Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Demand CMOS-Compatible Fabrication of Ultrathin Self-Aligned SiC Nanowire Arrays

The field of semiconductor nanowires (NWs) has become one of the most active and mature research areas. However, progress in this field has been limited, due to the difficulty in controlling the density, orientation, and placement of the individual NWs, parameters important for mass producing nanodevices. The work presented herein describes a novel nanosynthesis strategy for ultrathin self-aligned silicon carbide (SiC) NW arrays (≤ 20 nm width, 130 nm height and 200–600 nm variable periodicity), with high quality (~2 Å surface roughness, ~2.4 eV optical bandgap) and reproducibility at predetermined locations, using fabrication protocols compatible with silicon microelectronics. Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, ultraviolet-visible spectroscopic ellipsometry, atomic force microscopy, X-ray diffractometry, and transmission electron microscopy studies show nanosynthesis of high-quality polycrystalline cubic 3C-SiC materials (average 5 nm grain size) with tailored properties. An extension of the nanofabrication process is presented for integrating technologically important erbium ions as emission centers at telecom C-band wavelengths. This integration allows for deterministic positioning of the ions and engineering of the ions’ spontaneous emission properties through the resulting NW-based photonic structures, both of which are critical to practical device fabrication for quantum information applications. This holistic approach can enable the development of new scalable SiC nanostructured materials for use in a plethora of emerging applications, such as NW-based sensing, single-photon sources, quantum LEDs, and quantum photonics.
Dynamic planar map lllustration

There are many types of illustrations that are easier to create in planar-map-based illustration systems than in the more common stacking-based systems. One weakness shared by all existing planar-map-based systems is that the editability of the drawing is severely hampered once coloring has begun. The paths that define the areas to be filled become divided wherever they intersect, making it difficult or impossible to edit them as a whole. Live Paint is a new metaphor that allows planar-map-based coloring while maintaining all the original paths unchanged. When a user makes a change, the regions and edges defined by the new paths take on fill and stroke attributes from the previous regions and edges. This results in greater editing flexibility and ease of use. Live Paint uses a set of heuristics to match each region and edge in a changed illustration with a region or edge in the previous version, a task that is more difficult than it at first appears. It then transfers fill and stroke attributes accordingly.
bíogo/hts: high throughput sequence handling for the Go language

provides a Go native implementation of the SAM specification (Group for SAM and BAM alignment formats commonly used for representation of high throughput genomic data, the BAI, CSI and tabix indexing formats, and the BGZF blocked compression format. The bíogo/hts packages perform parallelized read and write operations and are able to cache recent reads according to user-specified caching methods. The parallelisation approach used by the bíogo/hts package is influenced by the approach of the D implementation, sambamba by Tarazov et al. A. et al. 2015). The bíogo/hts APIs have been constructed to provide a consistent interface to sequence alignment data and the underlying compression system in order to aid ease of use and tool development.
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, these methods are susceptible to the presence of sharp road curves, challenging traffic conditions, strong shadows, and severe lighting changes. In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction. Specifically, we train our steering angle predictive model by distilling multi-layer knowledge from multiple heterogeneous auxiliary networks that perform related but different tasks, e.g., image segmentation or optical flow estimation. As opposed to multi-task learning, our method does not require expensive annotations of related tasks on the target set. This is made possible by applying contemporary off-the-shelf networks on the target set and mimicking their features in different layers after transformation. The auxiliary networks are discarded after training without affecting the runtime efficiency of our model. Our approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming the previous best by a large margin of 12.8% and 52.1%1, respectively. Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.
The Integration of Uavand Backpack Lidar Systems for Forest Inventory

Light detection and ranging (Lidar) has been demonstrated with strong capability in capturing three-dimensional (3D) forest structures. The reducing cost and weight of lidar sensors and the development of positioning systems make light-weighted near-surface lidar platforms, such as unmanned aerial vehicle (UAV) and backpack, become possible. These light-weighted lidar platforms have great potentials to be used as complimentary tools to improve both the accuracy and efficiency of forest inventory. In this study, we presented our self-developed low-cost UAV lidar system and backpack lidar system. A kinetic calibration algorithm and an improved 3D simultaneous localization and mapping algorithm were used to improve the positioning accuracy of the UAV lidar system and backpack lidar system, respectively. We then explored the possibility to automatically register backpack lidar data with UAV lidar data in a conifer forest using a two-step method based on segmented individual tree locations and the iterative closest point algorithm. The results of this study can provide guidance for a systematic lidar solution in forest inventory.
Ku and X-Band Scatterometer Observations of Deep Snow at Snowex 2017: Polarimetric Responses to Microstructure Controls

Radar measurements of accumulated snow using a Ku and X-band scatterometer instrument system were conducted at Grand Mesa, Colorado during the SnowEx 2017 field experiment in February 2017. Coincident snowpack measurements to the radar observations were made to characterize snow stratigraphy, snow density, snow grain structure, and snow thermodynamic properties. Eight sites were observed with the UWScat systems including two sites from a platform positioned 9 m above the ground and adjacent to a woodlot. The 6 non-wooded sites show consistent behavior of Ku and X-band power response (backscatter) compared with previous field studies. The first of these 6 sites was characterized by a wet snowpack response, while the other 5 had strong volume scattering response from the dry snowpack. The forest response is complex and requires further analysis to better understand the radar response from the woody biomass. The paper demonstrates and explores several response types from the radar observations of snow, including the backscatter power response, the polarimetric responses, all of which shed light on the Ku and X-band response from the ~2m snowpack.
Deep Tensor Factorization for Hyperspectral Image Classification

High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral data sets show the superiority of the proposed method.
Enhancing Customer Response Capability through Organizational Knowledge Resources in Service Encounters

Considering that a firm’s service competitiveness comes from customer service representatives (CSRs)’ capability, it is essential to enhance their capability first to create superior customer service. Accordingly, this study provides a way of creating CSRs’ capability by proving how organizational knowledge accumulated in knowledge management systems influences their customer response expertise and speed. Moreover, drawing on studies of organization memory (OM), it is defined by the three dimensions of OM level, OM dispersion, and OM usability. To test the proposed model and hypotheses, this study conducted the structural equation modeling analysis using a total of 373 responses collected on CSRs. The results indicate that both OM level and dispersion greatly increase customer response expertise. Besides, OM level, dispersion, and usability enhance customer response speed. Particularly, OM usability is the most effective in improving customer response speed. Finally, CSRs’ service performance depends on customer response expertise and speed. this study draws and proves the constructs that can capture CSRs’ abilities (i.e., customer response expertise and speed), which are needed to provide good service in call centers, from the study of Jayachandran et al. (2004). Their study has been also applied in following studies that focus on a firm’s agility (Roberts and Grover, 2012; Setia et al., 2013). Furthermore, this study proved the effectiveness of two constructs in improving service performance.
Long-Wave Hyperspectral Imaging for Lithological Mapping: A Case Study

Hyperspectral long-wave infrared imaging (LWIR HSI) adds a promising complement to visible, near infrared, and shortwave infrared (VNIR and SWIR) HSI data in the field of mineral mapping. It enables characterization of rock-forming minerals such as silicates and carbonates, which show no detectable or extremely weak features in VNIR and SWIR In the last decades, there has been a steady increase of publications on satellite, aerial, and laboratory LWIR data. However, the application of LWIR HSI for ground-based, close-range remote sensing of vertical geological outcrops is sparsely researched and will be the focus of the current study. We present a workflow for acquisition, mosaicking, and radiometric correction of LWIR HSI data. We demonstrate the applicability of this workflow using a case study from a gravel quarry in Germany. Library spectra are used for spectral unmixing and mapping of the main lithological units, which are validated using sample X-ray diffraction (XRD) and thin section analysis as well as FTIR point spectrometer data.
The Nedd8-activating enzyme inhibitor MLN4924 suppresses colon cancer cell growth via triggering autophagy

Neddylation is a post-translational protein modification process. MLN4924 is a newly discovered pharmaceutical neddylation inhibitor that suppresses cancer growth with several cancer types. In our study, we first investigated the effect of MLN4924 on colon cancer cells (HCT116 and HT29). MLN4924 significantly inhibited the neddylation of cullin-1 and colon cancer cell growth in a time and dose-dependent manner. MLN4924 induced G2/M cell cycle arrest and apoptosis in HCT116 and HT29 cells. Moreover, MLN4924 also triggered autophagy in HCT116 and HT29 cells via suppressing the PI3K/AKT/mTOR pathway. Inhibiting autophagy by autophagy inhibitor 3-MA or ATG5 knockdown reversed the function of MLN4924 in suppressing colon cancer cell growth and cell death. Interestingly, MLN4924 suppresses colon cell growth in a xenograft model. Together, our finding revealed that blocking neddylation is an attractive colon cancer therapy strategy, and autophagy might act as a novel anti-cancer mechanism for the treatment of colon cancer by MLN4924.
Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals

The increasing availability of “big” (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e., positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.
AIS Electronic Library (AISeL)

Organizations need to innovate to be competitive and Information Technology (IT) is considered as a strategic tool for this purpose. The present study anchors on the organizational learning framework to theorize that an organization could gain competitive advantage through two activities, namely open innovation (consisting of inbound and outbound strategies) and management innovation. Next, IT capability plays an important moderating role in the relationship between innovation strategy and competitive advantage. We validate our theorization using a set of matched-sample survey data that contain responses from managerial-level executives of 112 manufacturing and service organizations. Theoretical and practical implications are discussed.
Urban Impervious Surface Extraction Based on the Integration of Remote Sensing Images and Social Media Data

This paper presents an inspiring approach for accurate estimation of impervious surfaces, which exploits the strength of two kind of heterogeneous features, i.e., physical features derived from satellite images and social features derived from social media datasets, respectively. On the one hand, we use a morphological attribute profiles guided spectral mixture analysis model to achieve estimates of physical features. On the other hand, we mine the social features from textual information of social media datasets. Then, a multivariable linear regression model is conducted to obtain the impervious surfaces. Experiment results, conducted with multi-spectral images collected by LANDSAT-8 and social media datasets scraped from Sina Weibo of Guangzhou city, suggest that our approach could lead to reliable and good estimation of the imperviousness.
A Modified Wave Spectrum for Modeling in Remote Sensing Problems

In this paper we present a new directional spectrum based on both oceanological and remote sensing data. The model is compared with popular ocean wave models used for remote sensing problems of T. Elfouhaily, P.A. Hwang and V. Kudryavtsev. It is shown that the modified wave spectrum is a good choice for L band RCS modeling for moderate incidence angles. It is also shown that the modeling results with the modified wave spectrum are in agreement with experimental data. In a region after spectral peak the model is presented in a form of power-law dependency of spectral density on frequency, where power is different for different wave ranges. It allows carrying out analytical transformations easily. An asymmetrical angular spreading function is used in a spectral model.
Dynamic Resource Management for Heterogeneous Many-Cores

With the advent of many-core systems, use cases of embedded systems have become more dynamic: Plenty of applications are concurrently executed, but may dynamically be exchanged and modified even after deployment. Moreover, resources may temporally or permanently become unavailable because of thermal aspects, dynamic power management, or the occurrence of faults. This poses new challenges for reaching objectives like timeliness for real-time or performance for best-effort program execution and maximizing system utilization. In this work, we first focus on dynamic management schemes for reliability/aging optimization under thermal constraints. The reliability of on-chip systems in the current and upcoming technology nodes is continuously degrading with every new generation because transistor scaling is approaching its fundamental limits. Protecting systems against degradation effects such as circuits' aging comes with considerable losses in efficiency. We demonstrate in this work why sustaining reliability while maximizing the utilization of available resources and hence avoiding efficiency loss is quite challenging – this holds even more when thermal constraints come into play. Then, we discuss techniques for run-time management of multiple applications which sustain real-time properties. Our solution relies on hybrid application mapping denoting the combination of design-time analysis with run-time application mapping. We present a method for Real-time Mapping Reconfiguration (RMR) which enables the Run-Time Manager (RM) to execute realtime applications even in the presence of dynamic thermal- and reliability-aware resource management. This paper is paper of the ICCAD 2018 Special Session on “Managing Heterogeneous Many-cores for High-Performance and Energy-Efficiency”. The other two papers of this Special sessions are [1] and [2].
Incorporating Pairwise Learning into Latent Dirichlet Allocation for Effective Item Recommendations

Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept of pairwise learning into the latent Dirichlet allocation model to discover user preferences which differentiate users’ precedence on items. A voting mechanism applied to the learned user preferences is devised so that favorite items are suggested to the users. Preliminary experiments based on a real-world dataset demonstrate that the discovered user preferences are effective in item recommendations. Also, incorporating pairwise learning successfully enhances the LDA-based recommendation method in terms of the recommendation precision and coverage rate at H.
Chlamydia trachomatis Inhibits Homologous Recombination Repair of DNA Breaks by Interfering with PP2A Signaling

Chlamydia trachomatis induces DNA double-strand breaks in host cells but simultaneously inhibits proper DNA damage response and repair mechanisms. This may render host cells prone to loss of genetic integrity and transformation. Here we show that C. trachomatis prevents activation of the key DNA damage response mediator ATM by preventing the release from PP2A, leading to a complete absence of homologous recombination repair in host cells. ABSTRACT Cervical and ovarian cancers exhibit characteristic mutational signatures that are reminiscent of mutational processes, including defective homologous recombination (HR) repair. How these mutational processes are initiated during carcinogenesis is largely unclear. Chlamydia trachomatis infections are epidemiologically associated with cervical and ovarian cancers. Previously, we showed that C. trachomatis induces DNA double-strand breaks (DSBs) but suppresses Ataxia-telangiectasia mutated (ATM) activation and cell cycle checkpoints. The mechanisms by which ATM regulation is modulated and its consequences for the repair pathway in C. trachomatis-infected cells remain unknown. Here, we found that Chlamydia bacteria interfere with the usual response of PP2A to DSBs. As a result, PP2A activity remains high, as the level of inhibitory phosphorylation at Y307 remains unchanged following C. trachomatis-induced DSBs. Protein-protein interaction analysis revealed that C. trachomatis facilitates persistent interactions of PP2A with ATM, thus suppressing ATM activation. This correlated with a remarkable lack of homologous recombination (HR) repair in C. trachomatis-infected cells. Chemical inhibition of PP2A activity in infected cells released ATM from PP2A, resulting in ATM phosphorylation. Activated ATM was then recruited to DSBs and initiated downstream signaling, including phosphorylation of MRE11 and NBS1 and checkpoint kinase 2 (Chk2)-mediated activation of the G2/M cell cycle checkpoint in C. trachomatis-infected cells. Further, PP2A inhibition led to the restoration of C. trachomatis-suppressed HR DNA repair function. Taking the data together, this study revealed that C. trachomatis modulates PP2A signaling to suppress ATM activation to prevent cell cycle arrest, thus contributing to a deficient high-fidelity HR pathway and a conducive environment for mutagenesis. IMPORTANCE Chlamydia trachomatis induces DNA double-strand breaks in host cells but simultaneously inhibits proper DNA damage response and repair mechanisms. This may render host cells prone to loss of genetic integrity and transformation. Here we show that C. trachomatis prevents activation of the key DNA damage response mediator ATM by preventing the release from PP2A, leading to a complete absence of homologous recombination repair in host cells.
Effect of the diets with pumpkin silage and synthetic &bgr;‐carotene on the carotenoid, immunoglobulin and bioactive protein content and fatty acid composition of colostrum

Forty Simmental cows were divided into four groups with 10 per group to investigate the effect of a transition diet supplemented with synthetic β-carotene and natural carotenoid-rich feed (pumpkin silage) on chemical composition of the colostrum. The control group (I) was fed grass and maize silage and supplemental concentrates, group II additionally received 400 mg/day/cow of synthetic β-carotene, in group, III 40% DM of maize silage was replaced with pumpkin silage to increase β-carotene intake by 400 mg in relation to group I, and in group IV 60% DM of maize silage was replaced with pumpkin silage without adjusting for β-carotene. Colostrum was collected from the cows within 5 hr of calving to determine colostrum composition, the content of immunoglobulins and carotenoids, total antioxidant status (TAS), the content of some bioactive proteins and the composition of fatty acids. The study showed that the experimental diets had no effect on the gross composition of colostrum. The content of α-carotene, β-carotene and violaxanthin was significantly higher in group IV than in group I and that of lutein higher in group IV than in groups I and II. Total antioxidant status of colostrum in group IV was significantly higher than in group I. Groups III and IV were characterized by a higher concentration of immunoglobulin IgG in relation to group I. IgM level in groups II, III and IV was significantly higher than in group I and that in groups III and IV was also higher than in group II. The highest IgA concentration was observed in group IV and the lowest in group I. Lysozyme concentration was higher in group IV compared to groups I and II. The diets had no effect on the total content of SFA, MUFA and PUFA in colostrum.