Miracle-dz/UniFAR-FTUs huggingface dataset:
A Self-Adaptive Trust Region Algorithm with Line Search Technique

In this paper, we propose an algorithm for unconstrained optimization that employs both adaptive trust region techniques with line searchs. Unlike traditional adaptive trust region methods, our algorithm does not resolve the sub problem if the trial step isn’t accepted, but instead performs the Wolfe line search at each iteration. Under mild conditions, the global convergence is proved and the super linear convergence of the new algorithm is shown without the condition that the Hessian of the objective function at the solution be positive definite. Preliminary numerical results indicate that the performance of the new method is very efficient.
All-fiber continuous wave coherent homodyne terahertz spectrometer operating at 1.55 µm wavelengths

Room temperature homodyne and heterodyne THz detections are reported using an 1.55 µm-based THz source. The first experimental demonstration is a continuous wave terahertz homodyne detection, where THz emission is performed with an InGaAs/InP uni-travelling carrier photodiode monolithically integrated with a horn antenna. The coherent homodyne detection is achieved with an ion-irradiated InGaAs photoconductor integrated with a spiral antenna. Experimental results has been obtained up to 700 GHz with a good signal to noise ratio. The second experimental setup is a room temperature heterodyne detection with a schottky mixer.
What Explains Microfinance Distribution Surplus? A Stakeholder-oriented Approach

What are the drivers of productivity surplus distribution to microfinance stakeholders? This paper shows that the size of the institution is the main indicator that can explain the gain in productivity surplus but also the surplus given to clients (decrease of interest rates) and staff. Moreover, cooperatives keep a significantly lesser part of their surplus for future growth, reserve, or distribution to investors. Finally, larger, more subsidised MFIs, and particularly cooperatives, tend to give a greater part of their surplus to their employees.
Chronic myeloid leukemia stem cells in the era of targeted therapies: resistance, persistence and long-term dormancy

Targeted therapies of chronic myeloid leukemia (CML) using tyrosine kinase inhibitors (TKI) have profoundly changed the natural history of the disease with a major impact on survival. Molecular monitoring with BCR-ABL quantification shows that a status of undetectable molecular residual disease (UMRD) is obtained in a significant minority of patients. However, it remains unclear whether these patients are definitively cured of their leukemia. Imatinib mesylate withdrawal trials have demonstrated the rapid appearance of the malignant clone in the majority of the patients whereas some patients remain in a state of UMRD. It has clearly been demonstrated that the most primitive stem cells are refractory to all TKIs used in clinical practice. In addition, long-term dormancy is one of the most fundamental characteristics of hematopoietic stem cells. In this context, we have recently undertaken a systematic analysis of the bone marrow stem cell compartment in several patients in durable UMRD. We have demonstrated the long-term persistence of a considerable amount of BCR-ABL-expressing stem cells, even in the absence of relapse. The phenomenon of long-term leukemic stem cell dormancy is of major importance in CML and one of the key questions in cancer biology in general. We discuss, here, the potential mechanisms, including intrinsic and microenvironmental factors, that control the response of leukemic stem cells (LSCs) to targeted therapies and potential novel strategies currently in progress with a curative intent. Moreover, we propose a molecular evaluation of the residual LSC compartment in selected patients in order to develop rational TKI-cessation strategies in CML.
Source code Analyzer Source code Fixpoint constraints Fixpoint constraints Syntactic Matching Function ! Constraint Merger Differential derivations Marginal Inference

Programs often evolve continuously by integrating changes from multiple programmers. The effective adoption of program analysis tools in this continuous integration setting is hindered by the need to only report alarms relevant to a particular program change. We present a probabilistic framework, Drake, to apply program analyses to continuously evolving programs. Drake is applicable to a broad range of analyses that are based on deductive reasoning. The key insight underlying Drake is to compute a graph that concisely and precisely captures differences between the derivations of alarms produced by the given analysis on the program before and after the change. Performing Bayesian inference on the graph thereby enables to rank alarms by likelihood of relevance to the change. We evaluate Drake using Sparrow—a static analyzer that targets buffer-overrun, format-string, and integer-overflow errors—on a suite of ten widely-used C programs each comprising 13k–112k lines of code. Drake enables to discover all true bugs by inspecting only 30 alarms per benchmark on average, compared to 85 (3X more) alarms by the same ranking approach in batch mode, and 118 (4X more) alarms by a differential approach based on syntactic masking of alarms which also misses 4 of the 26 bugs overall.
Elastic Net Constraints for Shape Matching

We consider a parametrized relaxation of the widely adopted quadratic assignment problem (QAP) formulation for minimum distortion correspondence between deformable shapes. In order to control the accuracy/sparsity trade-off we introduce a weighting parameter on the combination of two existing relaxations, namely spectral and game-theoretic. This leads to the introduction of the elastic net penalty function into shape matching problems. In combination with an efficient algorithm to project onto the elastic net ball, we obtain an approach for deformable shape matching with controllable sparsity. Experiments on a standard benchmark confirm the effectiveness of the approach.
Effects of Predictability of Load Magnitude on the Response of the Flexor Digitorum Superficialis to a Sudden Fingers Extension

Muscle reflexes, evoked by opposing a sudden joint displacement, may be modulated by several factors associated with the features of the mechanical perturbation. We investigated the variations of muscle reflex response in relation to the predictability of load magnitude during a reactive grasping task. Subjects were instructed to flex the fingers 2–5 very quickly after a stretching was exerted by a handle pulled by loads of 750 or 1250 g. Two blocks of trials, one for each load (predictable condition), and one block of trials with a randomized distribution of the loads (unpredictable condition) were performed. Kinematic data were collected by an electrogoniometer attached to the middle phalanx of the digit III while the electromyography of the Flexor Digitorum Superficialis muscle was recorded by surface electrodes. For each trial we measured the kinematics of the finger angular rotation, the latency of muscle response and the level of muscle activation recorded below 50 ms (short-latency reflex), between 50 and 100 ms (long-latency reflex) and between 100 and 140 ms (initial portion of voluntary response) from the movement onset. We found that the latency of the muscle response lengthened from predictable (35.5±1.3 ms for 750 g and 35.5±2.5 ms for 1250 g) to unpredictable condition (43.6±1.3 ms for 750 g and 40.9±2.1 ms for 1250 g) and the level of muscle activation increased with load magnitude. The parallel increasing of muscle activation and load magnitude occurred within the window of the long-latency reflex during the predictable condition, and later, at the earliest portion of the voluntary response, in the unpredictable condition. Therefore, these results indicate that when the amount of an upcoming perturbation is known in advance, the muscle response improves, shortening the latency and modulating the muscle activity in relation to the mechanical demand.
Goal-oriented buffer management revisited

In this paper we revisit the problem of achieving multi-class workload response time goals by automatically adjusting the buffer memory allocations of each workload class. We discuss the virtues and limitations of previous work with respect to a set of criteria we lay out for judging the success of any goal-oriented resource allocation algorithm. We then introduce the concept of hit rate concavity and develop a new goal-oriented buffer allocation algorithm, called Class Fencing, that is based on this concept. Exploiting the notion of hit rate concavity results in an algorithm that not only is as accurate and stable as our previous work, but also more responsive, more robust, and simpler to implement.
Guns and Trafficking in Crack-Cocaine and Other Drug Markets

This article examines the relationship between gun possession and the nature of an offender’s involvement in drug markets. The analyses are based on data obtained from drug offenders who participated in the 1997 Survey of Inmates of State and Federal Correctional Facilities. The authors find that participants in crack-cocaine markets are more likely to possess guns than participants in powdered-cocaine, opiate, and marijuana markets, particularly if they are street-level crack dealers. However, participants in barbiturates and amphetamine markets also have high rates of gun possession. The authors also find relatively high levels of gun possession among traffickers who handle stashes of moderately large market value, who have central roles in the trade, and who are members of drug organizations. Finally, offenders who are young, female, African American, and from lower economic status are more likely to traffic in crack cocaine than in other drugs.
Interdisciplinarity in Engineering ( INTER-ENG 2013 ) Wavelet transform based HRV analysis

Heart rate variability (HRV) is a measure of variations of the successive heart beats. It is usually calculated by analyzing the time series of beat-to-beat intervals from ECG signal. HRV is considered as an indicator of the activity of autonomic regulation of circulatory function and as the one of the most significant methods of analyzing the activity of the autonomic nervous system. In this study a Wavelet Packet Transform (WPT) based HRV feature extraction method is presented and compared with the Fourier transformed based one. The results show that a good WPT decomposition gives a significant index of sympathovagal balance, which is the variance (or power) of the ECG signal changes as a function of frequency. © 2013 The Authors.Published by Elsevier B.V. Selection and peer-review under responsibility ofDepartment of Electrical and Computer Engineering, Faculty of Engineering, “Petru Maior” University of Tîrgu Mureș.
Realistic real‐time rendering of landscapes using billboard clouds

We present techniques for realistic real-time rendering of complex landscapes that consist of many highly detailed plant models. The plants are approximated by dynamically changing sets of billboards. Realistic illumination is approximated using spherical harmonics. Since even the rendering of simple billboard cloud plants is too time consuming, the landscape in the background is approximated with shell textures. The combination of these techniques allows us to render large scenes in real-time with varying illumination, which is interesting for computer games and interactive visualization in landscaping and architecture as well as modelling
Aquatic beetles Communities & REDD + Leaf phenology & transpiration Attracting ibises to safe roost sites

We present the results of a faunistic inventory undertaken on aquatic Polyphaga beetles as part of a workshop organised by the BIO-PHIL project in Cambodia. Three small rivers and a pond in Kampong Speu Province were sampled in July 2018. Twenty-one taxa were collected during the sampling and identifi ed to species or morphospecies level. These are described with comments on habitat, distribution and further remarks, where applicable. A checklist of the genera and species of aquatic Dryopidae, Elmidae, Hydraenidae, Hydrophilidae, Psephenidae and Spercheidae of Cambodia is presented which includes new records for the country.
Nitrate Intake Does Not Influence Bladder Cancer Risk: The Netherlands Cohort Study

Objectives N-nitroso compounds, endogenously formed from nitrate-derived nitrite, are suspected to be important bladder carcinogens. However, the association between nitrate exposure from food or drinking water and bladder cancer has not been substantially investigated in epidemiologic studies. Methods We evaluated the associations between nitrate exposure and bladder cancer in the Netherlands Cohort Study, conducted among 120,852 men and women, 55–69 years of age at entry. Information on nitrate from diet was collected via a food frequency questionnaire in 1986 and a database on nitrate content of foods. Individual nitrate exposures from beverages prepared with tap water were calculated by linking the postal code of individual residence at baseline to water company data. After 9.3 years of follow-up and after excluding subjects with incomplete or inconsistent dietary data, 889 cases and 4,441 subcohort members were available for multivariate analyses. We calculated incidence rate ratios (RR) and corresponding 95% confidence intervals (CIs) using Cox regression analyses. We also evaluated possible effect modification of dietary intake of vitamins C and E (low/high) and cigarette smoking (never/ever). Results The multivariate RRs for nitrate exposure from food, drinking water, and estimated total nitrate exposure were 1.06 (95% CI, 0.81–1.31), 1.06 (95% CI, 0.82–1.37), and 1.09 (95% CI, 0.84–1.42), respectively, comparing the highest to the lowest quintiles of intake. Dietary intake of vitamins C and E (low/high) and cigarette smoking (never/ever) had no significant impact on these results. Conclusion Although the association between nitrate exposure and bladder cancer risk is biologically plausible, our results in this study do not support an association between nitrate exposure and bladder cancer risk.
How Do I Address You? Modelling addressing behavior based on an analysis of a multi-modal corpora of conversational discourse

Addressing is a special kind of referring and thus principles of multi-modal referring expression generation will also be basic for generation of address terms and addressing gestures for conversational agents. Addressing is a special kind of referring because of the different (second person instead of object) role that the referent has in the interaction. Based on an analysis of addressing behaviour in multi-party face-to-face conversations (meetings, TV discussions as well as theater plays), we present outlines of a model for generating multi-modal verbal and non-verbal addressing behaviour for agents in multi-party interactions.
A droop controller is intrinsically a phase-locked loop

This paper demonstrates that a droop controller, which is fundamental to the operation of power systems and now the parallel operation of inverters, is intrinsically the same as a phase-locked loop (PLL), which is widely adopted in modern electrical engineering. This bridges the gap between the two communities working on droop control and PLLs. As a result, droop controllers and PLLs can be improved and further developed via adopting the advancements in the other community. This also offers insightful understanding to power systems that have inverters and generators mixed together and leads to significant technological breakthrough for the grid connection of renewable energy. For example, there is no longer a need for grid-connected inverters to have a phase-locked loop and a droop controller at the same time.
Kent Sutherland More on Language Models 1 Review : Relevance Model

In the previous lecture, we discussed the idea of relevance models, as presented in [Lavrenko & Croft 01]. For each query, a language model for relevance is constructed. The final product is a language model based on a collection of documents. The final model estimation details were very similar to query likelihood, even though the relevance model was derived from the ideas in [Robertson & Spärck Jones 76]. The relevance model work reinforces the “importance of being reversed” (Lafferty and Zhai’s phrasing), as assigning likelihood to a query from a document-based model yields better statistical estimates than assigning likelihood to a document based on a query-based model. While such an approach to deriving language models “works” mathematically, the intuition for it severely stretches the original assumptions made in the Robertson & Spärck Jones approach. This discussion starts with a mostly clean slate and attempts to justify the language model approach to query likelihood in a more intuitive fashion.
Learning to Control Drones in Natural Environments: A Survey

Lightweight, autonomous drones are soon expected to be used in a wide variety of tasks such as aerial surveillance, delivery, or monitoring of existing architectures. A large body of literature in robotic perception and control exists. Existing methods are mature, but not robust, therefore hindering drones’ deployment in natural environments, like a city or a forest. Indeed, in unstructured and dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. A recent line of research has exploited the “perception-awareness” of deep learning techniques to unlock autonomous flight in uncontrolled environments. This is motivated by the insight that traditional methods relying on global state estimates in the form of robot poses are doomed to fail because of the inherent difficulties of pose estimation at high speed along with their inability to adequately cope with dynamic environments. In this paper, we survey existing learning-based methods for drone navigation and identify open areas of research for future work.
Worst-case Asymmetric Distributed Source Coding

We consider a worst-case asymmetric distributed source coding problem where an information sink communicates with $N$ correlated information sources to gather their data. A data-vector $\bar{x} = (x_1, ., x_N) \sim {\mathcal P}$ is derived from a discrete and finite joint probability distribution ${\mathcal P} = p(x_1, ., x_N)$ and component $x_i$ is revealed to the $i^{\textrm{th}}$ source, $1 \le i \le N$. We consider an asymmetric communication scenario where only the sink is assumed to know distribution $\mathcal P$. We are interested in computing the minimum number of bits that the sources must send, in the worst-case, to enable the sink to losslessly learn any $\bar{x}$ revealed to the sources. We propose a novel information measure called information ambiguity to perform the worst-case information-theoretic analysis and prove its various properties. Then, we provide interactive communication protocols to solve the above problem in two different communication scenarios. We also investigate the role of block-coding in the worst-case analysis of distributed compression problem and prove that it offers almost no compression advantage compared to the scenarios where this problem is addressed, as in this paper, with only a single instance of data-vector.
A 3GHz to 7GHz fast-hopping frequency synthesizer for UWB

A 3 to 7 GHz fast-hopping frequency synthesizer for ultra-wideband (UWB) applications, realized in a standard digital 0.13 /spl mu/m CMOS copper technology is presented. The synthesizer is based on single-sideband (SSB) up- or downconversion of a low frequency (LF) input by means of an integrated LC-VCO used as local oscillator (LO). Two LO are selectable to achieve the large output frequency range with reduced LF bandwidth requirements. This select multiplexer can also be used to pulse the output frequency of the synthesizer with switching times less than 1 nsec. Operating from a 1.5 V supply the LO leakage and sideband suppression is better than -25 dBc for downconversion and <-18 dBc for upconversion, which is sufficient for UWB applications. Measured output frequency range is 3.3 to 6.1 GHz, limited by measurement equipment. Power consumption is 95 mW and module area 0.7 mm/sup 2/ including pads.
Plasmonic Antennas based Gas sensor using Graphene

Plasmonics based sensors have been a prominent area of research. Plasmonics based sensors are widely used in bio sensing and many other applications. The basic mechanism in which these sensors operate is that there are metal nano particles that are separated by a very small distance between them. The dimensions of these nano particles is much smaller than the incident light [1]. For sensing purposes, we use the light in the visible spectrum. The dimension of those nano particles is in the range of 20200nm [2]. When light is incident on these nano particles, it generates a very high electric field in the gap between the two nano particles. This generated electric field is dependent on the size, shape and material of the nano particles and also the properties of the environment surrounding the nano particles.
THE ARRIVAL OF A SECOND CHILD :

The interplay between the infant's development and its family context during the early years has been studied in a longitudinal approach. The family was conceptualized as a problem solvina group trying to integrate a new child and to find a new balance. Sixteen normal families having one child at the age of one to three and a second child born at the beginning of the study were observed monthly in unstructured situations in their homes during a two year period, Three years later, when the second children were about five years old, both children of each family were given intelligence tests. Tape recorded observations were split up into short episodes lasting between 20 and 40 seconds each and scored according to categories covering different domains of family interaction such as family constellation, family dynamics. and socialization activities. Data from seven families were cross classified and analyzed as to aeneral trends of chances and continuities during the two year period according to the method of log-linear models (study 1); verbal intelligence scores of each families' two children in preschool/school age were used as guidelines for a longitudinal follow down approach: four families whose both children had high scores on verbal intelligence tests and four families with low scoring children were selected from the total sample. The two contrasting groups were used for exploring salient differences in family-specific socialization activities during the early years (study 2). Results of general trend analyses point to an age specific adaptation rhythm of family dynamics durino the two year period. Results from the study of contrasting groups point to both familyand age-specific socialization patterns during the early years.
Neural Networks and Introduction to Deep Learning 1

These techniques have enabled significant progress in the fields of sound and image processing, including facial recognition, speech recognition, computer vision, automated language processing, text classification (for example spam recognition). Potential applications are very numerous. A spectacularly example is the AlphaGo program, which learned to play the go game by the deep learning method, and beated the world champion in 2016.