Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Level Alignment in Ternary Organic Solar Cells

Ternary organic solar cells (TOSC) are currently under intensive investigation, recently reaching a record efficiency of 17.1%. The origin of the device open‐circuit voltage (VOC), already a multifaceted issue in binary OSC, is even more complex in TOSCs. Herein, two ternary systems are investigated with one donor (D) and two acceptor materials (A1, A2) including fullerene and nonfullerene acceptors. By varying the ratio between the two acceptors, VOC is found to be gradually tuned between those of the two binary systems, D:A1 and D:A2. To investigate the origin of this change, ultraviolet photoemission spectroscopy (UPS) depth profiling is employed, which is used to estimate the photovoltaic gap in the ternary systems. The results reveal an excellent agreement between the estimated photovoltaic gap and the VOC for all mixing ratios, suggesting that the energetic alignment between the blend components varies depending on the ratio D:A1:A2. Furthermore, the results indicate that the sum of radiative and nonradiative losses in these ternary systems is independent of the blend composition. Finally, the superiority of UPS over X‐ray photoemission spectroscopy (XPS) depth profiling is demonstrated in resolving compositional profiles for material combinations with very similar chemical, but dissimilar electronic structures, as common in TOSCs.
Hierarchical BiGraph Neural Network as Recommendation Systems

Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain. In specific, developing recommendation systems often require addressing sparse structured data which often lacks the feature richness in either the user and/or item side and requires processing within the correct context for optimal performance. These datasets intuitively can be mapped to and represented as networks or graphs. In this paper, we propose the Hierarchical BiGraph Neural Network (HBGNN), a hierarchical approach of using GNNs as recommendation systems and structuring the user-item features using a bigraph framework. Our experimental results show competitive performance with current recommendation system methods and transferability.
Denoising Individual Bias for Fairer Binary Submatrix Detection

Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications. Existing binary matrix factorization (BMF) or co-clustering (CC) methods often assume i.i. d background noise. However, this assumption could be easily violated in real data, where heterogeneous row- or column-wise probability of binary entries results in disparate element-wise background distribution, and paralyzes the rationality of existing methods. We propose a binary data denoising framework, namely BIND, which optimizes the detection of true patterns by estimating the row- or column-wise mixture distribution of patterns and disparate background, and eliminating the binary attributes that are more likely from the background. BIND is supported by thoroughly derived mathematical property of the row- and column-wise mixture distributions. Our experiment on synthetic and real-world data demonstrated BIND effectively removes background noise and drastically increases the fairness and accuracy of state-of-the arts BMF and CC methods.
Learning-Based Computer-Aided Prescription Model for Parkinson's Disease: A Data-Driven Perspective

In this article, we study a novel problem: “automatic prescription recommendation for PD patients. ” To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learning lAtent Symptoms (PALAS), could recommend prescription using the multi-modality representation of the data. In PALAS, a latent symptom space is learned to better model the relationship between symptoms and prescription drug, as there is a large semantic gap between them. Moreover, we present an efficient alternating optimization method for PALAS. We evaluated our method using the data collected from 136 PD patients at Nanjing Brain Hospital, which can be regarded as a large dataset in PD research community. The experimental results demonstrate the effectiveness and clinical potential of our method in this recommendation task, if compared with other competing methods.
Évaluation du niveau de connaissances de l’hypertension artérielle chez des patients de plus de 70 ans en médecine générale

Buts de l’etude : L’objectif de ce travail etait de decrire le niveau de connaissances concernant l’hypertension arterielle (HTA) chez des patients de plus de 70 ans en medecine generale en Normandie. Le lien entre le niveau de connaissances de l’HTA et la litteratie, les caracteristiques socio-demographiques et geriatriques du patient et les caracteristiques de la maladie a egalement ete etudie. Methodes : Le niveau de connaissances concernant l’HTA et la litteratie des patients ont ete evalues par auto-questionnaires realises en salle d’attente lors d’une consultation chez le medecin generaliste. Les caracteristiques geriatriques, les caracteristiques de l’HTA et le traitement ont ete recueillis lors de la consultation de medecine generale. Resultats : 78 patients d’âge moyen 77,7±6 ans ont ete inclus. 85,9 % savaient qu’il presentait une HTA. La note moyenne au questionnaire de niveau de connaissances de l’HTA etait de 17,9±3 (note maximale possible 23). Le taux de bonnes reponses (aucune erreur) par categorie de questions etait respectivement de 66,7 % concernant les questions relatives a la connaissance de la pathologie, 51,3 % pour celles sur le traitement, 38,5 % pour les complications, 37,2 % pour le regime hyposode et 10,3 % concernant celles relatives aux symptomes de la maladie. La litteratie etait inadequate dans 60,3 % des cas. Les patients presentant une litteratie adequate avaient une note au questionnaire concernant la connaissance de l’HTA significativement superieure a ceux presentant une litteratie inadequate (19,2±2 vs. 16,9±3,1, p Conclusion : Notre etude demontre que le niveau de connaissances de l’HTA est insuffisant chez les patients de plus de 70 ans en medecine generale en Normandie et qu’il est lie au niveau de litteratie. Ameliorer le niveau de connaissances concernant l’HTA chez les patients âge est necessaire afin d’en diminuer la morbi-mortalite associee. De plus, une evaluation simple de la litteratie des patients est possible en medecine generale et doit etre realisee afin d’adapter l’information donnee au patient.
DIRECT CLONING OF MELANOMA ANTIGEN 1 (MAGE-1) AND THE E2 GENE OF HOG CHOLERA VIRUS (HCV) FROM THE BLUNT END PCR PRODUCT USING pET 101/D -TOPO

The objective of this study was to clone the full length of coding region of Mage-1 and E2 gene of Hog Cholera Virus (HCV) usingthe plasmid pET101/D-TOPO to get the corresponding recombinant plasmid. Samples were obtained from the liver tissue withhepatocellular carcinoma and a pig spleen infected by HCV from Sukoharjo Middle Java. cDNA were amplified by PCR usingGMTOPOF-GMTOPOR for Mage-1 and RS-1-RS-2 for E2 HCV.The purified PCR products were cloned into pET101/D-TOPO asdirect cloning expression vector. The gene target was transformed into E. coli Top10. Analysis of the recombinant plasmid wasundertaken by sequencing and restriction test. The PCR of Mage-1 gene resulted fragments at + 1105bp for first round PCR and at+ 931bp for the second round. The PCR of E2 gene HCV produced fragment at + 1200 bp. The DNA targets were cloned intopET101/D-TOPO from the blunt end PCR product directly. The sequence of full length of coding region of Mage-1 contained 927nucleotides that encoded 309 amino acids residues. The sequence of the full length coding region of E2 gene HCV was encoded by1218 nucleotide. EcoRV enzyme cuts vector pET101/D-TOPO at nucleotide positions at 545 and 4775bp. The result of EcoRVrestriction had band at 4230 and 2450 for pETGM/MAGE1-HCC and 4200 and 2700bp for pETRS/E2-SH. The recombinant plasmidpETGM/MAGE1-HCC and pETRS/E2-SH were obtained from the blunt end PCR product for direct cloning using pET101/D-TOPO. (FMI 2013;49 101-108)
Towards a more rigorous treatment of uncertainties on the velocity distribution of dark matter particles for capture in stars

Dark matter (DM) capture in stars offers a rich phenomenology that makes it possible to probe a wide variety of particle DM scenarios in diverse astrophysical environments. In spite of decades of improvements to refine predictions of capture-related observables and better quantify astrophysical and particle-physics uncertainties, the actual impact of the Galactic phase-space distribution function of DM has been overlooked. In this work, we tackle this problem by making use of self-consistent equilibrium phase-space models based on the Eddington inversion formalism and an extension of this method to a DM halo with some degree of anisotropy in velocity space. We demonstrate that incorrectly accounting for the variation of the DM velocity distribution with position in the Galaxy leads to a systematic error between a factor two and two orders of magnitude, depending in particular on the target star, the DM candidate mass and the type of interaction involved. Moreover, we show that underlying phase-space properties, such as the anisotropy of the velocity tensor, actually play an important part—previously disregarded—and can have a sizable impact on predictions of capture rates and subsequent observables. We argue that Eddington-like methods, which self-consistently account for kinematic constraints on the components of the Galaxy, actually provide a reliable next-to-minimal approach to narrow down uncertainties from phase-space modeling on predictions of observables related to DM capture in stars.
Eulerian dynamics in multi-dimensions with radial symmetry

We study the global wellposedness of pressure-less Eulerian dynamics in multi-dimensions, with radially symmetric data. Compared with the 1D system, a major difference in multi-dimensional Eulerian dynamics is the presence of the spectral gap, which is difficult to control in general. We propose a new pair of scalar quantities that provides a significant better control of the spectral gap. Two applications are presented. (i) the Euler-Poisson equations: we show a sharp threshold condition on initial data that distinguish global regularity and finite time blowup; (ii) the Euler-alignment equations: we show a large subcritical region of initial data that leads to global smooth solutions.
Super-resolution in vivo retinal imaging using structured illumination ophthalmoscopy

Structured illumination microscopy (SIM) is one of the most versatile super-resolution techniques. Yet, its application to live imaging has been so far mainly limited to fluorescent and stationary specimens. Here, we present advancements in SIM to jointly tackle all the challenges of imaging living samples, i.e., obtaining super-resolution over an undistorted wide-field while dealing with sample motion, scattering, sample-induced optical aberrations and low signal-to-noise ratio. By using adaptive optics to compensate for optical aberrations and a reconstruction algorithm tailored for a moving and thick tissue, we successfully applied SIM to in vivo retinal imaging and demonstrated structured illumination ophthalmoscopy for high contrast super-resolution in vivo imaging of the human retina.
Inclination Excitation of Solar System Debris Disk Due to Stellar Flybys

Most stars form in clusters where relatively close encounters with other stars are common and can leave imprints on the orbital architecture of planetary systems. In this paper, we investigate the inclination excitation of debris disk particles due to such stellar encounters. We derive an analytical expression that describes inclination excitation in the hierarchical limit where the stellar flyby is distant. We then obtain numerical results for the corresponding particle inclination distribution in the nonhierarchical regime using a large ensemble of N-body simulations. For encounters with expected parameters, we find that the bulk inclination of the disk particles remains low. However, a distinct high-inclination population is produced by prograde stellar encounters for particles with final pericenter distances above 50 au. The maximum extent it of the inclination distribution scales with the inclination of the encounter for massive star flybys with low incoming velocity. The inclination distribution of observed trans-Neptunian objects places constraints on the dynamical history of our solar system. For example, these results imply an upper limit on product of the number density n of the solar birth cluster and the Sun’s residence time τ of the form nτ ≲ 8 × 104 Myr pc−3. Stronger constraints can be derived with future observational surveys of the outer solar system.
Network connectivity optimization: An evaluation of heuristics applied to complex networks and a transportation case study

Network optimization has generally been focused on solving network flow problems, but recently there have been investigations into optimizing network characteristics. Optimizing network connectivity to maximize the number of nodes within a given distance to a focal node and then minimizing the number and length of additional connections has not been as thoroughly explored, yet is important in several domains including transportation planning, telecommunications networks, and geospatial analysis. We compare several heuristics to explore this network connectivity optimization problem with the use of random networks, including the introduction of two planar random networks that are useful for spatial network simulation research, and a real-world case study from urban planning and public health. We observe significant variation between nodal characteristics and optimal connections across network types. This result along with the computational costs of the search for optimal solutions highlights the difficulty of finding effective heuristics. A novel genetic algorithm is proposed and we find this optimization heuristic outperforms existing techniques and describe how it can be applied to other combinatorial and dynamic problems.
Traffic Control Gesture Recognition for Autonomous Vehicles

A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. We introduce a dataset that is based on 3D body skeleton input to perform traffic control gesture classification on every time step. Our dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. To evaluate our dataset, we propose eight sequential processing models based on deep neural networks such as recurrent networks, attention mechanism, temporal convolutional networks and graph convolutional networks. We present an extensive evaluation and analysis of all approaches for our dataset, as well as real-world quantitative evaluation. The code and dataset is publicly available4.
On Package Freshness in Linux Distributions

The open-source Linux operating system is available through a wide variety of distributions, each containing a collection of installable software packages. It can be important to keep these packages as fresh as possible to benefit from new features, bug fixes and security patches. However, not all distributions place the same emphasis on package freshness. We conducted a survey in the first half of 2020 with 170 Linux users to gauge their perception of package freshness in the distributions they employ, the value they place on package freshness and the reasons why they do so, and the methods they use to update packages. The results of this survey reveal that, for the aforementioned reasons, keeping packages up to date is an important concern to Linux users and that they install and update packages through their distribution’s official repositories whenever possible, but often resort to third-party repositories and package managers for proprietary software and programming language libraries. Some distributions are perceived to be much quicker in deploying package updates than others. These results are useful to assess the expectations and requirements of Linux users in terms of package freshness and guide them in choosing a fitting distribution.
Sergey Ivanovitch Ognev and the formation of theriology in Russia

Abstract Sergey I. Ognev (1886–1951), one of Russia’s most famous biologists, was both a theoretical scientist and a great field researcher. Ognev’s “Fauna Mosquensis” (1913) is the most comprehensive study of the mammals of European Russia after Pallas. Throughout his life, Ognev’s work involved two important components: biological observation in nature and study of geographical distribution. His combination of excellent knowledge of natural habitats and morphology created a unique insight. This effort resulted in seven volumes of “Mammals of Eastern Europe and Northern Asia” and “Mammals of the USSR and Adjacent Countries,” published from 1928 to 1950. This work included comprehensive taxonomic accounts, detailed morphological descriptions, geographical ranges and natural history data for 264 mammalian species and 635 forms. 4879 pages were illustrated, including almost 2000 photographs and drawings of skulls and dentition, a huge number of distributional maps made by the author and 56 color figures drawn by famous animal painters. The taxonomic position of a surprisingly large portion of forms classified by Ognev have been confirmed by modern molecular genetic approaches. Ognev also authored significant books on ecology and field photography. It is impossible to overestimate the significance of Ognev’s contribution to mammal studies; his “Mammals of the USSR…” volumes remain one of the most cited works in the field.
Sparse based Super Resolution Multilayer Ultrasonic Imaging

In this paper, we model the signal propagation effect in ultrasonic imaging using Huygens principle and use this model to develop sparse signal representation based imaging techniques for a two-layer object immersed in water. Relying on the fact that the image of interest is sparse, we cast such an array based imaging problem as a sparse signal recovery problem and develop two types of imaging methods, one method uses only one transducer to illuminate the region of interest {and for this case the system is modeled as a single input multiple output (SIMO) system. The second method relies on all transducers to transmit ultrasonic waves into the material under test and in this case the system is modeled as a multiple input multiple output (MIMO) system}. We further extend our work to a scenario where the propagation velocity of the wave in the object under test is not known precisely. {We discuss different techniques such as greedy based algorithms as well as $\ell_1$-norm minimization based approach to solve the proposed sparse signal representation based method. We give an assessment of the computational complexity of the $\ell_1$-norm minimization based approach for the SIMO and the MIMO cases. We further point out the superiority of the $\ell_1$-norm minimization based approach over the greedy based algorithms. Then we give a comprehensive analysis of error for both the greedy based approaches as well as the $\ell_1$-norm minimization based technique for both the SIMO and the MIMO cases. The analysis utilizes tools from two powerful branches of modern analysis, \emph{local analysis in Banach spaces} and \emph{concentration of measure}}. We finally apply our methods to experimental data gathered from a solid test sample immersed in water and show that sparse signal recovery based techniques outperform the conventional methods available in the literature.
Optimal taxi fleet management: a linear programming approach to the taxi capacity problem

This paper develops a model to determine the optimal number of taxis in a city by examining the trade-off between the overall profitability of the taxi service versus the customer satisfaction. We provide a data analytic investigation of taxi trips in New York City. We model the taxi service strategy by a fleet management model that can handle arrivals and deterministic travel times. Under this model, we examine the number of taxis in a particular period of time and measure the maximum profit in the overall system and the minimum number of rejected customer requests. We observe that the maximum profit of the overall system can be reduced significantly due to reducing the cost of driving without passenger(s). We present a case study with New York City Taxi data with several experimental evaluations of our model with a different period of time during the day and also with a realistic and a heuristic model. The results provide a better understanding of the requirement to satisfy the demand in a different period of time. These data may have important implications in the field of self-driving vehicles in the near future.
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones

A novel framework for meeting transcription using asynchronous microphones is proposed in this paper. It consists of audio synchronization, speaker diarization, utterance-wise speech enhancement using guided source separation, automatic speech recognition, and duplication reduction. Doing speaker diarization before speech enhancement enables the system to deal with overlapped speech without considering sampling frequency mismatch between microphones. Evaluation on our real meeting datasets showed that our framework achieved a character error rate (CER) of 28.7 % by using 11 distributed microphones, while a monaural microphone placed on the center of the table had a CER of 38.2 %. We also showed that our framework achieved CER of 21.8 %, which is only 2.1 percentage points higher than the CER in headset microphone-based transcription.