Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a plasmonic back reflector using Ag nanoparticles with a mirror support for an a-Si:H solar cell

Plasmonic nanoparticles have unique optical properties and these properties are affected by any surrounding structures, or lack thereof. Nanoparticles are often added to a device without fully assessing the effect that each interface will have on the nanoparticle’s response. In this work, we simulate and fabricate devices utilizing hemispherical nanoparticles integrated into the back reflector of an amorphous silicon solar cell. 3D finite difference time domain simulations were used to calculate the optical absorption of a 300nm amorphous silicon layer as a function of the size of the nanoparticles, the distance between the nanoparticles and the active layer, and the distance between the nanoparticles and the mirror. Two transparent conducting oxides, aluminum doped zinc oxide and indium tin oxide, are investigated to determine the importance of the material properties between the nanoparticles and mirror. Silver hemispherical nanoparticles with a diameter of 150nm placed directly on the a-Si:H and a 60nm.
ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats

Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%. Furthermore, we apply ISPY on three new communities to extensively evaluate ISPY’s practical usage. Moreover, we publish over 30K issue-solution pairs extracted from 11 communities. We believe that ISPY can facilitate community-based software development by promoting knowledge sharing and shortening the issue-resolving process.
Charge density wave breakdown in a heterostructure with electron-phonon coupling

Understanding the influence of vibrational degrees of freedom on transport through a heterostructure poses considerable theoretical and numerical challenges. In this work, we use the density-matrix renormalization group (DMRG) method together with local basis optimization (LBO) to study the half-filled Holstein model in the presence of a linear potential, either isolated or coupled to tight-binding leads. In both cases, we observe a decay of charge-density-wave (CDW) states at a sufficiently strong potential strength. Local basis optimization selects the most important linear combinations of local oscillator states to span the local phonon space. These states are referred to as optimal modes. We show that many of these local optimal modes are needed to capture the dynamics of the decay, that the most significant optimal mode on the initially occupied sites remains well described by a coherent-state typical for small polarons, and that those on the initially empty sites deviate from the coherent-state form. Additionally, we compute the current through the structure in the metallic regime as a function of voltage. For small voltages, we reproduce results for the Luttinger parameters. As the voltage is increased, the effect of larger electron-phonon coupling strengths becomes prominent. Further, the most significant optimal mode remains almost unchanged when going from the ground state to the current-carrying state in the metallic regime.
Electric Trapping and Cooling of Polyatomic Molecules

The present thesis focuses on developing a general method to trap and to cool molecules to ultralow temperatures (<1mK). The implemented opto-electrical Sisyphus cooling uses state-dependent potentials combined with optical pumping to achieve this goal. In a first experiment, a temperature reduction by a factor of 5 was achieved. A key part of the experiment is the realization of a novel design for an electric trap for molecules. This allowed molecules to be trapped with a lifetime of almost 30s.
Neural network optimal feedback control with enhanced closed loop stability

Recent research has shown that supervised learning can be an effective tool for designing optimal feedback controllers for high-dimensional nonlinear dynamic systems. But the behavior of these neural network (NN) controllers is still not well understood. In this paper we use numerical simulations to demonstrate that typical test accuracy metrics do not effectively capture the ability of an NN controller to stabilize a system. In particular, some NNs with high test accuracy can fail to stabilize the dynamics. To address this we propose two NN architectures which locally approximate a linear quadratic regulator (LQR). Numerical simulations confirm our intuition that the proposed architectures reliably produce stabilizing feedback controllers without sacrificing optimality.
Multi-View Spatial-Temporal Model for Travel Time Estimation

Taxi arrival time prediction is essential for building intelligent transportation systems. Traditional prediction methods mainly rely on extracting features from traffic maps, which cannot model complex situations and nonlinear spatial and temporal relationships. Therefore, we propose Multi-View Spatial-Temporal Model (MVSTM) to capture the mutual dependence of spatial-temporal relations and trajectory features. Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics. Experiments on large-scale taxi trajectory data have shown that our approach is more effective than the existing novel methods. The source code can be found at https://github.com/775269512/SIGSPATIAL-2021-GISCUP-4th-Solution.
Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity makes it time consuming. It is an operation that requires about 80-120 hours per hectare annually, making an automated robotic system that helps in speeding up the process a crucial tool in large-size vineyards. We will describe (a) a novel expert annotated dataset for grapevine segmentation, (b) a state of the art neural network implementation and (c) generation of pruning points following agronomic rules, leveraging the simplified structure of the plant. With this approach, we are able to generate a set of pruning points on the canes, paving the way towards a correct automation of grapevine winter pruning.
Whole-Body Control with Motion/Force Transmissibility for Parallel-Legged Robot

For achieving kinematically suitable configurations and highly dynamic task execution, an efficient way is to consider robot performance indices in the whole-body control (WBC) of robots. However, current WBC methods have not considered the intrinsic features of parallel robots, especially motion/force transmissibility (MFT). This paper proposes an MFT-enhanced WBC scheme for parallel-legged robots. Introducing the performance indices of MFT into a WBC is challenging due to the nonlinear relationship between MFT indices and the robot configuration. To overcome this challenge, we establish the MFT preferable space of the robot offline and formulate it as a polyhedron in the joint space at the acceleration level. Then, the WBC employs the polyhedron as a soft constraint. As a result, the robot possesses high-speed and high-acceleration capabilities by satisfying this constraint. The offline preprocessing relieves the online computation burden and helps the WBC achieve a 1kHz servo rate. Finally, we validate the performance and robustness of the proposed method via simulations and experiments on a parallel-legged bipedal robot.
Molecular dynamics study of CO2 absorption and desorption in zinc imidazolate frameworks

We report a study of the absorption of CO2 into a number of zinc imidazolate framework structures, and subsequent desorption, using the molecular dynamics simulation method with force fields partly developed by ourselves. The simulations primarily give results concerning the mechanism of CO2 absorption under conditions likely to be found in industrial waste gas streams. In particular we compare the rate of uptake of CO2 for different ZIFs. We also show that it is possible to observe desorption by reduced pressure and high temperature. These characteristics confirm that ZIFs might be suitable for CO2 absorption within an industrial capture and sequestration process.
Comparing decision mining approaches with regard to the meaningfulness of their results

Decisions and the underlying rules are indispensable for driving process execution during runtime, i.e., for routing process instances at alternative branches based on the values of process data. Decision rules can comprise unary data conditions, e.g., age>40, binary data conditions where the relation between two or more variables is relevant, e.g. temperature1<temperature2, and more complex conditions that refer to, for example, parts of a medical image. Decision discovery aims at automatically deriving decision rules from process event logs. Existing approaches focus on the discovery of unary, or in some instances binary data conditions. The discovered decision rules are usually evaluated using accuracy, but not with regards to their semantics and meaningfulness, although this is crucial for validation and the subsequent implementation/adaptation of the decision rules. Hence, this paper compares three decision mining approaches, i.e., two existing ones and one newly described approach, with respect to the meaningfulness of their results. For comparison, we use one synthetic data set for a realistic manufacturing case and the two real-world BPIC 2017/2020 logs. The discovered rules are discussed with regards to their semantics and meaningfulness.
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation

Pre-trained language models have shown remarkable results on various NLP tasks. Nevertheless, due to their bulky size and slow inference speed, it is hard to deploy them on edge devices. In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA) since the computational cost of FFN is 2$\sim$3 times larger than MHA. Hence, to compact BERT, we are devoted to designing efficient FFN as opposed to previous works that pay attention to MHA. Since FFN comprises a multilayer perceptron (MLP) that is essential in BERT optimization, we further design a thorough search space towards an advanced MLP and perform a coarse-to-fine mechanism to search for an efficient BERT architecture. Moreover, to accelerate searching and enhance model transferability, we employ a novel warm-up knowledge distillation strategy at each search stage. Extensive experiments show our searched EfficientBERT is 6.9$\times$ smaller and 4.4$\times$ faster than BERT$\rm_{BASE}$, and has competitive performances on GLUE and SQuAD Benchmarks. Concretely, EfficientBERT attains a 77.7 average score on GLUE \emph{test}, 0.7 higher than MobileBERT$\rm_{TINY}$, and achieves an 85.3/74.5 F1 score on SQuAD v1.1/v2.0 \emph{dev}, 3.2/2.7 higher than TinyBERT$_4$ even without data augmentation. The code is released at https://github.com/cheneydon/efficient-bert.
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descriptive categories. However, such a representation is not directly interpretable for decision-making and constrains robot operation to a specific domain. Thus, we propose to segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. Building around an image segmentation network, we present a generic affordance consisting of 3 driveability levels which can broadly apply to both urban and off-road scenes. By encoding these levels with soft ordinal labels, we incorporate inter-class distances during learning which improves segmentation compared to standard “hard” one-hot labelling. In addition, we propose a navigation-oriented pixel-wise loss weighting method which assigns higher importance to safety-critical areas. We evaluate our approach on large-scale public image segmentation datasets ranging from sunny city streets to snowy forest trails. In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.
Activity induced turbulence in driven active matter

Turbulence in driven stratified active matter is considered. The relevant parameters characterizing the problem are the Reynolds number Re / uL   and an active matter Richardson-like number, 2 / R S L Du   . Here u is the mean velocity of flow, L is the system size,  is the kinematic viscosity,  is the activity coefficient, S is the concentration gradient and D is the active matter diffusion coefficient. In the mixing limit, Re 1, 1 R   , we show that the standard Kolmogorov energy spectrum law, 5/3 ( ) E k k   , is realized. On the other hand, in the stratified limit, Re 1, 1 R   , there is a new turbulence universality class with 7/5 ( ) E k k  . The crossover from one regime to the other is discussed in detail. Experimental predictions and probes are also discussed.
Resource Utilization of Distributed Databases in Edge–Cloud Environment

A benchmark study of modern distributed databases (DDBs) (e.g., Cassandra, MongoDB, Redis, and MySQL) is an important source of information for selecting the right technology for managing data in edge–cloud deployments. While most of the existing studies have investigated the performance and scalability of DDBs in cloud computing, there is a lack of focus on resource utilization (e.g., energy, bandwidth, and storage consumption) of workload offloading for DDBs deployed in edge–cloud environments. For this purpose, we conducted experiments on various physical and virtualized computing nodes, including variously powered servers, Raspberry Pi, and hybrid cloud (OpenStack and Azure). Our extensive experimental results reveal insights into which database under which offloading scenario is more efficient in terms of energy, bandwidth, and storage consumption.
Hybrid Local-Global Transformer for Image Dehazing

Recently, the Vision Transformer (ViT) has shown impressive performance on high-level and low-level vision tasks. In this paper, we propose a new ViT architecture, named Hybrid Local-Global Vision Transformer (HyLoG-ViT), for single image dehazing. The HyLoG-ViT block consists of two paths, the local ViT path and the global ViT path, which are used to capture local and global dependencies. The hybrid features are fused via convolution layers. As a result, the HyLoG-ViT reduces the computational complexity and introduces locality in the networks. Then, the HyLoG-ViT blocks are incorporated within our dehazing networks, which jointly learn the intrinsic image decomposition and image dehazing. Speciﬁcally, the network consists of one shared encoder and three decoders for reﬂectance prediction, shading prediction, and haze-free image generation. The tasks of reﬂectance and shading prediction can produce meaningful intermediate features that can serve as complementary features for haze-free image generation. To effectively aggregate the complementary features, we propose a complementary features selection module (CFSM) to select the useful ones for image dehazing. Extensive experiments on homogeneous, non-homogeneous, and nighttime dehazing tasks reveal that our proposed Transformer-based dehazing network can achieve comparable or even better performance than CNNs-based dehazing models.
Far-ultraviolet Spectra of Main-sequence O Stars at Extremely Low Metallicity

Metal-poor massive stars dominate the light we observe from star-forming dwarf galaxies and may have produced the bulk of energetic photons that reionized the universe at high redshift. Yet, the rarity of observations of individual O stars below the 20% solar metallicity (Z ⊙) of the Small Magellanic Cloud (SMC) hampers our ability to model the ionizing fluxes of metal-poor stellar populations. We present new Hubble Space Telescope far-ultraviolet (FUV) spectra of three O-dwarf stars in the galaxies Leo P (3% Z ⊙), Sextans A (6% Z ⊙), and WLM (14% Z ⊙). We quantify equivalent widths of photospheric metal lines and strengths of wind-sensitive features, confirming that both correlate with metallicity. We infer the stars’ fundamental properties by modeling their FUV through near-infrared spectral energy distributions and identify stars in the SMC with similar properties to each of our targets. Comparing to the FUV spectra of the SMC analogs suggests that (1) the star in WLM has an SMC-like metallicity, and (2) the most metal-poor star in Leo P is driving a much weaker stellar wind than its SMC counterparts. We measure projected rotation speeds and find that the two most metal-poor stars have high vsin(i) ≥ 290 km s−1, and estimate just a 3%–6% probability of finding two fast rotators if the metal-poor stars are drawn from the same vsin(i) distribution observed for O dwarfs in the SMC. These observations suggest that models should include the impact of rotation and weak winds on ionizing flux to accurately interpret observations of metal-poor galaxies in both the near and distant universe.
Potential Impacts of Mass Nutritional Supplementation on Measles Dynamics: A Simulation Study

The bidirectional interaction between undernutrition and infection can be devastating to child health. Nutritional deficiencies stunt growth, impair immunity, increase susceptibility to both infection and more severe outcomes from common infections. Simultaneously, infections compound undernutrition by increasing metabolic demand, reducing food intake, and impairing nutrient absorption. Treatment of acute malnutrition (wasting) can reverse at least some of its deleterious effects and reduce susceptibility to, and transmission of, infectious diseases. Nutrition-specific approaches may be packaged with other interventions, including immunization, to support overall child health. To understand how mass nutritional supplementation, treatment of wasting, and vaccination affect the dynamics of a vaccine-preventable infection, we developed a population-level, compartmental model of measles transmission of children aged 6-23 months old, stratified by nutrition status. We simulated a range of scenarios to assess the potential reductions in measles infection and mortality associated with targeted therapeutic feeding for children who are wasted and with a mass supplementation intervention. Nutrition interventions were assumed to reduce susceptibility to measles-related death and to increase engagement with the health sector, leading to increased vaccination rates. We found that if all wasted children receive therapeutic feeding, and if this leads to an increase in measles vaccination uptake, up to 12%, 20%, and 23% reductions in measles infections, measles-attributable mortality, and overall mortality among wasted children, respectively, could be averted. Combination of wasting treatment and mass supplementation coverage followed by an increase in vaccination coverage of non-wasted children from a baseline of 75% to 85%, leads to a 64-66% reduction in measles infection and mortality and 27% reduction in overall mortality among wasted children, compared with the wasting treatment alone. Our work highlights the synergistic benefits that may be achieved by leveraging mass nutritional supplementation as a touch point with the health system, to increase rates of vaccination in order to improve child survival beyond what would be expected from the additive benefits of each intervention.
Timing and magnitude of climatic extremes differentially elevate mortality but enhance recovery in a fish population

The countervailing effects of disturbances (e.g., high mortality and enhanced recovery) on population dynamics can occur through demographic processes under rapidly increasing climatic extremes. Across an extreme‐event gradient, we mechanistically demonstrated how dramatic changes in streamflow have affected the population persistence of endangered salmon in monsoonal Taiwan over a three‐decade period. Our modeling indicated that the dynamics of the age‐structured population were attributed to demographic processes, in which extensive mortality was characterized as a function of climatic extremes and vulnerability in the young stage of fish. In the stochastic simulations, we found that the extensive mortality and high proportion of large fish resulted from extreme flooding, which caused high values of postimpact population recovery. Our empirical evidence suggests that the magnitudes and timing of disturbance can explain the population persistence when facing climatic extremes and thereby challenges the understanding of the mechanistic drivers of these countervailing phenomena under changing environmental conditions.
Development and evaluation of standardized pregnancy identification and trimester distribution algorithms in U.S. IBM MarketScan® Commercial and Medicaid data

Creation of new algorithms to identify pregnancies in automated health care claims databases is of public health importance, as it allows us to learn more about medication use and safety in a vulnerable population. Previous algorithms were largely created using international classification of disease codes, but despite the U.S. code transition in 2015, few algorithms are available with the latest ICD‐10‐CM codes.