Miracle-dz/UniFAR-FTUs huggingface dataset:
A new metal transfer process for van der Waals contacts to vertical Schottky-junction transition metal dichalcogenide photovoltaics

We develop a new technique for transferring metal contacts to create ultrathin solar cells from 2D materials. Two-dimensional transition metal dichalcogenides are promising candidates for ultrathin optoelectronic devices due to their high absorption coefficients and intrinsically passivated surfaces. To maintain these near-perfect surfaces, recent research has focused on fabricating contacts that limit Fermi-level pinning at the metal-semiconductor interface. Here, we develop a new, simple procedure for transferring metal contacts that does not require aligned lithography. Using this technique, we fabricate vertical Schottky-junction WS2 solar cells, with Ag and Au as asymmetric work function contacts. Under laser illumination, we observe rectifying behavior and open-circuit voltage above 500 mV in devices with transferred contacts, in contrast to resistive behavior and open-circuit voltage below 15 mV in devices with evaporated contacts. One-sun measurements and device simulation results indicate that this metal transfer process could enable high specific power vertical Schottky-junction transition metal dichalcogenide photovoltaics, and we anticipate that this technique will lead to advances for two-dimensional devices more broadly.
Influence of static disorder and polaronic band formation on the interfacial electron transfer in organic photovoltaic devices

Understanding the interfacial charge-separation mechanism in organic photovoltaics requires, due to its high level of complexity, bridging between chemistry and physics. To elucidate the charge separation mechanism, we present a fully quantum dynamical simulation of a generic one-dimensional Hamiltonian, which physical parameters model prototypical PCBM or $\text{C}_{60}$ acceptor systems. We then provide microscopic evidence of the influence random static and dynamic potentials have on the interfacial charge-injection rate. In particular, we unveil that dynamic potentials, due to strong electron-vibration interactions, can lead to the formation of polaronic bands. Such dynamical potentials, when compared to random static potentials, can provide the main detrimental influence on the efficiency of the process of interfacial charge-separation.
Cascade Feature Aggregation for Human Pose Estimation

Human pose estimation plays an important role in many computer vision tasks and has been studied for many decades. However, due to complex appearance variations from poses, illuminations, occlusions and low resolutions, it still remains a challenging problem. Taking the advantage of high-level semantic information from deep convolutional neural networks is an effective way to improve the accuracy of human pose estimation. In this paper, we propose a novel Cascade Feature Aggregation (CFA) method, which cascades several hourglass networks for robust human pose estimation. Features from different stages are aggregated to obtain abundant contextual information, leading to robustness to poses, partial occlusions and low resolution. Moreover, results from different stages are fused to further improve the localization accuracy. The extensive experiments on MPII datasets and LIP datasets demonstrate that our proposed CFA outperforms the state-of-the-art and achieves the best performance on the state-of-the-art benchmark MPII.
Recursive speech separation for unknown number of speakers

In this paper we propose a method of single-channel speaker-independent multi-speaker speech separation for an unknown number of speakers. As opposed to previous works, in which the number of speakers is assumed to be known in advance and speech separation models are specific for the number of speakers, our proposed method can be applied to cases with different numbers of speakers using a single model by recursively separating a speaker. To make the separation model recursively applicable, we propose one-and-rest permutation invariant training (OR-PIT). Evaluation on WSJ0-2mix and WSJ0-3mix datasets show that our proposed method achieves state-of-the-art results for two- and three-speaker mixtures with a single model. Moreover, the same model can separate four-speaker mixture, which was never seen during the training. We further propose the detection of the number of speakers in a mixture during recursive separation and show that this approach can more accurately estimate the number of speakers than detection in advance by using a deep neural network based classifier.
Evolution of Format Preserving Encryption on IoT Devices: FF1+

The Internet of Things (IoT) is a network of interconnected low-power sensing devices designed to interact and communicate with each other. To avoid compromising user privacy, it is necessary to encrypt these channels. We introduce Format Preserving Encryption (FPE), a modern cryptosystem that allows full customization of the ciphertext, while offering comparable security to AES. To gauge the performance of FPE, we compare the NIST-approved FF1 algorithm against several symmetric and asymmetric encryption schemes on a Raspberry Pi 3. While suitable for small plaintexts, FF1 breaks down for longer character strings. We propose a modified algorithm, FF1+, that implements dynamic round selection and key scheduling. Significant performance improvements are observed in our results, thus demonstrating FF1+ as a viable cryptosystem for IoT devices.
Unsupervised Time Series Extraction from Controller Area Network Payloads

This paper introduces a method for unsupervised tokenization of Controller Area Network (CAN) data payloads using bit level transition analysis and a greedy grouping strategy. The primary goal of this proposal is to extract individual time series which have been concatenated together before transmission onto a vehicle's CAN bus. This process is necessary because the documentation for how to properly extract data from a network may not always be available; passenger vehicle CAN configurations are protected as trade secrets. At least one major manufacturer has also been found to deliberately misconfigure their documented extraction methods. Thus, this proposal serves as a critical enabler for robust third-party security auditing and intrusion detection systems which do not rely on manufacturers sharing confidential information.
Mastering Software Robot Development Projects: Understanding the Association between System Attributes & Design Practices

Software robots tend to increasingly take over organizational processes. However, little is known about principles of developing as opposed to using robotic systems, such as RPA robots and chatbots. Therefore, based on a comparative case study, this paper elaborates how different types of robots, due to distinguishing system attributes, relate to different design practices that arise from varying challenges of transforming existing routines into robots.
Variational Uncalibrated Photometric Stereo Under General Lighting

Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable. To eliminate such restrictions, we propose an efficient principled variational approach to uncalibrated PS under general illumination. To this end, the Lambertian reflectance model is approximated through a spherical harmonic expansion, which preserves the spatial invariance of the lighting. The joint recovery of shape, reflectance and illumination is then formulated as a single variational problem. There the shape estimation is carried out directly in terms of the underlying perspective depth map, thus implicitly ensuring integrability and bypassing the need for a subsequent normal integration. To tackle the resulting nonconvex problem numerically, we undertake a two-phase procedure to initialize a balloon-like perspective depth map, followed by a “lagged” block coordinate descent scheme. The experiments validate efficiency and robustness of this approach. Across a variety of evaluations, we are able to reduce the mean angular error consistently by a factor of 2–3 compared to the state-of-the-art.
Crowdsourcing Convergence: Aggregating Partial Clusters to Facilitate Collaborative Convergence

This paper is an exploratory effort to investigate the possibility of using crowdsourcing to execute part of the collaborative convergence process. Participants were assigned with creating buckets or clusters from a random subset of the overall pool of brainstorming ideas. These sub-sorts were aggregated into a weighted graph and partitioned into discrete buckets. Analysis of this aggregated, consensus sort provides support that crowdsourcing may be a feasible option when organizing brainstorming ideas into discrete categories
Magnetic Switching in Vapochromic Oxalato-Bridged 2D Copper(II)-Pyrazole Compounds for Biogenic Amine Sensing

A new two-dimensional (2D) coordination polymer of the formula {Cu(ox)(4-Hmpz)·1/3H2O}n (1) (ox = oxalate and 4-Hmpz = 4-methyl-1H-pyrazole) has been prepared, and its structure has been determined by single-crystal X-ray diffraction. It consists of corrugated oxalato-bridged copper(II) neutral layers featuring two alternating bridging modes of the oxalate group within each layer, the symmetric bis-bidentate (μ-κ2O1,O2:κ2O2′,O1′) and the asymmetric bis(bidentate/monodentate) (μ4-κO1:κ2O1,O2:κO2′:κ2O2′,O1′) coordination modes. The three crystallographically independent six-coordinate copper(II) ions that occur in 1 have tetragonally elongated surroundings with three oxygen atoms from two oxalate ligands, a methylpyrazole-nitrogen defining the equatorial plane, and two other oxalate-oxygen atoms occupying the axial positions. The monodentate 4-Hmpz ligands alternatively extrude above and below each oxalate-bridged copper(II) layer, and the water molecules of crystallization are located between the layers. Compound 1 exhibits a fast and selective adsorption of methylamine vapors to afford the adsorbate of formula {Cu(ox)(4-Hmpz)·3MeNH2·1/3H2O}n (2), which is accompanied by a concomitant color change from cyan to deep blue. Compound 2 transforms into {Cu(ox)(4-Hmpz)·MeNH2·1/3H2O}n (3) under vacuum for three hours. The cryomagnetic study of 1–3 revealed a unique switching from strong (1) to weak (2 and 3) antiferromagnetic interactions. The external control of the optical and magnetic properties along this series of compounds might make them suitable candidates for switching optical and magnetic devices for chemical sensing.
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games

Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfect-information games, but it is still a challenge to achieve (nearly) optimal results (in other words, an approximate Nash Equilibrium) in large-scale imperfect-information games (i.e. war games, football coach or business strategies). Neural Fictitious Self Play (NFSP) is an effective algorithm for learning approximate Nash equilibrium of imperfect-information games from self-play without prior domain knowledge. However, it relies on Deep Q-Network, which is off-line and is hard to converge in online games with changing opponent strategy, so it can't approach approximate Nash equilibrium in games with large search scale and deep search depth. In this paper, we propose Monte Carlo Neural Fictitious Self Play (MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which greatly improves the performance on large-scale zero-sum imperfect-information games. Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can't. Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It use asynchronous and parallel architecture to collect game experience. In experiments, we show that parallel actor-learners have a further accelerated and stabilizing effect on training.
Robust Building-Based Registration of Airborne Lidar Data and Optical Imagery on Urban Scenes

The motivation of this paper is to address the problem of registering airborne LiDAR data and optical aerial or satellite imagery acquired from different platforms, at different times, with different points of view and levels of detail. In this paper, we present a robust registration method based on building regions, which are extracted from optical images using mean shift segmentation, and from LiDAR data using a 3D point cloud filtering process. The matching of the extracted building segments is then carried out using Graph Transformation Matching (GTM) which allows to determine a common pattern of relative positions of segment centers. Thanks to this registration, the relative shifts between the data sets are significantly reduced, which enables a subsequent fine registration and a resulting high-quality data fusion.
The Southernmost Pre-Columbian Dogs in the Americas: Phenotype, Chronology, Diet and Genetics

ABSTRACT The archaeological record shows the presence of medium-sized dogs with mesocephalic skulls in Southeast South America, from at least the end of the third millennium BP to historical times, along 700 km from southern Brazil to the wetlands of the Paraná River in Argentina. These dogs, associated with complex hunter-gatherers, do not appear to have been the product of exchange with Andean societies as previous theories suggested, but rather of a local breeding process, probably reflecting the offspring of a founder population introduced in the area before at least the third millennium BP. Isotopic values show a C3 omnivorous pattern, resulting from a broad and opportunistic niche, not overlapping with that of humans. The relationships between humans and their dogs were very complex; some of the dogs were buried in mortuary areas, in double human-dog burials, meanwhile others were used as a source of raw material. Shortly after the introduction of European dogs, they were quickly assimilated by these introduced dogs, which is supported by the pairwise distance analysis. Phylogenetic analysis illustrates the maternal lineage of these pre-Columbian and modern dogs, both belonging to the haplogroup A, supporting a common ancestry.
Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data

Opportunities to collect real-time social media data during a crisis remain limited to location and keyword filtering despite the sparsity of geographic metadata and the tendency of keyword-based methods to capture information posted by remote rather than local users. Here we introduce a third, network filtering method that uses social network ties to infer the location of social media users in a geographic community and collect data from networks of these users during a crisis. In this paper we compare all three methods by analysing the distribution of situational reports of infrastructure damage and service disruption across location, keyword, and network-filtered social media data during a weather emergency. We find that network filtering doubles the number of situational reports collected in real-time compared to location and keyword filtering alone, but that all three methods collect unique reports that can support situational awareness of incidents occurring across a community.
Succession in Family Restaurant Business: An Intergenerational Perspective

This study aims at examining the succession process of family restaurants in Surabaya from the perspective of its former owners and successors. It was conducted to determine the succession planning of the former owners and successors to maintain their family business and to investigate the obstacles faced during the process. This study employs a qualitative method by studying two family restaurants in Surabaya where three informants from each restaurant were interviewed. It was found that in a Family Business Enterprise (FBE) type, formal succession planning was not necessary because the businesses were fully managed by the successors’ family members. In addition, family values, hobbies, and passion were found to be fundamental aspects to be considered in the succession of family business planning.
Scaling up MIMO Radar for Target Detection

This work focuses on target detection in a colocated MIMO radar system. Instead of exploiting the "classical’ temporal domain, we propose to explore the spatial dimension (i.e., number of antennas M) to derive asymptotic results for the detector. Specifically, we assume no a priori knowledge of the statistics of the autoregressive data generating process and propose to use a mispecified Wald-type detector, which is shown to have an asymptotic χ-squared distribution as M → ∞. Closed-form expressions for the probabilities of false alarm and detection are derived. Numerical results are used to validate the asymptotic analysis in the finite system regime. It turns out that, for the considered scenario, the asymptotic performance is closely matched already for M ≥ 50.
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction

In this paper, we present a novel integrated approach for keyphrase generation (KG). Unlike previous works which are purely extractive or generative, we first propose a new multi-task learning framework that jointly learns an extractive model and a generative model. Besides extracting keyphrases, the output of the extractive model is also employed to rectify the copy probability distribution of the generative model, such that the generative model can better identify important contents from the given document. Moreover, we retrieve similar documents with the given document from training data and use their associated keyphrases as external knowledge for the generative model to produce more accurate keyphrases. For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases. Experiments on the five KG benchmarks demonstrate that our integrated approach outperforms the state-of-the-art methods.
FoveaBox: Beyond Anchor-based Object Detector

We present FoveaBox, an accurate, flexible and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize the predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations for each input image. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance of 42.1 AP on the standard COCO detection benchmark. Specially for the objects with arbitrary aspect ratios, FoveaBox brings in significant improvement compared to the anchor-based detectors. More surprisingly, when it is challenged by the stretched testing images, FoveaBox shows great robustness and generalization ability to the changed distribution of bounding box shapes. The code will be made publicly available.
Overlay-Based Android Malware Detection at Market Scales: Systematically Adapting to the New Technological Landscape

Android <italic>overlay</italic> enables one app to draw over other apps by creating an extra <monospace>View</monospace> layer atop the host <monospace>View</monospace>, which nevertheless can be exploited by malicious apps (malware) to attack users. To combat this threat, prior countermeasures concentrate on restricting the capabilities of overlays at the OS level while sacrificing overlays’ usability; recently, the overlay mechanism has been substantially updated to prevent a variety of attacks, which however can still be evaded by considerable adversaries. To address these shortcomings, a more pragmatic approach is to enable <italic>early detection</italic> of overlay-based malware during the app market review process, so that all the capabilities of overlays can stay unchanged. For this purpose, in this paper we first conduct a large-scale comparative study of overlay characteristics in benign and malicious apps, and then implement the OverlayChecker system to automatically detect overlay-based malware for one of the world’s largest Android app stores. In particular, we have made systematic efforts in feature engineering, UI exploration, emulation architecture, and run-time environment, thus maintaining high detection accuracy (97 percent precision and 97 percent recall) and short per-app scan time (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="gong-ieq1-3079433.gif"/></alternatives></inline-formula>1.7 minutes) with only two commodity servers, under an intensive workload of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="gong-ieq2-3079433.gif"/></alternatives></inline-formula>10K newly submitted apps per day.
ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification

Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance. Although depthwise separable convolution can be an efficient module to approximate a standard convolution, it often leads to reduced representational power of networks. In this paper, under budget constraints such as computational cost (MAdds) and the parameter count, we propose a novel basic architectural block, ANTBlock. It boosts the representational power by modeling, in a high dimensional space, interdependency of channels between a depthwise convolution layer and a projection layer in the ANTBlocks. Our experiments show that ANTNet built by a sequence of ANTBlocks, consistently outperforms state-of-the-art low-cost mobile convolutional neural networks across multiple datasets. On CIFAR100, our model achieves 75.7% top-1 accuracy, which is 1.5% higher than MobileNetV2 with 8.3% fewer parameters and 19.6% less computational cost. On ImageNet, our model achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms (20% faster) on iPhone 5s over MobileNetV2.