TMS has proven to be a powerful tool to investigate the role of the articulatory motor system in speech perception. Conclusions: TMS studies have provided support for the view that the motor structures that control the movements of the articulators contribute not only to speech production but also to speech perception.
A parameterless mixture model for large margin classification

This paper presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the dataset from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometric vectors, analogous to SVM's support vectors are obtained in order to yield the final large margin solution from a mixture model approach. A preliminary experimental study with five real-world benchmarks showed that the method is promising.
A Bayesian Approach to Target Tracking with Finite-Set-Valued Observations

This paper presents a Bayes recursion for tracking a target that generates multiple measurements with state dependent sensor field of view and clutter. Our Bayesian formulation is mathematically well-founded due to our use of a mathematically consistent likelihood function derived from random finite set theory. A particle implementation of the proposed filter is given. Under linear Gaussian assumptions, an exact closed form solution to the proposed recursion is derived, and efficient implementations are given.
One after the other: Effects of sequence patterns of breached and overfulfilled obligations

ABSTRACT To date, the study of psychological contracts has primarily centred on the question how retrospective evaluations of the psychological contract impact employee attitudes and behaviours, and/or focus on individual coping processes in explaining responses to breached or overfulfilled obligations. In this study, we aim to assess the extent to which sequences of breached and overfulfilled obligations impact job satisfaction and citizenship behaviour intentions. By integrating psychological contract research and theories on cognitive information processing, we formulate competing hypotheses on how sequences of breached and/or overfulfilled obligations lead to patterns of job satisfaction and citizenship behaviour intentions. These competing hypotheses were tested using a vignette study and an experiment. A Bayesian approach was used to test these pattern hypotheses directly against each other. The results show that breached obligations have an immediate negative impact on our outcome variables. Moreover, sequentially breached obligations lead to a continuous decline of job satisfaction and citizenship behaviour intentions. Overfulfilled obligations do little to compensate this negative impact. Implications for theory and practice are discussed.
Two forms of electrical resonance at theta frequencies, generated by M‐current, h‐current and persistent Na+ current in rat hippocampal pyramidal cells

Coherent network oscillations in the brain are correlated with different behavioural states. Intrinsic resonance properties of neurons provide a basis for such oscillations. In the hippocampus, CA1 pyramidal neurons show resonance at theta (θ) frequencies (2‐7 Hz). To study the mechanisms underlying θ‐resonance, we performed whole‐cell recordings from CA1 pyramidal cells (n= 73) in rat hippocampal slices. Oscillating current injections at different frequencies (ZAP protocol), revealed clear resonance with peak impedance at 2‐5 Hz at ≈33 °C (increasing to ≈7 Hz at ≈38 °C). The θ‐resonance showed a U‐shaped voltage dependence, being strong at subthreshold, depolarized (≈‐60 mV) and hyperpolarized (≈‐80 mV) potentials, but weaker near the resting potential (‐72 mV). Voltage clamp experiments revealed three non‐inactivating currents operating in the subthresold voltage range: (1) M‐current (IM), which activated positive to ‐65 mV and was blocked by the M/KCNQ channel blocker XE991 (10 μm); (2) h‐current (Ih), which activated negative to ‐65 mV and was blocked by the h/HCN channel blocker ZD7288 (10 μm); and (3) a persistent Na+ current (INaP), which activated positive to ‐65 mV and was blocked by tetrodotoxin (TTX, 1 μm). In current clamp, XE991 or TTX suppressed the resonance at depolarized, but not hyperpolarized membrane potentials, whereas ZD7288 abolished the resonance only at hyperpolarized potentials. We conclude that these cells show two forms of θ‐resonance: ‘M‐resonance’ generated by the M‐current and persistent Na+ current in depolarized cells, and ‘H‐resonance’ generated by the h‐current in hyperpolarized cells. Computer simulations supported this interpretation. These results suggest a novel function for M/KCNQ channels in the brain: to facilitate neuronal resonance and network oscillations in cortical neurons, thus providing a basis for an oscillation‐based neural code.
A Decentralized VPN Service over Generalized Mobile Ad-Hoc Networks

We present a decentralized VPN service that can be built over generalized mobile ad-hoc networks (Generalized MANETs), in which topologies can be represented as a time-varying directed multigraph. We address wireless ad-hoc networks and overlay ad-hoc networks as instances of Generalized MANETs. We first propose an architecture to operate on various kinds of networks through a single set of operations. Then, we design and implement a decentralized VPN service on the proposed architecture. Through the development and operation of a prototype system we implemented, we found that the proposed architecture makes the VPN service applicable to each instance of Generalized MANETs, and that the VPN service makes it possible for unmodified applications to operate on the networks.
Economic models for tiered network services

Tiered network services have been prevalent in current networking industry. The term tiered service means that the network operator only provides a small set of tiers (levels)—which offer progressively higher levels of service—to the customers each of whom will be mapped to one of the given tiers. In this thesis we focus on the economic issues for tiered network service. We first formulate the problem of selecting service tiers from three perspectives: one that considers the users interests only, one that considers only the service provider’s interests, and one that considers both simultaneously. We consider the solution to this problem under two cases: (1) the discrete case, i.e., each user demand is known to the service provider; (2) the stochastic case, i.e., the service provider only knows the probability distribution of the user demands. For both cases, we present accurate and efficient algorithms based on dynamic programming. After finding the set of (near-) optimal service tiers, we then employ game-theoretic techniques to find an optimal price for each service tier that strikes a balance between the conflicting objectives of users and service provider. This work provides a theoretical framework for reasoning about and pricing Internet tiered services, as well as a practical toolset for network providers to develop customized menus of service offerings. We further consider some advanced economic topics in tiered network service. We notice that some network services may tend to be elastic, i.e., the users may value a given service differently and show different willingness to pay for the service. In this thesis, we assume that users are partitioned into some distinct classes. We develop an optimal algorithm to select jointly the set of service tiers and their prices so as to maximize the provider profits. Our research shows that introducing multiple tiers can be an effective market segmentation strategy that may lead to an increase in profits. Another advanced topic in tiered network service is service bundling, which means the network providers combine several services together and sell them as a single package at a lower price than that if the services are sold separately. Based on tools from microeconomics and utility theory, we developed an efficient method to find tiered structures for bundles of network services with the objective of maximizing provider profits under user constraints.
Evolutionary Approaches to Solve an Integrated Lot Scheduling Problem in the Soft Drink Industry

This paper proposes two evolutionary approaches as procedures to solve the synchronized and integrated two-level lot-sizing and scheduling problem (SITLSP). This problem can be found in some industrial settings, mainly soft drink companies, where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. The first approach to solve the SITLSP is a multi-population genetic algorithm (GA) with a hierarchical ternary tree structure for populations. The second approach is a memetic algorithm (MA) that extends the GA approach through the inclusion of a local search procedure. The computational study reported reveals that those methods are an effective alternative to solve real-world instances of the SITLSP.
Imaging Approaches to Parkinson Disease

Parkinson disease (PD) is associated with nigral degeneration and striatal dopamine deficiency. Demonstrating midbrain structural abnormalities with transcranial sonography or diffusion-weighted MRI or showing striatal dopamine terminal dysfunction with PET or SPECT supports the diagnosis and rationalizes the use of dopaminergic medications. In atypical PD variants, transcranial sonography can detect striatal hyperechogenicity, and diffusion-weighted imaging can detect increased putamen water diffusion, whereas 18F-FDG PET reveals reduced lentiform nucleus glucose metabolism. PET and SPECT can detect changes in striatal dopamine levels after levodopa administration and relate these to motor responses. Loss of cortical dopaminergic and cholinergic function is present in demented PD and, on occasion, amyloid deposits can be detected. Loss of cardiac sympathetic innervation can be sensitively detected in PD with 18F-dopamine PET or 123I-metaiodobenzylguanidine SPECT. Finally, PET can detect widespread brain inflammation in PD. This review discusses the role of structural and functional imaging for diagnosing and managing different parkinsonian syndromes.
Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis

The bacterial community structure of a fluidized-bed reactor fed by vinasses (wine distillation waste) was analyzed. After PCR amplification, four small-subunit (SSU) rDNA clone libraries of Bacteria, Archaea, Procarya, and Eucarya populations were established. The community structure was determined by operational taxonomic unit (OTU) phylogenetic analyses of 579 partial rDNA sequences (about 500 bp long). A total of 146 OTUs were found, comprising 133, 6, and 7 from the Bacteria, Archaea, and Eucarya domains, respectively. A total of 117 bacterial OTU were affiliated with major phyla: low-G+C gram-positive bacteria, Cytophaga-Flexibacter-Bacteroides, Proteobacteria, high-G+C gram-positive bacteria, and Spirochaetes, where the clone distribution was 34, 26, 17, 6, and 4%, respectively. The other 16 bacterial OTUs represent 13% of the clones. They were either affiliated with narrow phyla such as Planctomyces-Chlamydia, green nonsulfur bacteria, or Synergistes, or deeply branched on the phylogenetic tree. A large number of bacterial OTUs are not closely related to any other hitherto determined sequences. The most frequent bacterial OTUs represents less than 5% of the total bacterial SSU rDNA sequences. However, the 20 more frequent bacterial OTUs describe at least 50% of these sequences. Three of the six Archaea OTUs correspond to 95% of the Archaea population and are very similar to already known methanogenic species: Methanosarcina barkeri, Methanosarcina frisius, and Methanobacterium formicicum. In contrast, the three other Archaea OTUs are unusual and are related to thermophilic microorganisms such as Crenarchaea or Thermoplasma spp. Five percent of the sequences analyzed were chimeras and were removed from the analysis.
THE INTERIORS OF GIANT PLANETS: Models and Outstanding Questions

▪ Abstract We know that giant planets played a crucial role in the making of our Solar System. The discovery of giant planets orbiting other stars is a formidable opportunity to learn more about these objects, what their composition is, how various processes influence their structure and evolution, and most importantly how they form. Jupiter, Saturn, Uranus, and Neptune can be studied in detail, mostly from close spacecraft flybys. We can infer that they are all enriched in heavy elements compared to the Sun, with the relative global enrichments increasing with distance to the Sun. We can also infer that they possess dense cores of varied masses. The intercomparison of presently characterized extrasolar giant planets shows that they are also mainly made of hydrogen and helium, but that they either have significantly different amounts of heavy elements, have had different orbital evolutions, or both. Hence, many questions remain and need to be answered to make significant progress on the origins of planets.
TERAPI BERPIKIR POSITIF DAN TEKANAN DARAH PADA PASIEN HIPERTENSI

Hipertensi merupakan satu penyakit dengan angka kejadian tertinggi di dunia. Terapi berpikir positif adalah Suatu jenis terapi kognitif yang digunakan untuk memodifikasi perilaku. Tujuan penelitian ini adalah untuk mengetahui pengaruh terapi berpikir positif terhadap tekanan darah pada pasien hipertensi di Puskesmas Welahan I Jepara.metode yang digunakan dalam penelitian ini adalah desain penelitian pre experimental one group pretest-posttest. Populasinya sebanyak 291 orang dengan sampel 30 responden. Teknik pengambilan sampel non probability sampling berupa purposive sampling. Hasilnya adalah p value = 0,000 (<0,05) yang berarti ada pengaruh terapi berpikir positif terhadap tekanan darah pada pasien hipertensi di Puskesmas Welahan I Kabupaten Jepara.