Miracle-dz/UniFAR-FTUs huggingface dataset:
An exploratory study

Purpose – This paper aims to empirically explore the influence that different factors have on the creation of university spin-offs. Design/methodology/approach – This is an exploratory study that uses a multilevel design. The study follows a purposive sampling method where data are gathered from a variety of sources. The hypotheses are tested with a sample of 52 universities from the Latin American region using multiple hierarchical regression analysis. Findings – The results indicate that entrepreneurial orientation, incubators or entrepreneurship support programs and goods market efficiency are the factors that positively influence the creation of university spinoffs. Research limitations/implications – The results of this study should be observed in light of some limitations. The sample size is an important factor, as a bigger sample could allow for examination of crossinstitutional variation in the context of different countries. In addition, the lack of records or public databases makes it difficult to incorporate more information on spin-offs creation, including features or firm performance. Originality/value – This study is the first to empirically explore the university spin-offs creation phenomenon in the Latin American region. Hence, it contributes to university entrepreneurship literature, specifically to better understand this phenomenon from a more holistic perspective across different levels of analysis at the same time that it incorporates previous proposals to explain entrepreneurial orientation at universities.
Representation Learning for Voice Profiling

Voice-profiling is the deduction of a speakers characteristics from their voice, and has many applications in audio forensics. Characteristics that are determined include the speakers gender, age, and ethnicity along with other physical characteristics. Another challenging voice-profiling problem is facial image reconstruction, where an image of the speakers face is generated from a voice recording. One of the major challenges in this field is choosing a generalizable representation of voice that encodes all relevant speaker characteristics. Prior computational voice-profiling techniques modelled the production of voice as a physical system, and defined multiple voice signal features that encode speaker characteristics. Recent advances in artificial neural networks has resulted in an improvement in performance across voice profiling tasks, but such methods are often purely data-driven; the representation and relationships between voice and speaker characteristics are learned from a large dataset, not necessarily leveraging the knowledge-based voice features from prior work. In this work, we combine domain-specific signal-processing features with state of the art neural network techniques to learn a generalizable audio representation for voice-profiling. The learned representation is evaluated on multiple voice-profiling tasks including speaker identification, gender classification, and age prediction. Additionally, we present a novel framework for face generation tasks and evaluate our voice representation on facial image reconstruction from voice. August 27, 2019 DRAFT
Stochastic Optimization with Adaptive Batch Size : Discrete Choice Models as a Case Study

The 2.5 quintillion bytes of data created each day brings new opportunities, but also new stimulating challenges for the discrete choice community. Opportunities because more and more new and larger data sets will undoubtedly become available in the future. Challenging because insights can only be discovered if models can be estimated, which is not simple on these large datasets. In this paper, inspired by the good practices and the intensive use of stochastic gradient methods in the ML field, we introduce the algorithm called Window Moving Average Adaptive Batch Size (WMA-ABS) which is used to improve the efficiency of stochastic second-order methods. We present preliminary results that indicate that our algorithms outperform the standard secondorder methods, especially for large datasets. It constitutes a first step to show that stochastic algorithms can finally find their place in the optimization of Discrete Choice Models.
DATASET USING A MOMENT-BASED ESTIMATOR By

We consider a particular instance of a common problem in recommender systems: using a database of book reviews to inform usertargeted recommendations. In our dataset, books are categorized into genres and sub-genres. To exploit this nested taxonomy, we use a hierarchical model that enables information pooling across across similar items at many levels within the genre hierarchy. The main challenge in deploying this model is computational: the data sizes are large, and fitting the model at scale using off-the-shelf maximum likelihood procedures is prohibitive. To get around this computational bottleneck, we extend a moment-based fitting procedure proposed for fitting single-level hierarchical models to the general case of arbitrarily deep hierarchies. This extension is an order of magnitude faster than standard maximum likelihood procedures. The fitting method can be deployed beyond recommender systems to general contexts with deeply-nested hierarchical generalized linear mixed models.
Term E ff ects of Salinization on Compound Release from Drained and Restored Coastal Wetlands

Over the past two decades, great efforts have been made to restore coastal wetlands through the removal of dikes, but challenges remain because the effects of flooding with saline water on water quality are unknown. We collected soil samples from two adjacent coastal fen peatlands, one drained and diked, the other open to the sea and rewetted, aiming at assessing the mobility and export of various compounds. Microcosm experiments with constant flow-through conditions were conducted to determine the effluent concentrations of dissolved organic carbon (DOC), ammonium (NH4), and phosphate (PO43−) during saline–fresh water cycles. Sodium chloride (NaCl) was used to adjust salinity (saline water, NaCl concentration of 0.12 mol L−1; fresh water, NaCl concentration of 0.008 mol L−1) and served as a tracer. A model analysis of the obtained chloride (Cl−) and sodium (Na) breakthrough curves indicated that peat soils have a dual porosity structure. Sodium was retarded in peat soils with a retardation factor of 1.4 ± 0.2 due to adsorption. The leaching tests revealed that water salinity has a large impact on DOC, NH4, and PO43− release. The concentrations of DOC in the effluent decreased with increasing water salinity because the combination of high ionic strength (NaCl concentration of 0.12 mol L−1) and low pH (3.5 to 4.5) caused a solubility reduction. On the contrary, saline water enhanced NH4 release through cation exchange processes. The PO43− concentrations, however, decreased in the effluent with increasing water salinity. Overall, the decommissioning of dikes at coastal wetlands and the flooding of once drained and agriculturally used sites increase the risk that especially nitrogen may be leached at higher rates to the sea.
On Scalable Service Function Chaining with O(1) Flowtable Entries

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.
School of Economic s and Finance UK Term Structure Decompositions at the Zero Lower Bound

This paper employs a Zero Lower Bound (ZLB) consistent shadow-rate model to decompose UK nominal yields into expectation and term premia components. Compared to a standard affine term structure model, it performs relatively better in a ZLB setting and effectively captures the countercyclical nature of term premia. The ZLB model is then exploited to estimate inflation expectations and risk premia. This entails jointly pricing and decomposing nominal and real UK yields. We find evidence that mediumand longterm inflation expectations are contained within narrower bounds since the early 1990s, suggesting monetary policy credibility improved after the introduction of inflation targeting. JEL classification numbers: E31, E43, E52, E58, G12.
A cladistic analysis of subfamilial relationships I in the Chrysomelidae sensu lato ( Chrysomeloidea )

A phylogeny of the subfamilies traditionally assigned to the Chrysomelidae and Bruchidae is suggested, based on cladistic relationships of 29 family-rank taxa and 71 characters. The analysis is primarily based on a review of published morphological characters of adults and larvae. Various plesiomorphic Cerambycidae and allied taxa (a lineage within Chrysomeloidea) and Curculionoidea are used for outgroup comparison. The hypothesis of monophyly of the Chrysomelidae (including Megalopodidae and Bruchinae) is tested. The Chrysomeloidea is divided into four groups: (1) the families of the cerambycid lineage; (2) Megalopodidae (with subfamilies Megalopodinae, Palophaginae and Zeugophorinae); (3) Orsodacnidae (with subfamilies Aulacoscelidinae and Orsodacninae); (4) Chrysomelidae (with subfamilies Bruchinae, Chrysomelinae, Criocerinae, Cryptocephalinae, Donaciinae, Eumolpinae, Galerucinae, Hispinae, Lamprosomatinae and Sagrinae). Evidence is given for monophyly of the following: Megalopodidae + (Orsodacnidae + Chrysomelidae); Palophaginae + (Megalopodinae + Zeugophorinae); Orsodacnidae + Chrysomelidae; Aulacoscelidinae + Orsodacninae; Sagrinae + Bruchinae; Chrysomelinae + Galerucinae; Eumolpinae + (Lamprosomatinae + Cryptocephalinae). Various possible arrangements of subfamilies of Chrysomelidae are discussed. Support is given for subsuming Cassidinae under Hispinae, Alticinae in Galerucinae, Synetinae and Megascelidinae in Eumolpinae, Sphaerocharitinae in Lamprosomatinae and Clytrinae and Chlamisinae in Cryptocephalinae. Keys are given for the identification of larvae and adults of the families and subfamilies of Chrysomeloidea. Host association and biogeography are discussed briefly and compared with the proposed phylogeny.
A Randomized , Controlled Trial of Virtual Reality-Graded Exposure Therapy for Post-Traumatic Stress Disorder in Active Duty Service Members with Combat-Related Post-Traumatic Stres

Virtual reality (VR)-based therapy has emerged as a potentially useful means to treat post-traumatic stress disorder (PTSD), but randomized studies have been lacking for Service Members from Iraq or Afghanistan. This study documents a small, randomized, controlled trial of VR-graded exposure therapy (VR-GET) versus treatment as usual (TAU) for PTSD in Active Duty military personnel with combat-related PTSD. Success was gauged according to whether treatment resulted in a 30 percent or greater improvement in the PTSD symptom severity as assessed by the Clinician Administered PTSD Scale (CAPS) after 10 weeks of treatment. Seven of 10 participants improved by 30 percent or greater while in VR-GET, whereas only 1 of the 9 returning participants in TAU showed similar improvement. This is a clinically and statistically significant result (w1⁄4 6.74, p < 0.01, relative risk 3.2). Participants in VR-GET improved an average of 35 points on the CAPS, whereas those in TAU averaged a 9-point improvement ( p < 0.05). The results are limited by small size, lack of blinding, a single therapist, and comparison to a relatively uncontrolled usual care condition, but did show VR-GET to be a safe and effective treatment for combat-related PTSD.
Insurers ’ Underwriting Result and Investment Risk-Taking Behavior-- Evidence from U . S . Property and Casualty Insurance Industry

This paper presents an analysis about the impact of insurers’ underwriting income on their investment risk-taking strategy using empirical evidence from U.S. property-casualty insurance industry. To see this effect, I raise two streams of hypotheses to test: capacity constraint hypothesis and gambling hypothesis. The empirical evidences are in favor of capacity constraint theory: insurers’ investment in risky assets increase as their underwriting loss decrease. And I also find that insurers’ investment in risky asset during soft market is slightly higher than hard market. I therefore conclude that insurer adjust their investment risk-taking decisions based on their underwriting results.
Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures

The ubiquity of accelerators in high-performance computing has driven programming complexity beyond the skill-set of the average domain scientist. To maintain performance portability in the future, it is imperative to decouple architecture-specific programming paradigms from the underlying scientific computations. We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating program definition from its optimization. By combining fine-grained data dependencies with high-level control-flow, SDFGs are both expressive and amenable to program transformations, such as tiling and double-buffering. These transformations are applied to the SDFG in an interactive process, using extensible pattern matching, graph rewriting, and a graphical user interface. We demonstrate SDFGs on CPUs, GPUs, and FPGAs over various motifs --- from fundamental computational kernels to graph analytics. We show that SDFGs deliver competitive performance, allowing domain scientists to develop applications naturally and port them to approach peak hardware performance without modifying the original scientific code.
Multi-Task Learning with Language Modeling for Question Generation

This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.
Analysis and Performance Evaluation of OSPF and RIP Routing Protocols Using QualNet

Undoubtedly, the human life tasks rely more and more on computers. Especially after the internet and communication technologies leaded to the E (electronic) to be includes in almost everything from governments to trading and banking system, etc. Hence, the quality of the services can be provided by such systems became a matter of interest. Since the routing protocols are the fundamental procedures controlling the network work. They became the most interest field to be researched. In this paper, we simulate two of the widest used routing protocols, RIP and OSPF performs in different scenarios and network configurations, then we analyze the performance (total data sent and received, total throughput and average end to end delay) of each scenario in different conditions. The result of simulation show that OSPF performs better than RIP especially when it comes to the reliability of connection and the convergence time to cope the network failure. QualNet simulation tool had been used to design the network; analysis of the results was examine using standard tools.
Robust Function-on-Function Regression

ABSTRACT Functional linear regression is a widely used approach to model functional responses with respect to functional inputs. However, classical functional linear regression models can be severely affected by outliers. We therefore introduce a Fisher-consistent robust functional linear regression model that is able to effectively fit data in the presence of outliers. The model is built using robust functional principal component and least squares regression estimators. The performance of the functional linear regression model depends on the number of principal components used. We therefore introduce a consistent robust model selection procedure to choose the number of principal components. Our robust functional linear regression model can be used alongside an outlier detection procedure to effectively identify abnormal functional responses. A simulation study shows our method is able to effectively capture the regression behavior in the presence of outliers, and is able to find the outliers with high accuracy. We demonstrate the usefulness of our method on jet engine sensor data. We identify outliers that would not be found if the functional responses were modeled independently of the functional input, or using nonrobust methods.
Vanishing diffusion limits for planar fronts in bistable models with saturation

We deal with heteroclinic planar fronts for parameter-dependent reaction-diffusion equations with bistable reaction and saturating diffusive term like $$ u_t=\epsilon \, \textrm{div}\, \left(\frac{\nabla u}{\sqrt{1+\vert \nabla u \vert^2}}\right) + f(u), \quad u=u(x, t), \; x \in \textbf{R}^n, \, t \in \textbf{R}, $$ analyzing in particular their behavior for $\epsilon \to 0$. First, we construct monotone and non-monotone planar traveling waves, using a change of variables allowing to analyze a two-point problem for a suitable first-order reduction; then, we investigate their asymptotic behavior for $\epsilon \to 0$, showing in particular that the convergence of the critical fronts to a suitable step function may occur passing through discontinuous solutions.
Social Engineering in a Post-Phishing Era: Ambient Tactical Deception Attacks

It is an ordinary day working from home, and you are part of a team that regularly interacts over email. Since this is your main line of communication, the company trained you to spot phishing emails. You've learned to skip over emails that exhibit obvious phishing red flags: suspicious links, attachments, grammar errors, etc. You just received an email from your boss about a major project on which you play a critical role. The email is more demanding than usual, even impolite. Your boss has generally seemed more upset with you lately, so you approach them to express your concerns and clear the air. Your boss is not receptive to your feedback. This causes a rift that impacts your working relationship, compromising the effectiveness and productivity of the entire team. You have been a victim of an Ambient Tactical Deception (ATD) attack. We developed and tested a proof-of-concept social engineering attack targeting web-based email users. The attack is executed through a malicious browser extension that acts as a man-in-the-middle and reformats the textual content to alter the emotional tone of the email. The objective of ATD is not stealing credentials or data. ATD seeks to coerce a user to the attacker's desired behavior via the subtle manipulation of trusted interpersonal relationships. This goes beyond simple phishing and this paper reports the findings from study that investigated an ATD attack on the politeness strategy used in work emails.
Transport signatures of a Floquet topological transition at the helical edge

The manipulation of the helical edge states of two-dimensional topological insulators is crucial for the development of technological applications. Recently, an important step forward, namely, the experimental realization of a quantum point contact between helical edges, has been accomplished. We theoretically predict that such a quantum point contact, in the presence of a time periodic applied electric field, is characterized by a topological quantum phase transition in the Floquet spectrum. Moreover, we show that it is possible to detect this dynamical topological quantum phase transition by a bare conductance measurements.
Pacer: Network Side-Channel Mitigation in the Cloud

An important concern for many Cloud customers is data confidentiality. Of particular concern are potential data leaks via side channels, which arise when mutually untrusted parties contend on resources such as CPUs, caches, and networks. In this paper, we present a principled solution for mitigating side channels that arise from shared network links. Our solution, Pacer, shapes the outbound traffic of a Cloud tenant to make it independent of the tenant's secrets by design. At the same time, Pacer permits traffic variations based on public (non-secret) aspects of the tenants' computation, thus enabling efficient sharing of network resources. Implementing Pacer requires modest changes to the guest OS and the hosting hypervisor, and only minimal changes to guest applications. Experiments show that Pacer allows guests to protect their secrets with overhead close to the minimum possible considering the guest's conditional traffic distribution given public information. For instance, Pacer can hide a requested Wiktionary document in one of two size clusters at an average throughput and bandwidth overhead of 6.8% and 150%, respectively.
EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I/O bandwidth, power consumption is dominated by I/O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly, and lossless compression scheme for the feature maps present within convolutional neural networks. We present hardware architectures and synthesis results for the compressor and decompressor in 65 nm. With a throughput of one 8-bit word/cycle at 600 MHz, they fit into 2.8 kGE and 3.0 kGE of silicon area, respectively—together the size of less than seven 8-bit multiply-add units at the same throughput. We show that an average compression ratio of <inline-formula> <tex-math notation="LaTeX">$5.1\times $ </tex-math></inline-formula> for AlexNet, <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> for VGG-16, <inline-formula> <tex-math notation="LaTeX">$2.4\times $ </tex-math></inline-formula> for ResNet-34 and <inline-formula> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> for MobileNetV2 can be achieved—a gain of 45–70% over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference.