Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Framework for Cooperation Behavior of Start-ups: Developing a Multi-Item Scale from an Empirical Perspective

Many start-ups are in search of cooperation partners to develop their innovative business models. In response, incumbent firms are introducing increasingly more cooperation systems to engage with start-ups. However, many of these cooperations end in failure. Although qualitative studies on cooperation models have tried to improve the effectiveness of incumbent start-up strategies, only a few have empirically examined start-up cooperation behavior. Considering the lack of adequate measurement models in current research, this paper focuses on developing a multi-item scale on cooperation behavior of start-ups, drawing from a series of qualitative and quantitative studies. The resultant scale contributes to recent research on start-up cooperation and provides a framework to add an empirical perspective to current research.
Learning to Cluster Faces on an Affinity Graph

Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data becomes an appealing alternative. Recent works have shown that clustering unlabeled faces is a promising approach, often leading to notable performance gains. Yet, how to effectively cluster, especially on a large-scale (i.e. million-level or above) dataset, remains an open question. A key challenge lies in the complex variations of cluster patterns, which make it difficult for conventional clustering methods to meet the needed accuracy. This work explores a novel approach, namely, learning to cluster instead of relying on hand-crafted criteria. Specifically, we propose a framework based on graph convolutional network, which combines a detection and a segmentation module to pinpoint face clusters. Experiments show that our method yields significantly more accurate face clusters, which, as a result, also lead to further performance gain in face recognition.
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use. It is derived from the original audio and text materials of the LibriSpeech corpus, which has been used for training and evaluating automatic speech recognition systems. The new corpus inherits desired properties of the LibriSpeech corpus while addressing a number of issues which make LibriSpeech less than ideal for text-to-speech work. The released corpus consists of 585 hours of speech data at 24kHz sampling rate from 2,456 speakers and the corresponding texts. Experimental results show that neural end-to-end TTS models trained from the LibriTTS corpus achieved above 4.0 in mean opinion scores in naturalness in five out of six evaluation speakers. The corpus is freely available for download from this http URL.
A Framework for Modeling Linear Surface Waves on Shear Currents in Slowly Varying Waters

We present a theoretical and numerical framework -- which we dub the Direct Integration Method (DIM) -- for simple, efficient and accurate evaluation of surface wave models allowing presence of a current of arbitrary depth dependence, and where bathymetry and ambient currents may vary slowly in horizontal directions. On horizontally constant water depth and shear current the DIM numerically evaluates the dispersion relation of linear surface waves to arbitrary accuracy, and we argue that for this purpose it is superior to two existing numerical procedures: the piecewise-linear approximation and a method due to \textit{Dong \& Kirby} [2012]. The DIM moreover yields the full linearized flow field at little extra cost. We implement the DIM numerically with iterations of standard numerical methods. The wide applicability of the DIM in an oceanographic setting in four aspects is shown. Firstly, we show how the DIM allows practical implementation of the wave action conservation equation recently derived by \textit{Quinn et al.} [2017]. Secondly, we demonstrate how the DIM handles with ease cases where existing methods struggle, i.e.\ velocity profiles $\mathbf{U}(z)$ changing direction with vertical coordinate $z$, and strongly sheared profiles. Thirdly, we use the DIM to calculate and analyse the full linear flow field beneath a 2D ring wave upon a near--surface wind--driven exponential shear current, revealing striking qualitative differences compared to no shear. Finally we demonstrate that the DIM can be a real competitor to analytical dispersion relation approximations such as that of \textit{Kirby \& Chen} [1989] even for wave/ocean modelling.
The Prognostic Value of a Tumor Microenvironment-Based Immune Cell Infiltration Score Model in Colon Cancer

The current study aimed to construct a prognostic predictive model based on tumor microenvironment. CIBERSORT and ESTIMATE algorithms were used to reveal the immune cell infiltration (ICI) landscape of colon cancer. Patients were classified into three clusters by ConsensusClusterPlus algorithm. ICI scores of each patient were determined by principal component analysis. Patients were divided into high and low ICI score groups. Survival, gene expression, and somatic mutation of the two groups were compared. We found that patients with no lymph node invasion, no metastasis, T1–2 disease, and stage I–II had higher ICI scores. Calcium signaling pathway, leukocyte transendothelial migration pathway, MAPK signaling pathway, TGF β pathway, and Wnt signaling pathway were enriched in the high ICI score group. Immune-checkpoint and immune-activity associated genes were decreased in high ICI score patients. Patients in the high ICI score group had better survival. Prognostic value of ICI score was independent of tumor mutational burden (TMB). The ICI score model constructed in the current study may serve as an independent prognostic biomarker in colon cancer.
Laser-seeding Attack in Quantum Key Distribution

Quantum key distribution (QKD) based on the laws of quantum physics allows the secure distribution of secret keys over an insecure channel. Unfortunately, imperfect implementations of QKD compromise its information-theoretical security. Measurement-device-independent quantum key distribution (MDI-QKD) is a promising approach to remove all side channels from the measurement unit, which is regarded as the "Achilles' heel" of QKD. An essential assumption in MDI-QKD is however that the sources are trusted. Here we experimentally demonstrate that a practical source based on a semiconductor laser diode is vulnerable to a laser seeding attack, in which light injected from the communication line into the laser results in an increase of the intensities of the prepared states. The unnoticed increase of intensity may compromise the security of QKD, as we show theoretically for the prepare-and-measure decoy-state BB84 and MDI-QKD protocols. Our theoretical security analysis is general and can be applied to any vulnerability that increases the intensity of the emitted pulses. Moreover, a laser seeding attack might be launched as well against decoy-state based quantum cryptographic protocols beyond QKD.
Revisiting the Instability Problem of the Interacting Dark Energy Model in the Parameterized Post-Friedmann Framework

Dark energy might interact with dark matter in a direct, non-gravitational way, which can help remedy several theoretical defects. In order to find out the properties of interacting dark energy (IDE) models, it is necessary to investigate the cosmological perturbations in detail. However, due to the improper use of pressure perturbation of dark energy, a large-scale instability at the early stage occurs occasionally. In recent years, parameterized post-Friedmann approach has been used to calculate the perturbation equations in the IDE scenario. Under this framework, the dark energy pressure perturbation was replaced by the relationship between the momentum density of dark energy and other components on a large scale. However, this paper shows that if the interaction terms are related to the velocity perturbation of dark energy, the density perturbation of dark matter, and the matter power spectrum will diverge when the equation of state parameter of dark energy w is close to −1. A simple parameterization steering clear of this problem is proposed in this paper, which is a more general form and can be applied to explore the interaction between dark matter and dark energy by using various cosmological data.
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure

Workload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon’s public cloud infrastructure using compute, memory and IO resource utilization data.
Teaching GANs to sketch in vector format

Sketching is a fundamental human cognitive ability. Deep Neural Networks (DNNs) have achieved the state-of-the-art performance in recognition tasks like image recognition, speech recognition etc. but have not made significant progress in generating stroke-based sketches a.k. a sketches in vector format. Though there are Variational Auto Encoders (VAEs) for generating sketches in vector format, there is no Generative Adversarial Network (GAN) architecture for the same. In this paper, we propose a standalone GAN architecture called SkeGAN and a hybrid VAE-GAN architecture called VASkeGAN, for sketch generation in vector format. SkeGAN is a stochastic policy in Reinforcement Learning (RL), capable of generating both multidimensional continuous and discrete outputs. VASkeGAN draws sketches by coupling the efficient representation of data by VAE with the powerful generating capabilities of GAN. We have validated that SkeGAN and VASkeGAN generate visually appealing sketches with minimal scribble effect and is comparable to a recent work titled Sketch-RNN.
On Training Robust PDF Malware Classifiers

Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the evasion cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 92.27% average verified robust accuracy over three properties, while maintaining 99.74% accuracy and 0.56% false positive rate. With simple robustness properties, our robust model maintains 7% higher robust accuracy than all the baseline models against unrestricted whitebox attacks. Moreover, the state-of-the-art and new adaptive evolutionary attackers need up to 10 times larger $L_0$ feature distance and 21 times more PDF basic mutations (e.g., inserting and deleting objects) to evade our robust model than the baselines.
Who Do You Think We Are? The Data Publics in Digital Government Policy

This study provides conceptual clarity on open data users by connecting an empirical analysis of policy documents to emerging theoretical research on data publics. Releasing files to the public for reuse is the primary objective of policy on open government data. Recent public sphere scholarship provides insights into who reuses data by defining a data public as people who actively construct narratives with openly available digital sources. A content analysis of United States federal policy documents identified the language used to represent people who might reuse data. An inductive qualitative analysis of mandated digital strategy reports generated a taxonomy that characterizes people mentioned in open data policy. In addition to the taxonomy, this research contributes a set of propositions to predict data reuse based on these characteristics. The results encourage further dialog between public sphere and digital government scholars to establish testable explanations about data publics.
Unsupervised Person Image Generation With Semantic Parsing Transformation

In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we propose a new pathway to decompose the hard mapping into two more accessible subtasks, namely, semantic parsing transformation and appearance generation. Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning. Secondly, an appearance generative network learns to synthesize semantic-aware textures. Thirdly, we demonstrate that training our framework in an end-to-end manner further refines the semantic maps and final results accordingly. Our method is generalizable to other semantic-aware person image generation tasks, e.g., clothing texture transfer and controlled image manipulation. Experimental results demonstrate the superiority of our method on DeepFashion and Market-1501 datasets, especially in keeping the clothing attributes and better body shapes.
Deep Surface Normal Estimation With Hierarchical RGB-D Fusion

The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image. Specifically, the features from color image and depth are successively integrated at multiple scales to ensure global surface smoothness while preserving visually salient details. Meanwhile, the depth features are re-weighted with a confidence map estimated from depth before merging into the color branch to avoid artifacts caused by input depth corruption. Additionally, a hybrid multi-scale loss function is designed to learn accurate normal estimation given noisy ground-truth dataset. Extensive experimental results validate the effectiveness of the fusion strategy and the loss design, outperforming state-of-the-art normal estimation schemes.
Efficient Estimation of Heat Kernel PageRank for Local Clustering

Given an undirected graph G and a seed node s, the local clustering problem aims to identify a high-quality cluster containing s in time roughly proportional to the size of the cluster, regardless of the size of G. This problem finds numerous applications on large-scale graphs. Recently, heat kernel PageRank (HKPR), which is a measure of the proximity of nodes in graphs, is applied to this problem and found to be more efficient compared with prior methods. However, existing solutions for computing HKPR either are prohibitively expensive or provide unsatisfactory error approximation on HKPR values, rendering them impractical especially on billion-edge graphs. In this paper, we present TEA and TEA+, two novel local graph clustering algorithms based on HKPR, to address the aforementioned limitations. Specifically, these algorithms provide non-trivial theoretical guarantees in relative error of HKPR values and the time complexity. The basic idea is to utilize deterministic graph traversal to produce a rough estimation of exact HKPR vector, and then exploit Monte-Carlo random walks to refine the results in an optimized and non-trivial way. In particular, TEA+ offers practical efficiency and effectiveness due to non-trivial optimizations. Extensive experiments on real-world datasets demonstrate that TEA+ outperforms the state-of-the-art algorithm by more than four times on most benchmark datasets in terms of computational time when achieving the same clustering quality, and in particular, is an order of magnitude faster on large graphs including the widely studied Twitter and Friendster datasets.
Acinetobacter baumannii Deactivation by Means of DBD-Based Helium Plasma Jet

Acinetobacter baumannii is a typically short, almost round, rod-shaped (coccobacillus) Gram-negative bacterium. It can be an opportunistic pathogen in humans, affecting people with compromised immune systems, and it is becoming increasingly important as a hospital-associated (nosocomial) infection. It has also been isolated from environmental soil and water samples. In this work, unlike conventional medical methods like antibiotics, the influence of atmospheric-pressure cold plasma on this bacterium is evaluated by means of a colony count technique and scanning electron microscopy. The plasma used here refers to streamers axially propagating into a helium channel penetrating the atmospheric air. The plasma is probed with high resolution optical emission spectroscopy and copious reactive species are unveiled under low-temperature conditions. Based on the experimental results, post-treatment (delayed) biochemical effects on Acinetobacter baumannii and morphological modifications appear dominant, leading to complete deactivation of this bacterium.
Detecting Important Terms in Source Code for Program Comprehension

Software Engineering research has become extremely dependent on terms (words in textual data) extracted from source code. Different techniques have been proposed to extract the most “important” terms from code. These terms are typically used as input to research prototypes: the quality of the output of these prototypes will depend on the quality of the term extraction technique. At present no consensus exists about which technique predicts the best terms for code comprehension. We perform a literature review, and propose a unified prediction model based on a Naive Bayes algorithm. We evaluate our model in a field study with professional programmers, as well as a standard 10-fold synthetic study. We found our model predicts the top quartile of the most-important terms with approximately 50% precision and recall, outperforming other popular techniques. We found the predictions from our model to help programmers to the same degree
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact

Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer human-robot interaction. Desirable grasps are not only stable, but also functional: they enable post-grasp actions with the object. However, functional grasp synthesis for high degree-of-freedom anthropomorphic hands from object shape alone is challenging. We present ContactGrasp, a framework for functional grasp synthesis from object shape and contact on the object surface. Contact can be manually specified or obtained through demonstrations. Our contact representation is object-centric and allows functional grasp synthesis even for hand models different than the one used for demonstration. Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents. The project webpage is https://contactdb.cc. gatech.edu/contactgrasp.html.
Can Title Images Predict the Emotions and the Performance of Crowdfunding Projects?

Crowdfunding is a novel way to raise funds from individuals. However, taking Kickstarter for example, more than 60% of projects failed to reach the funding targets. Hence it is imperative to study how to improve the successfulness of the projects. From a design perspective, we intend to investigate that can the characteristics of title images of the projects on the search page of the crowdfunding website predict the performance of crowdfunding projects. We use objective standards to measure the aesthetic features of the title images. And we introduce emotions as important antecedents for the performance of a project. We used deep learning to extract the emotion metrics from the title images. Analysis results provide significant evidence that aesthetic attributes of images can predict emotion in images, and emotions, such as sadness and contentment, can predict the performance of crowdfunding projects. Our results provide both theoretical and practical values.
Endocrine Modulation of Sending and Receiving Signals in Context-Dependent Social Communication

Animal communication requires senders to transmit signals through the environment to conspecific receivers, which then leads to context-dependent behavioral decisions. Sending and receiving sensory information in social contexts, however, can be dramatically influenced by an individual’s internal state, particularly in species that cycle in and out of breeding or other physiological condition like nutritional state or social status. Modulatory substances like steroids, peptides, and biogenic amines can influence both the substrates used for sending social signals (e.g., motivation centers, sensorimotor pathways, and muscles) as well as the peripheral sensory organs and central neural circuitry involved in the reception of this information and subsequent execution of behavioral responses. This issue highlights research from neuroethologists on the topic of modulation of sending and receiving social signals and demonstrates that it can occur in both males and females, in different senses at both peripheral sensory organs and the brain, at different levels of biological organization, on different temporal scales, in various social contexts, and across many diverse vertebrate taxa. Modifying a signal produced by a sender or how that signal is perceived in a receiver provides flexibility in communication and has broad implications for influencing social decisions like mate choice, which ultimately affects reproductive fitness and species persistence. This phenomenon of modulators and internal physiological state impacting communication abilities is likely more widespread than currently realized and we hope this issue inspires others working on diverse systems to examine this topic from different perspectives. An integrative and comparative approach will advance discovery in this field and is needed to better understand how endocrine modulation contributes to sexual selection and the evolution of animal communication in general.