Miracle-dz/UniFAR-FTUs huggingface dataset:
Complications of Percutaneous Release of the Trigger Finger

Aim: Trigger finger is a common cause of hand pain and dysfunction. In this study, we aimed to evaluate retrospectively short and long-term outcomes of patients with trigger fingers who underwent percutaneous release operations. Materials and methods: Thirty-nine patients who underwent percutaneous release of the trigger finger were analyzed retrospectively. The patients were evaluated for digital nerve injury (hypoesthesia), recurrence, painful scar, and tendon rupture. Results: The patients' median age was 54 years (minimum 32 years - maximum 63 years). Hypoesthesia was most frequently seen at the first and fourth fingers. At the end of the first year, one patient developed tendon rupture (fourth finger). Recurrences were seen at the end of the first (n=5) and third (n=9) years. Recurrence was mostly seen in the fourth finger, followed by the third finger. Painful scars were observed in two patients. Conclusion: Percutaneous release is a blindly performed intervention and the emergence of unexpected complications should not be forgotten.
Heterogeneity of savanna canopy structure and function from imaging spectrometry and inverse modeling

Arid and semiarid ecosystems cover >40% of the earth’s terrestrial surface. Woody plants and herbaceous species, each with distinct allocation and phenological characteristics, respond to and constrain ecosystem function and biogeochemistry in these regions. High-resolution estimates of vegetation extent, aboveground allocation, and phenology are needed to improve regional links between ecosystem structure and function, especially under conditions of changing land use. We used a combination of imaging spectrometry and radiative transfer inverse modeling techniques to quantify the structural and biophysical attributes of plant canopies and land-cover types in a South Texas savanna. Estimated canopy structural attributes were used to calculate the fraction of photosynthetically active radiation (PAR; 400–700 nm) absorbed by plant canopies and the live foliage and nonphotosynthetic components of the canopies. A landscape analysis of these variables revealed the complexity of the biotic, abiotic, and anthropo.
Using stacked transformations for recognizing foreign accented speech

A common problem in speech recognition for foreign accented speech is that there is not enough training data for an accent-specific or a speaker-specific recognizer. Speaker adaptation can be used to improve the accuracy of a speaker-independent recognizer, but a lot of adaptation data is needed for speakers with a strong foreign accent. In this paper we propose a rather simple and successful technique of stacked transformations where the baseline models trained for native speakers are first adapted by using accent-specific data and then by another transformation using speaker-specific data. Because the accent-specific data can be collected offline, the first transformation can be more detailed and comprehensive, and the second one less detailed and fast. Experimental results are provided for speaker adaptation in English spoken by Finnish speakers. The evaluation results confirm that the stacked transformations are very helpful for fast speaker adaptation.
Young Consumers’ Cell Phone Usage in Developing Market:The Case of Turkish Youth Market

How do young Turkish consumers use cell phones? Can their behavior be differentiated, and what factors significantly identify categories of young wireless users? Why has this youth market become so important? Using survey research, descriptive statistics and Structured Equation Modeling (SEM), we analyzed results from a large sample of teenagers, providing a rich understanding of the substantial differences in Turkish youth consumers’ behavior toward cell phones, and discuss implications for cellphone marketers. Findings reveal three major usage groups in Turkey’s cell-phone youth market: trendy style, heavy-usage style and price-conscious style. These results provide substantial insight and direction for marketing strategists.
Optimierungspotenzial von Wäschen zur Biogasaufbereitung Teil 2. Chemische Wäschen

In Deutschland sind die Anforderungen fur die Einspeisung von Biogas in das Erdgasnetz durch die DVWG-Arbeitsblatter G 260 und G 262 festgelegt. Als Aufbereitungstechniken fur Biogas werden hauptsachlich die Druckwechseladsorption und verschiedenartige Waschen eingesetzt. Die Verfahren werden jedoch bisher kaum an die standortspezifischen Parameter angepasst. Am Beispiel der Diethanolamin(DEA)-Wasche werden die Einflusse der Prozessparameter und Optimierungspotenziale aufgezeigt. Die DEA-Wasche wird mit physikalischen Waschen u. a. hinsichtlich des Energiebedarfs verglichen. Die Untersuchung erfolgte auf Basis von Messungen, unterstutzt durch die Software Aspen Plus. In Germany, the requirements for biogas injection are specified by DVGW G 260 and G 262. The most applied biogas upgrading technologies are the pressure swing adsorption and physical as well as chemical scrubbers. However, these technologies are usually not adapted to the particular injection parameters of a specific location. In this work the influences of pressure, temperature and further parameters are shown for diethanolamine (DEA) scrubbers. Furthermore the energy demand of DEA scrubbers is compared with the energy demand of physical scrubbers. For the simulations the software Aspen Plus was applied, and measurements are used for verification.
Electromagnetic potentials without gauge transformations

In this paper, we show that the use of the Helmholtz theorem enables the derivation of uniquely determined electromagnetic potentials without the necessity of using gauge transformation. We show that the electromagnetic field comprises two components, one of which is characterized by instantaneous action at a distance, whereas the other propagates in retarded form with the velocity of light. In our attempt to show the superiority of the new proposed method to the standard one, we argue that the action-at-a-distance components cannot be considered as a drawback of our method, because the recommended procedure for eliminating the action at a distance in the Coulomb gauge leads to theoretical subtleties that allow us to say that the needed gauge transformation is not guaranteed. One of the theoretical consequences of this new definition is that, in addition to the electric E and magnetic B fields, the electromagnetic potentials are real physical quantities. We show that this property of the electromagnetic potentials in quantum mechanics is also a property of the electromagnetic potentials in classical electrodynamics.
Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions including emails, display ads and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, there is no formal justification for them and many of these fail even in simple canonical settings. The main contribution in this work is to develop an axiomatic framework for attribution in online advertising. In particular, we consider a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We propose a novel attribution metric, that we refer to as counterfactual adjusted Shapley value, which inherits the desirable properties of the traditional Shapley value. Furthermore, we establish that this metric coincides with an adjusted “unique-uniform” attribution scheme. This scheme is efficiently computable and implementable and can be interpreted as a correction to the commonly used uniform attribution scheme.
mtDNA sequence data supports an Asian ancestry and single introduction of the common carp into the Danube Basin

Common carp Cyprinus carpio (n=24) from the upper Danube (wild or feral forms, scaleless varieties) as well as Japanese koi shared the identical mtDNA haplotype (565 bp). In contrast, Amur River carp (n=5) exhibited unique haplotypes, 1-12 bp divergent from the Danubian one. These results support an Asian origin and single introduction or domestication event leading to the present diversity of common carp in Europe, as well as the ornamental Japanese koi.
Fatores de risco associados à mastite bubalina no Nordeste brasileiro

Risk factors for subclinical mastitis were studied in 474 buffaloes cows proceeding from four dairy farms located in the states of Pernambuco, Alagoas, Bahia and Ceara, Brazil. Milk samples (n=1896) of lactating buffaloes cows were examined for somatic cells count (SCC) and microbiologic exam, and a questionnaire composed by objective questions was applied in order to obtain animals and herd management data. Risk factors analysis was performed in two stages: univariate and mutivariate analysis. Two analysis were performed, one considering the animal classification for SCC as the dependent variable ( 400,000 - positive) and another, considering the microbiologic exam result (positive and negative). In the multivariate analysis considering SCC as dependent variable, the lack of teat washing was the only variable identified as a risk factor, so that farms without this procedure before the milking presented risk of infection of 2.68 (I.C. 1.49-4.83). In the multivariate analysis considering the microbiologic exam as dependent variable, it was observed that properties that performed the cleaning of the milking equipment manually presented risk of 1.85 (I.CI. 1.32-3.64), which was higher than those properties that performed the cleaning mechanically (p=0.019). Risk factors for the occurrence of subclinical mastitis in dairy buffaloes in Brazilian Northeast farms were related to the characteristics of improper milking management. Risk factors identified in this study must be carefully corrected in order to reduce the frequency of mastitis cases, and therefore, contribute for disease control and prevention in the herds.
Formal Fields: A Framework to Automate Code Generation Across Domains

Code generation, defined as automatically writing a piece of code to solve a given problem for which an evaluation function exists, is a classic hard AI problem. Its general form, writing code using a general language used by human programmers from scratch is thought to be impractical. Adding constraints to the code grammar, implementing domain specific concepts as primitives and providing examples for the algorithm to learn, makes it practical. Formal fields is a framework to do code generation across domains using the same algorithms and language structure. Its ultimate goal is not just solving different narrow problems, but providing necessary abstractions to integrate many working solutions as a single lifelong reasoning system. It provides a common grammar to define: a domain language, a problem and its evaluation. The framework learns from examples of code snippets about the structure of the domain language and searches completely new code snippets to solve unseen problems in the same field. Formal fields abstract the search algorithm away from the problem. The search algorithm is taken from existing reinforcement learning algorithms. In our implementation it is an apropos Monte-Carlo Tree Search (MCTS). We have implemented formal fields as a fully documented open source project applied to the Abstract Reasoning Challenge (ARC). The implementation found code snippets solving twenty two previously unsolved ARC problems.
A wide field-of-view low-resolution spectrometer at APEX: Instrument design and scientific forecast

Context.Characterising the large-scale structure in the Universe from present times to the high redshift epoch of reionisation is essential to constraining the cosmology, the history of star formation, and reionisation, to measuring the gas content of the Universe, and to obtaining a better understanding of the physical processes that drive galaxy formation and evolution. Using the integrated emission from unresolved galaxies or gas clouds, line intensity mapping (LIM) provides a new observational window to measure the larger properties of structures. This very promising technique motivates the community to plan for LIM experiments.Aims. We describe the development of a large field-of-view instrument, named CONCERTO (for CarbON CII line in post-rEionisation and ReionisaTiOn epoch), operating in the range 130–310 GHz from the APEX 12-m telescope (5100 m above sea level). CONCERTO is a low-resolution spectrometer based on the lumped element kinetic inductance detectors (LEKID) technology. Spectra are obtained using a fast Fourier transform spectrometer (FTS), coupled to a dilution cryostat with a base temperature of 0.1 K. Two two kilo-pixel arrays of LEKID are mounted inside the cryostat that also contains the cold optics and the front-end electronics.Methods. We present, in detail, the technological choices leading to the instrumental concept, together with the design and fabrication of the instrument and preliminary laboratory tests on the detectors. We also give our best estimates for CONCERTO sensitivity and give predictions for two of the main scientific goals of CONCERTO, that is, a [CII]-intensity mapping survey and observations of galaxy clusters.Results. We provide a detailed description of the instrument design. Based on realistic comparisons with existing instruments developed by our group (NIKA, NIKA2, and KISS), and on the laboratory characterisation of our detectors, we provide an estimate for CONCERTO sensitivity on the sky. Finally, we describe, in detail, two of the main scientific goals offered by CONCERTO at APEX.
Multi-Level Micro-Randomized Trial: Detecting the Proximal Effect of Messages on Physical Activity

Technological advancements in mobile devices have made it possible to deliver mobile health interventions to individuals. A novel intervention framework that emerges from such advancements is the just-in-time adaptive intervention (JITAI), where it aims to suggest the right support to the individual "just in time", when their needs arise, thus having proximal, near future effects. The micro-randomized trial (MRT) design was proposed recently to test the proximal effects of these JITAIs. In an MRT, participants are repeatedly randomized to one of the intervention options of various in the intervention components, at a scale of hundreds or thousands of decision time points over the course of the study. However, the extant MRT framework only tests the proximal effect of two-level intervention components (e.g. control vs intervention). In this paper, we propose a novel version of MRT design with multiple levels per intervention component, which we call "multi-level micro-randomized trial" (MLMRT) design. The MLMRT extends the existing MRT design by allowing multi-level intervention components, and the addition of more levels to the components during the study period. We apply generalized estimating equation type methodology on the longitudinal data arising from an MLMRT to develop the novel test statistics for assessing the proximal effects and deriving the associated sample size calculators. We conduct simulation studies to evaluate the sample size calculators based on both power and precision. We have developed an R shiny application of the sample size calculators. This proposed design is motivated by our involvement in the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation (DIAMANTE) study. This study uses a novel mobile application, also called "DIAMANTE", which delivers adaptive text messages to encourage physical activity.
Symmetric Positive Semi-definite Riemmanian Geometry with Application to Domain Adaptation

In this paper, we present new results on the Riemannian geometry of symmetric positive semi-definite (SPSD) matrices. First, based on an existing approximation of the geodesic path, we introduce approximations of the logarithmic and exponential maps. Second, we present a closed-form expression for Parallel Transport (PT). Third, we derive a canonical representation for a set of SPSD matrices. Based on these results, we propose an algorithm for Domain Adaptation (DA) and demonstrate its performance in two applications: fusion of hyper-spectral images and motion identification.
WaveFuse: A Unified Deep Framework for Image Fusion with Wavelet Transform

We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning. To our best knowledge, this is the first time the conventional image fusion method has been combined with deep learning. The useful information of feature maps can be utilized adequately through multi-scale discrete wavelet transform in our proposed method.Compared with other state-of-the-art fusion method, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation. Moreover, it's worth mentioning that comparable fusion performance trained in COCO dataset can be obtained by training with a much smaller dataset with only hundreds of images chosen randomly from COCO. Hence, the training time is shortened substantially, leading to the improvement of the model's performance both in practicality and training efficiency.
The turnpike with lack of observability

We obtain turnpike results for optimal control problems with lack of stabilizability in the state equation and/or detectability in the state term in the cost functional. We show how, under weakened stabilizability/detectability conditions, terminal conditions may affect turnpike phenomena. Numerical simulations have been performed to illustrate the theoretical results.
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration

Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs by writing sub-programs and then analysing their intermediate results to compose them in appropriate ways. Motivated by this intuition, we present a new synthesis approach that leverages learning to guide a bottom-up search over programs. In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a given set of input-output examples. This is a powerful combination because of several emergent properties: First, in bottom-up search, intermediate programs can be executed, providing semantic information to the neural network. Second, given the concrete values from those executions, we can exploit rich features based on recent work on property signatures. Finally, bottom-up search allows the system substantial flexibility in what order to generate the solution, allowing the synthesizer to build up a program from multiple smaller sub-programs. Overall, our empirical evaluation finds that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches. We demonstrate the effectiveness of our technique on a new data set for synthesis of string transformation programs.
Efficient Sampling Algorithms for Approximate Temporal Motif Counting

A great variety of complex systems ranging from user interactions in communication networks to transactions in financial markets can be modeled as temporal graphs, which consist of a set of vertices and a series of timestamped and directed edges. Temporal motifs in temporal graphs are generalized from subgraph patterns in static graphs which take into account edge orderings and durations in addition to structures. Counting the number of occurrences of temporal motifs is a fundamental problem for temporal network analysis. However, existing methods either cannot support temporal motifs or suffer from performance issues. In this paper, we focus on approximate temporal motif counting via random sampling. We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif. Furthermore, we devise an improved EWS algorithm that hybridizes edge sampling with wedge sampling for counting temporal motifs with 3 vertices and 3 edges. We provide comprehensive analyses of the theoretical bounds and complexities of our proposed algorithms. Finally, we conduct extensive experiments on several real-world datasets, and the results show that our ES and EWS algorithms have higher efficiency, better accuracy, and greater scalability than the state-of-the-art sampling method for temporal motif counting.
Modeling subgrid-scale stress by deconvolutional artificial neural networks in large eddy simulation of turbulence

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different spatial points are used as input features of the DANN models to reconstruct the unfiltered velocity. The grid width of the DANN models is chosen to be smaller than the filter width, in order to accurately model the effects of SGS dynamics. The DANN models can predict the SGS stress more accurately than the conventional approximate deconvolution method (ADM) and velocity gradient model (VGM) in a prior study: the correlation coefficients can be made larger than 99\% and the relative errors can be made less than 15\% for the DANN model. In an a posteriori study, a comprehensive comparison of the DANN model, the implicit large eddy simulation (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) shows that: the DANN model is superior to the ILES, DSM, and DMM models in the prediction of the velocity spectrum, various statistics of velocity and the instantaneous coherent structures without increasing the considerable computational cost. Besides, the trained DANN models without any fine-tuning can predict the velocity statistics well for different filter widths. These results indicate that the DANN framework with consideration of SGS spatial features is a promising approach to develop advanced SGS models in the LES of turbulence.
Graph Spanners by Sketching in Dynamic Streams and the Simultaneous Communication Model

Graph sketching is a powerful technique introduced by the seminal work of Ahn, Guha and McGregor'12 on connectivity in dynamic graph streams that has enjoyed considerable attention in the literature since then, and has led to near optimal dynamic streaming algorithms for many fundamental problems such as connectivity, cut and spectral sparsifiers and matchings. Interestingly, however, the sketching and dynamic streaming complexity of approximating the shortest path metric of a graph is still far from well-understood. Besides a direct $k$-pass implementation of classical spanner constructions, the state of the art amounts to a $O(\log k)$-pass algorithm of Ahn, Guha and McGregor'12, and a $2$-pass algorithm of Kapralov and Woodruff'14. In particular, no single pass algorithm is known, and the optimal tradeoff between the number of passes, stretch and space complexity is open. In this paper we introduce several new graph sketching techniques for approximating the shortest path metric of the input graph. We give the first {\em single pass} sketching algorithm for constructing graph spanners: we show how to obtain a $\widetilde{O}(n^{\frac{2}{3}})$-spanner using $\widetilde{O}(n)$ space, and in general a $\widetilde{O}(n^{\frac{2}{3}(1-\alpha)})$-spanner using $\widetilde{O}(n^{1+\alpha})$ space for every $\alpha\in [0, 1]$, a tradeoff that we think may be close optimal. We also give new spanner construction algorithms for any number of passes, simultaneously improving upon all prior work on this problem. Finally, we note that unlike the original sketching approach of Ahn, Guha and McGregor'12, none of the existing spanner constructions yield {\em simultaneous communication} protocols with low per player information. We give the first such protocols for the spanner problem that use a small number of rounds.