Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepMVSHair: Deep Hair Modeling from Sparse Views

We present DeepMVSHair, the first deep learning-based method for multi-view hair strand reconstruction. The key component of our pipeline is HairMVSNet, a differentiable neural architecture which represents a spatial hair structure as a continuous 3D hair growing direction field implicitly. Specifically, given a 3D query point, we decide its occupancy value and direction from observed 2D structure features. With the query point’s pixel-aligned features from each input view, we utilize a view-aware transformer encoder to aggregate anisotropic structure features to an integrated representation, which is decoded to yield 3D occupancy and direction at the query point. HairMVSNet effectively gathers multi-view hair structure features and preserves high-frequency details based on this implicit representation. Guided by HairMVSNet, our hair-growing algorithm produces results faithful to input multi-view images. We propose a novel image-guided multi-view strand deformation algorithm to enrich modeling details further. Extensive experiments show that the results by our sparse-view method are comparable to those by state-of-the-art dense multi-view methods and significantly better than those by single-view and sparse-view methods. In addition, our method is an order of magnitude faster than previous multi-view hair modeling methods.
Analysis and Visualization Features in PEVNET

The information visualization of networks has been a tricky task during the last decade. Visualization of features by way of merging, linking, and grouping of entity attributes is provided to criminal network investigators. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. We have found that the prevailing challenges to information visualization can be eliminated to a large extent by detecting evolving network patterns which are extracted by way of visual analysis of criminal activity based on temporal data, by examining some dynamics of criminal networks, and by making use of some novel interactive features. The current study will help to understand interesting patterns in criminal data by way of visualization. Besides our previously proposed network visualization features, we have appended five new features. These features include ‘Pie-chart feature’ which has been proposed for better ‘details on demand’ facility to the analysts. A ‘Trend analysis feature’ is proposed for visualizing the variation in different crimes over some span of time. The ‘Graphical Trend Analysis feature’ provides a graphical interface to the analysts. There is a unique ‘Encircle feature’, with the aid of which the desired clusters can be dragged away from the dense network for easy manipulation. With ‘Similar node feature’, the analysts may get summarized information regarding the activity of different nodes which are at distant apart. We have made an evaluation of our proposed visualization features by conducting an experiment. Thirty-two participants evaluated the system. The experiment was performed in two phases. In the first phase, a usability evaluation and qualitative feedback was carried out to check whether the features provided adequate results to the users. In the second phase, the comparison of the features had been performed against some other state-of-the-art tool. These tasks were to be performed in the groups of participants. The public data set of Chicago Narcotics was used. We found that the participants, of the PEVNET group, performed the tasks faster as compared to the other techniques used in the experiment. We have demonstrated the usability of the new features with examples by employing the datasets. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data. The usability, of the proposed features, indicates that the crime analysts will get a valuable insight into the criminal networks.
VOCAL: Vowel and Consonant Layering for Expressive Animator-Centric Singing Animation

Singing and speaking are two fundamental forms of human communication. From a modeling perspective however, speaking can be seen as a subset of singing. We present VOCAL, a system that automatically generates expressive, animator-centric lower face animation from singing audio input. Articulatory phonetics and voice instruction ascribe additional roles to vowels (projecting melody and volume) and consonants (lyrical clarity and rhythmic emphasis) in song. Our approach directly uses these insights to define axes for Melodic-accent and Pitch-sensitivity (Ma-Ps), which together provide an abstract space to visually represent various singing styles. In our system. vowels are processed first. A lyrical vowel is often sung tonally as one or more different vowels. We perform any such vowel modifications using a neural network trained on input audio. These vowels are then dilated from their spoken behaviour to bleed into each other based on Melodic-accent (Ma), with Pitch-sensitivity (Ps) modeling visual vibrato. Consonant animation curves are then layered in, with viseme intensity modeling rhythmic emphasis (inverse to Ma). Our evaluation is fourfold: we show the impact of our design parameters; we compare our results to ground truth and prior art; we present compelling results on a variety of voices and singing styles; and we validate these results with professional singers and animators.
Lightweight Privacy-Preserving Ride-Sharing Protocols for Autonomous Cars

Ride-sharing is a popular way of transportation that reduces traffic and the costs of the trip. Emerge of autonomous vehicles makes ride-sharing more popular because these vehicles do not require a driver’s effort. Therefore, in order to find a suitable ride-share, the service provider is not restricted to the driver’s trip. Thus, the autonomous cars are more flexible with matching the passengers. Passengers who want to participate in car-sharing send their trip data to a ride-sharing service provider. However, the passenger’s trip data contains sensitive information about the passenger’s locations. Multiple studies show that a person’s location data can reveal personal information about them, e.g., their health condition, home, work, hobbies, and financial situation. In this paper, we propose a lightweight privacy-preserving ride-sharing protocol for autonomous cars. Contrary to previous works on this topic, our protocol does not rely on any extra party to guarantee privacy and security. Our protocol consists of two main phases: i) privacy-preserving group forming, and ii) privacy-preserving fair pick-up point selection. In addition to ride-sharing, the two phases of our protocol can also be applied to other use cases. We have implemented our protocol for a realistic ride-sharing scenario, where 1000 passengers simultaneously request a ride-share. Our evaluation results show that the time and communication costs of our protocol are such that it is feasible for real-world applications.
A System Architecture for Achieving Proactive and Reactive Social Media Marketing

Social media is now becoming the most popular marketing platform and therefore more and more marketers are trying to use efficient tool or system for improving the performance of social media marketing, such as chatbots, social marketing system, social customer relationship management system, smarter advertising and promotion system, etc. In this paper, we intend to propose a system architecture for achieving the idea of proactive and reactive social media marketing. The system can collect data from social media and to discover related posts of Facebook fans pages or articles of web forums by matching keywords. Potential customers can also be discovered and then the system will allow marketer to interact with those potential customers by sending message or posting response. The system architecture will be introduced in this paper as well as the wireframes of the system will also be introduced to explain the functions and abilities of the system.
Isometric Energies for Recovering Injectivity in Constrained Mapping

Computing injective maps with low distortions is a long-standing problem in computer graphics. Such maps are particularly challenging to obtain in the presence of positional constraints, because an injective initial map is often not available. Recently, several energies were proposed and shown to be highly successful in optimizing injectivity from non-injective initial maps while satisfying positional constraints. However, minimizing these energies tends to produce elements with significant isometric distortions. This paper presents simple variants of these energies that retain their desirable traits while promoting isometry. While our method is not guaranteed to provide an injective map, we observe that, on large-scale 2D and 3D data sets, minimizing the proposed isometric variants results in a similar level of success in recovering injectivity as the original energies but a significantly lower isometric distortion.
Investigating the Dynamic Relationship Between Social Distancing Restrictions and the Adoption of Mobile Money Services in the Middle East: A Panel Data Analysis

This article aims to investigate how social distancing restrictions can be a potential factor to effect on the adoption of mobile money services (MMS) using panel data from seven countries in the Middle East (Egypt, Iraq, Jordan, Morocco, Palestinian territories, Qatar and the United Arab Emirates) during the period from January to December 2020. Employing panel data analysis, the author uses various measures from the global mobile money dataset GSMA and Google Coronavirus Disease 2019 (COVID-19) Community Mobility Reports. The findings reveal that the social distancing restrictions have begun to play an essential part in the shift to cashless payment due to the epidemic and influence the adoption and use of MMS. The results also suggest that the social distancing restrictions affect consumer adoption of MMS and indirectly affect retailers. Unlike prevailing studies, this study is unique in empirically investigating the association between COVID-19 community mobility and MMS adoption. This study contributes to financial studies on MMS adoption under extreme settings.
Red organic light-emitting diodes using an emitting assist dopant

We propose an emitting assist (EA) dopant system for obtaining organic light-emitting diodes (OLEDs) with pure red emission. The EA dopant (rubrene) did not itself emit but assisted the energy transfer from the host (Alq3) to the red emitting dopant (DCM2). The cell structure used was {indium tin oxide/hole injection layer [(20 nm), CuPc/hole transport layer (50 nm), NPB/emitting layer (40 nm), Alq3+DCM2 (2%)+rubrene (5 wt %)]/MgIn}. (CuPc: Copper (II) phthalocyanine, NPB: N, N′-Di(naphthalen-1-yl)-N, N′-diphenyl-benzidine, DCM2: 4-Dicyanomethylene- 2-methyl-6-[2-(2,3,6,7-tetrahydro-1H,5H-benzo[ij]quinolizin-8-yl)vinyl]-4H-pyran). A stable red emission (chromaticity coordinates: x=0.64, y=0.36) was obtained in this cell within the luminance range of 100–4000 cd/m2. When the cell was not doped with rubrene, the emission color changed from red to orange as the luminance increased. The EA dopant system is a promising method for obtaining red OLEDs.
A Mixed Integer Approach for the Transient Case of Gas Network Optimization

Natural gas is the third most important energy source in the world. Presently, the consumption of natural gas is increasing the most in comparison to other non-renewable energy sources. Therefore, optimization of gas transport in networks poses a very important industrial problem. In this thesis we consider the problem of time-dependent optimization in gas networks, also called Transient Technical Optimization (TTO). A gas network consists of a set of pipes to transport the gas from the suppliers to the consumers. Due to friction with the pipe walls gas pressure gets lost. This pressure loss is compensated by so called compressors. The aim of TTO is to minimize the fuel consumption of the compressors, where the demands of consumers have to be satisfied. Transient optimization of gas transmission is one of the great research challenges in this area. We formulate a mixed integer approach for the problem of TTO which concentrates on time-dependent and discrete aspects. Thereby, the nonlinearities resulting from physical constraints are approximated using SOS (Special Ordered Set) conditions. A branch-and-cut algorithm is developed which guarantees global optimality in dependence on the approximation accuracy. Concerning the nonlinearities, we discuss the quality of approximation grids by calculating approximation errors. The SOS conditions are implicitly handled via a branching scheme, supported by adequate preprocessing techniques. A heuristic approach based on simulated annealing yields an upper bound in our branch-and-cut framework. To improve the lower bound, we incorporate two separation algorithms. The first one results from theoretical studies of the so called switching polytopes which are defined by runtime conditions and switching processes of compressors. Linking of different SOS conditions gives a second separation strategy. We present theoretical investigations of the SOS 2 and SOS 3 polytope. These polytopes arise from the modeling of SOS Type 2 and SOS Type 3 conditions using additional binary variables. The results do not have practical relevance for our solution algorithm, but we characterize facet-defining inequalities providing complete linear descriptions of these polytopes. We evaluate the developed branch-and-cut algorithm using three test networks provided by our project partner E.ON Ruhrgas AG. Two are of artificial nature, as they were developed for test purposes. They contain all important elements of a gas network, but are rather small. The third network characterizes the major part of the Ruhrgas AG network in Western Germany. We test instances from three up to 24 coupled time steps.
Evolution along allometric lines of least resistance: Morphological differentiation in Pristurus geckos

Species living in distinct habitats often experience unique ecological selective pressures, which can drive phenotypic divergence. However, how ecophenotypic patterns are affected by allometric trends and trait integration levels is less well understood. Here we evaluate the role of allometry in shaping body size and shape diversity in Pristurus geckos utilizing differing habitats. We found that patterns of body shape allometry and integration were distinct in species with different habitat preferences, with ground-dwelling Pristurus displaying the most divergent allometric trend and the strongest integration. There was also strong concordance between static allometry across individuals and evolutionary allometry among species, revealing that body shape differences among individuals were predictive of evolutionary changes across the phylogeny at macroevolutionary scales. This suggested that phenotypic evolution occurred along allometric lines of least resistance, with allometric trajectories imposing a strong influence on the magnitude and direction of size and shape changes across the phylogeny. When viewed in phylomorphospace, the largest rock-dwelling species were most similar in body shape to the smallest ground-dwelling species, and vice versa. Thus, in Pristurus, phenotypic evolution along the differing habitat-based allometric trajectories resulted in similar body shapes at differing body sizes in distinct ecological habitats.
Generate a new crucian carp (Carassius auratus) strain without intermuscular bones by knocking out bmp6

Elimination of intermuscular bones (IMBs) is vital to the aquaculture industry of cyprinids. In our previous study, we characterized bmp6 as essential in the development of IMBs in zebrafish. Knockout of bmp6 results in the absence of IMBs in zebrafish without affecting growth and reproduction. Therefore, we hypothesized that bmp6 could be used to generate new cyprinid strains without IMBs by gene editing. In this study, we established a gene editing strategy for knocking out the two orthologs of bmp6 in diploid crucian carp (Carassius auratus). We obtained an F3 population with both orthologs knocked out, in which no IMBs were detected by bone staining and X-ray, indicating that the new strain without IMBs (named WUCI strain) was successfully generated. Furthermore, we extensively evaluated the performance of the new strain in growth, reproduction, nutrient components, muscle texture and structure, and metabolites in muscle. The results showed that the WUCI strain grew faster than the wild-type crucian carp at 4-month-age. The reproductive performance and flesh quality did not show significant differences between the WUCI strain and wild-type crucian carp. Moreover, the metabolomics analysis suggested that the muscle tissues of the WUCI strain significantly enriched some metabolites belonging to the Thiamine metabolism, Nicotinate and Nicotinamide Metabolism pathway, which plays beneficial effects in anti-aging, anti-oxidant, and anti-radiation damage. In conclusion, we established a strategy to eliminate IMBs in crucian carp and obtain a WUCI strain whose performance was expected compared to the wild-type crucian carp; meanwhile, the WUCI strain enriched some beneficial metabolites to human health in muscle tissue. This study is the first report that a farmed cyprinid strain without IMBs, which could be stably inherited, was obtained worldwide; it provided excellent germplasm for the cyprinids aquaculture industry and a useful molecular tool for eliminating IMBs in other cyprinids.
Chemosensory afference in the tentacle nerve of Lymnaea stagnalis

Although the neural control of behavior has been extensively studied in gastropods, basic gaps remain in our understanding of how sensory stimuli are processed. In particular, there is only patchy evidence regarding the functional roles of sense organs and the extensive peripheral nervous system they contain. Our goal was to use extracellular electrophysiological recordings to confirm the chemosensory role of the tentacles in the great pond snail, Lymnaea stagnalis. Employing a special twin channel suction electrode to improve signal-to-noise ratio, we applied three food odours (derived from earthworm-based food pellets, algae-based pellets, and fresh lettuce) to a reduced preparation of the tentacle while recording neuronal activity in the tentacle nerve. Responses were assessed by comparing average spike frequencies produced in response to saline flow with and without odours. Confirming the omnivorous nature of this gastropod, we report strong neuronal responses to earthworm-based food odours and mild neuronal responses to algae-based food odours. There were no clear neuronal responses produced when lettuce food odour or control saline was applied to the tentacle. Overall, our results provide strong evidence for the chemosensory role of the tentacles in L. stagnalis. While it is unclear whether the differences in neuronal responses can be explained by differing sizes, numbers, or populations of neurons, these results are a useful foundation for further study of peripheral nervous system function in gastropods. Summary Statement Great pond snail tentacles send sensory signals to the brain in response to some (but not all) food odours.
Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially-resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected and preprocessed Visium spatial transcriptomics data (17,943 genes at up to 5,000 spots per patient sampled in a honeycomb pattern) from tissue across four stage-III matched colorectal cancer patients. We compare and prototype several convolutional, Transformer, and graph convolutional neural networks to predict spatial RNA patterns under the hypothesis that the transformer and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model’s ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, results indicate that the transformer and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications.