Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Remittances in the Former Soviet Union: Methodological Complexities and Potential Solutions

Abstract Remittances in the former Soviet Union have increased rapidly over the past decade. In some countries of the former Soviet Union, remittances have reached staggering levels. For example, in Armenia, Kyrgyzstan, Moldova, Tajikistan, and Uzbekistan remittances now account for over 10% of GDP, with Tajikistan leading the pack with annual remittances of approximately 40% of GDP. Remittances in this group of economies now exceed foreign direct investment and foreign assistance. Because this rapid rise in remittances is a relatively recent trend and obtaining reliable data is difficult, this area of research has been underexplored. The aim of this paper is to provide a comprehensive review of existing remittance measurement methodologies. Moreover, we propose practical methods to adjust the Central Bank of Russia data to derive more accurate remittances estimates in selected countries of the former Soviet Union. These selected economies are major recipients of remittances among transition economies and account for as much as 10% of remittances worldwide. There have been attempts to provide this type of estimation in individual countries; however, there have been no studies, to our knowledge, that propose a general methodology for the region.
Vertical redistribution and horizontal inequity

Inequality of post-tax income among pre-tax equals is evaluated and aggregated to form a global index of horizontal inequity in the income tax. The vertical action of the tax is captured by its inequality effect on average between groups of pre-tax equals. Putting the two together, horizontal inequity measures loss of vertical performance. The identification problem, which has previously been thought insuperable, is addressed by a procedure validating the banding of income units into 'close equals' groups. The horizontal and vertical effects of a major Spanish income tax reform are evaluated. Lines for future investigation are suggested.
Synthesis with External Don ’ t-Cares Using Shannon Entropy and Craig Interpolation

Traditional digital circuit synthesis flows start from an HDL behavioral definition and assume that circuit functions are almost completely defined, making don’t-care conditions rare. However, recent design methodologies do not always satisfy these assumptions. For instance, third-party IP blocks used in a system-on-chip are often over-designed for the requirements at hand. By focusing only on the input combinations occurring in a specific application, one could resynthesize the system to greatly reduce its area and power consumption. Therefore we extend modern digital synthesis with a novel technique, called SWEDE, that makes use of extensive external don’t-cares. Experiments indicate that SWEDE scales to large ICs with half-million input vectors and handles practical cases well.
EFFECT OF RELAY LOCATION ON TWO-WAY DF AND AF RELAY IN LTE-A CELLULAR NETWORKS

Wireless multi-hop relay systems are important technologies in mobile communications. These systems ensure high-speed data transfer and extended coverage area at a low cost. This paper investigates the position of a relay problem for the maximum capacity enhancement of a twoway Relay Node (RN) to achieve an efficient and scalable Long Term Evolution-Advanced (LTE-A) wireless network design. The performance of a multi-hop relay network at an optimum position as well as a proposed half-duplex mode with two scenarios is investigated. A single RN for Amplify and Forward (AF) relay is proposed in the first scenario, whereas a Decode and Forward (DF) relay with a multi-hop LTE-A network is proposed in the second scenario. The performance of each scenario is analyzed. The optimum relay position that provides the maximum achievable rate with two-way performance is derived. The numerical results describe the optimum location of AF relay, which is 0.72 of the cell coverage radius, whereas the DF relay achieves a maximum bit rate when the relay location is at 0.52 from the cell coverage radius.
Test Schedule Optimization for Multicore SoCs: Handling Dynamic Voltage Scaling and Multiple Voltage Islands

In order to provide high performance with low power consumption, many multicore chips employ dynamic voltage scaling and voltage islands that operate at multiple power-supply voltage levels. Effective defect screening for such chips requires test applications at different operating voltages, which leads to higher test time and test cost compared to systems-on-a-chip (SoCs), which operate at only a single voltage level. We propose test scheduling techniques to minimize the testing time for multicore chips when each core is tested at multiple voltage levels and when it is tested for state retention when the core switches between two voltage levels. The proposed techniques include exact optimization based on integer linear programming and fast heuristic methods. Experimental results for two test-case SoCs from the industry highlight the effectiveness of the proposed method.
Teamwork in Real-World, Dynamic Environments.

Abstract : Flexible teamwork in real-world multi-agent domains is more than a union of agents simultaneous execution of individual plans, even if such execution pre-coordinated. Indeed, uncertainties in complex, dynamic domains often obstruct pre-planned coordination, with a resultant breakdown in teamwork. The central hypothesis in this paper is that for durable teamwork, agents should be provided explicit team plans and an underlying model of teamwork that explicitly outlines their commitments and responsibilities as participants in team activities. Such a model enables team members to flexibly reason about coordination activities. The underlying model we have provided is based on the joint intentions framework; although we present some key modifications to reflect the practical constraints in (some) real-world domains. This framework has been implemented in the context of a real-world synthetic environment for helicopter-combat simulation; some empirical results are presented.
Aggressive Maneuvering of Small Autonomous Helicopters: A Human-Centered Approach

Unmanned small autonomous helicopters can perform aggressive maneuvers that will be useful for operations in challenging conditions. This paper presents an analysis of the pilot’s execution of aggressive maneuvers from flight test data, collected on an instrumented small-scale acrobatic helicopter. A full-envelope nonlinear dynamic model of the helicopter was developed and validated for aggressive maneuvering. This model was used as part of the hardware-in-the-loop simulation environment to demonstrate intuitive control strategies that were determined from the analysis of flight data. This insight will be helpful in determining closed-loop strategies for autonomous aggressive maneuvering on MIT’s platform.
Diagnostic Contribution of Cardiac Magnetic Resonance in Patients with Acute Coronary Syndrome and Culprit-Free Angiograms

Background In spite of robust knowledge about underlying ischemic myocardial damage, acute coronary syndromes (ACS) with culprit-free angiograms raise diagnostic concerns. The present study aimed to evaluate the additional value of cardiac magnetic resonance (CMR) over commonly available non-CMR standard tests, for the differentiation of myocardial injury in patients with ACS and non-obstructed coronary arteries. Material/Methods Patients with ACS, elevated hs-TnT, and a culprit-free angiogram were prospectively enrolled into the study between January 2009 and July 2013. After initial evaluation with standard tests (ECG, echocardiography, hs-TnT) and provisional exclusion of acute myocardial infarction (AMI) in coronary angiogram, patients were referred for CMR with the suspicion of myocarditis or Takotsubo cardiomyopathy (TTC). According to the result of CMR, patients were reclassified as having myocarditis, AMI, TTC, or non-injured myocardium as assessed by late gadolinium enhancement. Results Out of 5110 patients admitted with ACS, 75 had normal coronary angiograms and entered the study; 69 of them (92%) were suspected for myocarditis and 6 (8%) for TTC. After CMR, 49 patients were finally diagnosed with myocarditis (65%), 3 with TTC (4%), 7 with AMI (9%), and 16 (21%) with non-injured myocardium. The provisional diagnosis was changed or excluded in 23 patients (31%), with a 9% rate of unrecognized AMI. Conclusions The study results suggest that the evaluation of patients with ACS and culprit-free angiogram should be complemented by a CMR examination, if available, because the initial work-up with non-CMR tests leads to a significant proportion of misdiagnosed AMI.
Bequest Taxation and r − g Emmanuel Farhi

We consider the role of the gap r − g between the return to capital r and the growth rate of the economy g in a political economy model of bequest taxation. Higher values of r− g lead to higher wealth inequality, resulting in higher and more progressive taxes on bequest. These conclusions hold only under specific but reasonable assumptions regarding the bequest motive, the relative magnitudes of bequest and labor income inequality and the nature of the political economy process.
Mitochondria Change Dynamics and Morphology during Grapevine Leaf Senescence

Leaf senescence is the last stage of development of an organ and is aimed to its ordered disassembly and nutrient reallocation. Whereas chlorophyll gradually degrades during senescence in leaves, mitochondria need to maintain active to sustain the energy demands of senescing cells. Here we analysed the motility and morphology of mitochondria in different stages of senescence in leaves of grapevine (Vitis vinifera), by stably expressing a GFP (green fluorescent protein) reporter targeted to these organelles. Results show that mitochondria were less dynamic and markedly changed morphology during senescence, passing from the elongated, branched structures found in mature leaves to enlarged and sparse organelles in senescent leaves. Progression of senescence in leaves was not synchronous, since changes in mitochondria from stomata were delayed. Mitochondrial morphology was also analysed in grapevine cell cultures. Mitochondria from cells at the end of their growth curve resembled those from senescing leaves, suggesting that cell cultures might represent a useful model system for senescence. Additionally, senescence-associated mitochondrial changes were observed in plants treated with high concentrations of cytokinins. Overall, morphology and dynamics of mitochondria might represent a reliable senescence marker for plant cells.
A distributed basis for analogical mapping

We are concerned with the practical feasibility of the neural basis of analogical mapping. All existing connectionist models of analogical mapping rely to some degree on local-ist representation (each concept or relation is represented by a dedicated unit/neuron). These localist solutions are implausible because they need too many units for human-level competence or require the dynamic re-wiring of networks on a sub-second time-scale. Analogical mapping can be formalised as finding an approximate isomorphism between graphs representing the source and target conceptual structures. Connectionist models of analogical mapping implement continuous heuristic processes for finding graph isomorphisms. We present a novel connectionist mechanism for finding graph isomorphisms that relies on distributed, high-dimensional representations of structure and mappings. Consequently, it does not suffer from the problems of the number of units scaling combinato-rially with the number of concepts or requiring dynamic network re-wiring.
WordNet Explorer : Applying Visualization Principles to Lexical Semantics

Interface designs for lexical databases in NLP have suffered from not following design principles developed in the information visualization research community. We present a design paradigm and show it can be used to generate visualizations which maximize the usability and utility of WordNet. The techniques can be gener-ally applied to other lexical databases used in NLP research.
Modeling the variability of EEG/MEG data through statistical machine learning

Brain neural activity generates electrical discharges, which manifest as electrical and magnetic potentials around the scalp. Those potentials can be registered with magnetoencephalography (MEG) and electroencephalography (EEG) devices. Data acquired by M/EEG is extremely difficult to work with due to the inherent complexity of underlying brain processes and low signal-to-noise ratio (SNR). Machine learning techniques have to be employed in order to reveal the underlying structure of the signal and to understand the brain state. This thesis explores a diverse range of machine learning techniques which model the structure of M/EEG data in order to decode the mental state. It focuses on measuring a subject's variability and on modeling intrasubject variability. We propose to measure subject variability with a spectral clustering setup. Further, we extend this approach to a unified classification framework based on Laplacian regularized support vector machine (SVM). We solve the issue of intrasubject variability by employing a model with latent variables (based on a latent SVM). Latent variables describe transformations that map samples into a comparable state. We focus mainly on intrasubject experiments to model temporal misalignment.
Knowledge-Based Out-of-Core Algorithms for Data Management in Visualization

Data management is the very first issue in handling very large datasets. Many existing out-of-core algorithms used in visualization are closely coupled with application-specific logic. This paper presents two knowledgebased out-of-core prefetching algorithms that do not use hard-coded rendering-related logic. They acquire the knowledge of the access history and patterns dynamically, and adapt their prefetching strategies accordingly. We have compared the algorithms with a demand-based algorithm, as well as a more domain-specific out-of-core algorithm. We carried out our evaluation in conjunction with an example application where rendering multiple point sets in a volume scene graph put a great strain on the rendering algorithm in terms of memory management. Our results have shown that the knowledge-based approach offers a better cache-hit to disk-access trade-off. This work demonstrates that it is possible to build an out-of-core prefetching algorithm without depending on rendering-related application-specific logic. The knowledge based approach has the advantage of being generic, efficient, flexible and self-adaptive.
Concerns with the SDT approach to causal conditional reasoning: a comment on Trippas, Handley, Verde, Roser, McNair, and Evans (2014)

Signal Detection Theory (SDT; Wickens, 2002) is a prominent measurement model that characterizes observed classification responses in terms of discriminability and response bias. In recent years, SDT has been increasingly applied within the psychology of reasoning (Rotello and Heit, 2009; Dube et al., 2010; Heit and Rotello, 2010, 2014; Trippas et al., 2013). SDT assumes that different stimulus types (e.g., valid and invalid syllogisms) are associated with different (presumably Gaussian) evidence or argument-strength distributions. Responses (e.g., “Valid” and “Invalid”) are produced by comparing the argument-strength of each syllogism with a set of established response criteria (Figure ​(Figure1A). 1A). The response profile associated to each stimulus type can be represented as a Receiver Operating Charateristics (ROC) function by plotting performance pairs (i.e., hits and false-alarms) along different response criteria, which Gaussian SDT predicts to be curvilinear (Figure ​(Figure1B1B). Figure 1 (A) A graphical representation of the SDT model for a syllogistic reasoning task. (B) ROC curve representing the cumulative probabilities for hypothetical pairs of hits and false-alarms (“valid” responses to valid and invalid syllogisms, . Trippas et al. (2014; henceforth THVRME) applied SDT to causal-conditional reasoning and make two points: (1) that SDT provides an informative characterization of data from a reasoning experiment with two orthogonal factors such as believability and argument validity; (2) that an inspection of the shape of causal-conditional ROCs provides insights on the suitability of normative theories with the consequence to consider affirmation and denial problems separately. The goal of this comment is to make two counterarguments: First, to point out that the SDT model is often unable to provide an informative characterization of data in designs as discussed by THVRME as it fails to unambiguously separate argument strength and response bias. THVRME's conclusion that “believability had no effect on accuracy […] but seemed to affect response bias” (p. 4) solely hinge on arbitrary assumptions. Second, that THVRME's reliance on ROC shape to justify a separation between affirmation and denial problems is unnecessary and misguided.
Spatio-temporal Motion Estimation & Segmentation in Layers: Robust Estimators, Markov Random Fields, Visual Grouping and Graph-based Optimization

In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimation of these motion models as well as the estimation of their support in the image domain. Our technique is based on an iterative process that integrates robust motion estimation, MRF-based formulation, combinatorial optimization and the use of visual as well as motion features to recover the parameters of the motion models as well as their support layers. Special handling of occlusions as well as adaptive techniques to detect new objects in the scene are also considered. Promising results demonstrate the potentials of our approach.
Intrahippocampal cannabinoid administration induces antinociceptive and amnestic response

In the present study, the possible role of a potent cannabinoid agonist, WIN55, 212-2 in the dorsal hippocampus on pain and memory performance has been evaluated. Animals were cannulated in CA1 region of the hippocampus using sterotaxic apparatus. Ten days after recovery, animals were trained in passive avoidance learning (PAL), and immediately received different doses of WIN 55212-2 (0.1, 0.25 and 0.5µg/rat), and were tested 24 h after the training. In the second part of the experiment animals received either WIN 55212-2 (0.5µg/rat) or saline respectively. Tail flick latency was measured three times with 10 minutes interval 30 minutes and 24 hours after the infusion into the CA1. Results indicate that post-training intra-CA1 administration of WIN55, 212-2 (0.25 and 0.5µg/rat) (P< 0.001) reduced step-down latency, showing an amnestic response. Also microinjection of WIN55212-2 (0.5ug/kg) into the CA1 region of the hippocampus induced analgesia compared to control group) (P< 0.01).
Mathematical and computational approaches can complement experimental studies of host–pathogen interactions

In addition to traditional and novel experimental approaches to study host–pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host–pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two‐photon intravital microscopy. Two others explore virtual or ‘in silico’ deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi‐scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host–pathogen interactions.
A Chaos-Based Blind Wavelet-Domain Watermarking Scheme Using Fast ICA

A new blind image watermarking scheme was proposed based on Fast ICA and the wavelet tree structure, which featured the following characteristics as scrambling the watermark to enhance the security, taking advantage of the human visual system properties and extracting the watermark without original image. The experimental results show that the proposed blind image watermarking scheme is invisible and robust against various signal processing attacks such as JPEG compression, noise adding, cropping and so on.
Critical Appraisal of Life Cycle Impact Assessment Databases for Agri‐food Materials

Simplified life cycle assessment (LCA) is an attempt to expedite standard LCA methodology. One common simplification is to use secondary databases comprised of results, instead of inventories, from previous LCA studies to avoid data collection and speed up calculations. This article contains an assessment of the depth and variance in such databases, using descriptive analyses and clustering of 2,276 published global warming potential weighted emissions records for agri‐food products. Tables with mean and standard deviation for each product were generated and can be used in the future by LCA practitioners. The analysis of the data sets shows that clustering emissions by product name explains 55% of the variance in the database, whereas geographic region and method of production of the material explain only 2%. Significant gaps in the richness and comprehensiveness of databases available are also identified; data unavailability is one of the main reasons for uncertainty. The conclusion is thus that the use of secondary databases looks promising, but there are still challenges to overcome regarding the depth of databases.
An improved QoS based routing in available bandwidth measurement using logarithmic backoff algorithm in MANets

In Mobile Adhoc Networks, channel bandwidth is a very limited resource. When the network is heavily loaded, the rate of collisions and hence the number of retransmissions increase. This leads to a notable decrease in network throughput and hence reduces quality of service because more and more bandwidth is wasted. MAC layer enables sharing the channel among all the nodes in the network effectively. It also enables perfect channel utilization. IEEE802.11 DCF uses exponential backoff algorithm to reduce collision. In this, average backoff value is increased after every collision due the large size of contention window. The time consumed by backoff process is more. Hence, proportion of bandwidth is lost due to collision as well as the overhead is introduced by backoff algorithm. This paper proposes an improved logarithmic based backoff mechanism to reduce collision and to improve the performance of the network. This algorithm uses logarithm of current backoff time to calculate next backoff. The difference between two backoff timers is small. So the chance of losing the channel access is less. Experimental results show that the proposed algorithm achieves better throughput, less delay and significant improvement in packet delivery ratio.