Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating parental care behaviour in same-sex pairing of zoo greater flamingo (Phoenicopterus roseus)

Same-sex pair bonds have been documented in several animal species and they are widespread in birds. However, little is known about the evolutionary origin and the adaptive value of such behaviour. The aim of this study was to investigate the parental behaviour of four zoo female greater flamingos involved in two breeding pairs, housed in a flock at Parco Natura Viva, Italy. Further, the behaviour of the study females was compared with that of male and female flamingos in heterosexual pairs described in a previous published work on this same flock. For each pair, the behaviour of both birds during the incubation period was recorded and twenty 10-minute sessions were run within the incubation period. A continuous focal animal sampling method was used to collect data on location (on the nest or not on the nest) and the parental care behaviour (e.g.: agonistic behaviours toward disturbing conspecifics, egg-care, nest-building, self-comfort behaviour, sleeping) of the two pairs. Data of the current study females were compared with those of females and males involved in heterosexual pairs of this same flock. Results showed that within each pair the egg-layer female stayed away from the nest more than the other female. In addition, the female that did not lay an egg was more involved in agonistic behaviour compared to other females, particularly when in specific locations. In heterosexual pairs, male flamingos were more involved in the incubation and in nest protection. Moreover, no significant differences in the time spent on the nest and away from the nest between the heterosexual male and the non-layer females of same-sex pairs were found. The same findings were reported when comparing heterosexual females and the egg-layer females of the same-sex pairs. Therefore, our findings suggest that in greater flamingos the behaviour of the female–female pairs seems to be equivalent to that of male-female bonds. Such research provides more insight into flamingo social behaviour, and their reproductive cycle, and provides information on why pair bonds may form and how these affect the wider breeding behaviour of the flock.
A Reexamination of Internationalized Domain Names: The Good, the Bad and the Ugly

Internationalized Domain Names (IDNs) are domain names containing non-ASCII characters. Despite its installation in DNS for more than 15 years, little has been done to understand how this initiative was developed and its security implications. In this work, we aim to fill this gap by studying the IDN ecosystem and cyber-attacks abusing IDN. In particular, we performed by far the most comprehensive measurement study using IDNs discovered from 56 TLD zone files. Through correlating data from auxiliary sources like WHOIS, passive DNS and URL blacklists, we gained many insights. Our discoveries are multi-faceted. On one hand, 1.4 million IDNs were actively registered under over 700 registrars, and regions within east Asia have seen prominent development in IDN registration. On the other hand, most of the registrations were opportunistic: they are currently not associated with meaningful websites and they have severe configuration issues (e.g., shared SSL certificates). What is more concerning is the rising trend of IDN abuse. So far, more than 6K IDNs were determined as malicious by URL blacklists and we also identified 1,516 and 1,497 IDNs showing high visual and semantic similarity to reputable brand domains (e.g., apple.com). Meanwhile, brand owners have only registered a few of these domains. Our study suggests the development of IDN needs to be re-examined. New solutions and proposals are needed to address issues like its inadequate usage and new attack surfaces.
Deep Extractor Network for Target Speaker Recovery From Single Channel Speech Mixtures

Speaker-aware source separation methods are promising workarounds for major difficulties such as arbitrary source permutation and unknown number of sources. However, it remains challenging to achieve satisfying performance provided a very short available target speaker utterance (anchor). Here we present a novel "deep extractor network" which creates an extractor point for the target speaker in a canonical high dimensional embedding space, and pulls together the time-frequency bins corresponding to the target speaker. The proposed model is different from prior works in that the canonical embedding space encodes knowledges of both the anchor and the mixture during an end-to-end training phase: First, embeddings for the anchor and mixture speech are separately constructed in a primary embedding space, and then combined as an input to feed-forward layers to transform to a canonical embedding space which we discover more stable than the primary one. Experimental results show that given a very short utterance, the proposed model can efficiently recover high quality target speech from a mixture, which outperforms various baseline models, with 5.2% and 6.6% relative improvements in SDR and PESQ respectively compared with a baseline oracle deep attracor model. Meanwhile, we show it can be generalized well to more than one interfering speaker.
Junctionless FETs With a Fin Body for Multi- ${V}_{\text{TH}}$  and Dynamic Threshold Operation

In this paper, a n-type junctionless FET (JLFET) with a p-type fin body (FB) is investigated using a 3-D numerical simulator. We show that the proposed device (FB-JLFET) with a p<sup>+</sup> FB can control the electrostatic potential of the channel more efficiently for multi-<inline-formula> <tex-math notation="LaTeX">${V} _{\text{TH}}$ </tex-math></inline-formula> (threshold voltage) and dynamic threshold (DT) operation. Moreover, body bias application is implemented in FB-JLFET and a large body factor (<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>) is predicted for wide range <inline-formula> <tex-math notation="LaTeX">${V} _{\text{TH}}$ </tex-math></inline-formula> modulation. Thanks to the stronger potential coupling between the channel and FB, the proposed device exhibits <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> improvement compared to the conventional JL bulk FinFETs under the same electrostatic control. In addition, DT operation is studied in the form of a JLFET for the first time and it exhibits 37% improvement of the on-state drive current and better subthreshold swing (S.S.) compared to FB-JLFET without DT. This paper provides the feasibility of multi-<inline-formula> <tex-math notation="LaTeX">${V} _{\text{TH}}$ </tex-math></inline-formula> operation with body bias mode for high performance or low-power applications and DT operation for high-speed circuits with low power consumption.
Computation Reuse in DNNs by Exploiting Input Similarity

In recent years, Deep Neural Networks (DNNs) have achieved tremendous success for diverse problems such as classification and decision making. Efficient support for DNNs on CPUs, GPUs and accelerators has become a prolific area of research, resulting in a plethora of techniques for energy-efficient DNN inference. However, previous proposals focus on a single execution of a DNN. Popular applications, such as speech recognition or video classification, require multiple back-to-back executions of a DNN to process a sequence of inputs (e.g., audio frames, images). In this paper, we show that consecutive inputs exhibit a high degree of similarity, causing the inputs/outputs of the different layers to be extremely similar for successive frames of speech or images of a video. Based on this observation, we propose a technique to reuse some results of the previous execution, instead of computing the entire DNN. Computations related to inputs with negligible changes can be avoided with minor impact on accuracy, saving a large percentage of computations and memory accesses. We propose an implementation of our reuse-based inference scheme on top of a state-of-the-art DNN accelerator. Results show that, on average, more than 60% of the inputs of any neural network layer tested exhibit negligible changes with respect to the previous execution. Avoiding the memory accesses and computations for these inputs results in 63% energy savings on average.
Multi-Task GANs for View-Specific Feature Learning in Gait Recognition

Gait recognition is of great importance in the fields of surveillance and forensics to identify human beings since gait is the unique biometric feature that can be perceived efficiently at a distance. However, the accuracy of gait recognition to some extent suffers from both the variation of view angles and the deficient gait templates. On one hand, the existing cross-view methods focus on transforming gait templates among different views, which may accumulate the transformation error in a large variation of view angles. On the other hand, a commonly used gait energy image template loses temporal information of a gait sequence. To address these problems, this paper proposes multi-task generative adversarial networks (MGANs) for learning view-specific feature representations. In order to preserve more temporal information, we also propose a new multi-channel gait template, called period energy image (PEI). Based on the assumption of view angle manifold, the MGANs can leverage adversarial training to extract more discriminative features from gait sequences. Experiments on OU-ISIR, CASIA-B, and USF benchmark data sets indicate that compared with several recently published approaches, PEI + MGANs achieves competitive performance and is more interpretable to cross-view gait recognition.
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations

It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various authors give to this claim: (1) stopping rule independence, (2) posterior calibration and (3) (semi-) frequentist robustness to optional stopping. We then prove theorems to the effect that these claims do indeed hold in a general measure-theoretic setting. For claims of type (2) and (3), such results are new. By allowing for non-integrable measures based on improper priors, we obtain particularly strong results for the practically important case of models with nuisance parameters satisfying a group invariance (such as location or scale). We also discuss the practical relevance of (1)--(3), and conclude that whether Bayes factor methods actually perform well under optional stopping crucially depends on details of models, priors and the goal of the analysis.
The Effects of a Modified Time Delay Procedure on Intraverbal Responding

ii Acknowledgments. iv Vita . v List of Figures . viii Chapter 1: Introduction . 1 Chapter 2: Method . 10 Participants . 10 Materials . 12 Setting. 12 Dependent Variables . 13 Independent Observer Agreement . 13 Treatment Integrity . 14 Experimental Design . 14 Pre-test . 15 Experimental Procedures. 16
Prometastatic Molecular Profiles in Breast Tumors From Socially Isolated Women

Abstract Background Social isolation is associated with accelerated breast cancer progression and increased disease recurrence and mortality, but the underlying biological mechanisms remain poorly understood. In preclinical models, beta-adrenergic signaling from fight-or-flight stress responses can stimulate prometastatic processes in the tumor microenvironment including upregulation of M2 macrophages, epithelial–mesenchymal transition (EMT), and lymphovascular invasion. This study examines whether the same pathways are upregulated in breast tumors from socially isolated cancer patients. Methods EMT and M1/M2 macrophage gene expression programs were analyzed by genome-wide transcriptional profiling, and lymphatic and vascular density were assessed by immunohistochemistry in primary tumors from 56 early-stage breast cancer patients who were part of the UCLA RISE study. Social isolation was quantified by the Social Provisions Scale, and disease characteristics were assessed by medical record review. General linear models were used to quantify differential gene expression across risk factor groups. Linear regression models were used to examine associations between social isolation and lymphovascular invasion. Results Tumors from socially isolated patients showed upregulated expression of genes involved in EMT (average score difference = +0.080 log2 mRNA abundance ± 0.034 standard error) and M2 macrophage polarization (+0.033 ± 0.014) as well as increased density of lymphatic vessels (β= –.29) but no difference in blood vessel density. TELiS promoter–based bioinformatics analyses indicated activation of CREB family transcription factors that mediate the gene-regulatory effects of β-adrenergic signaling (log2 fold-difference in promoter binding site prevalence: mean ± standard error = +0.49 ± 0.19). Conclusions Primary breast tumors from socially isolated patients show multiple prometastatic molecular alterations, providing a plausible biological pathway through which poor social support may accelerate breast cancer progression and defining new targets for intervention.
Container solutions for HPC Systems: A Case Study of Using Shifter on Blue Waters

Software container solutions have revolutionized application development approaches by enabling lightweight platform abstractions within the so-called "containers. " Several solutions are being actively developed in attempts to bring the benefits of containers to high-performance computing systems with their stringent security demands on the one hand and fundamental resource sharing requirements on the other. In this paper, we discuss the benefits and short-comings of such solutions when deployed on real HPC systems and applied to production scientific applications. We highlight use cases that are either enabled by or significantly benefit from such solutions. We discuss the efforts by HPC system administrators and support staff to support users of these type of workloads on HPC systems not initially designed with these workloads in mind focusing on NCSA's Blue Waters system.
Gain Switching of Monolithic 1.3 μm InAs/GaAs Quantum Dot Lasers on Silicon

We report the first demonstration of gain-switched optical pulses generated by continuous-wave 1.3 μm InAs/GaAs quantum dot (QD) broad-area lasers directly grown on silicon. The shortest observed pulses have typical durations between 175 and 200 ps with peak output powers of up to 66 mW. By varying the drive current pulsewidth and amplitude systematically, we find that the peak optical power is maximized through sufficiently long high-amplitude drive pulses, whereas shorter drive pulses with high amplitudes yield the narrowest achievable pulses. A three-level rate equation travelling-wave model is used for the simulation of our results in order to gain a first insight into the underlying physics and the laser parameters responsible for the observed behavior. The simulations indicate that a limited gain from the InAs QDs and a very high gain compression factor are the main factors contributing to the increased pulsewidth. As the optical spectra of the tested broad-area QD laser give a clear evidence of multitransverse-mode operation, the laser's dynamic response could be additionally limited by transversal variations of the gain, carrier density, and photon density over the 50 μm wide laser waveguide.
Orlis: Obfuscation-Resilient Library Detection for Android

Android apps often contain third-party libraries. For many program analyses, it is important to identify the library code in a given closed-source Android app. There are several clients of such library detection, including security analysis, clone/repackage detection, and library removal/isolation. However, library detection is complicated significantly by commonly-used code obfuscation techniques for Android. Although some of the state-of-the-art library detection tools are intended to be resilient to obfuscation, there is still room to improve recall, precision, and analysis cost. We propose a new approach to detect third-party libraries in obfuscated apps. The approach relies on obfuscation-resilient code features derived from the interprocedural structure and behavior of the app (e.g., call graphs of methods). The design of our approach is informed by close examination of the code features preserved by typical Android obfuscators. To reduce analysis cost, we use similarity digests as an efficient mechanism for identifying a small number of likely matches. We implemented this approach in the Orlis library detection tool. As demonstrated by our experimental results, Orlis advances the state of the art and presents an attractive choice for detection of third-party libraries in Android apps.
VNF chain allocation and management at data center scale

Recent advances in network function virtualization have prompted the research community to consider data-center-scale deployments. However, existing tools, such as E2 and SOL, limit VNF chain allocation to rack-scale and provide limited support for management of allocated chains. We define a narrow API to let data center tenants and operators allocate and manage arbitrary VNF chain topologies, and we introduce NetPack, a new stochastic placement algorithm, to implement this API at data-center-scale. We prototyped the resulting system, dubbed Daisy, using the Sonata platform. In data-center-scale simulations on realistic scenarios and topologies that are orders of magnitude larger than prior work, we achieve in all cases an allocation density within 96% of a recently introduced, theoretically complete, constraint-solver-based placement engine, while being 82x faster on average. In detailed emulation with real packet traces, we find that Daisy performs each of our six API calls with at most one second of throughput drop.
The matrix protein Fibulin-3 promotes KISS1R induced triple negative breast cancer cell invasion

Breast cancer is a leading cause of cancer mortality. In particular, triple negative breast cancer (TNBC) comprise a heterogeneous group of basal-like tumors lacking estrogen receptor (ERα), progesterone receptor (PR) and HER2 (ErbB2). TNBC represents 15–20% of all breast cancers and occurs frequently in women under 50 years of age. Unfortunately, these patients lack targeted therapy, are typically high grade and metastatic at time of diagnosis. The mechanisms regulating metastasis remain poorly understood. We have previously shown that the kisspeptin receptor, KISS1R stimulates invasiveness of TNBC cells. In this report, we demonstrate that KISS1R signals via the secreted extracellular matrix protein, fibulin-3, to regulate TNBC invasion. We found that the fibulin-3 gene is amplified in TNBC primary tumors and that plasma fibulin-3 levels are elevated in TNBC patients compared to healthy subjects. In this study, we show that KISS1R activation increases fibulin-3 expression and secretion. We show that fibulin-3 regulates TNBC metastasis in a mouse experimental metastasis xenograft model and signals downstream of KISS1R to stimulate TNBC invasion, by activating matrix metalloproteinase 9 (MMP-9) and the MAPK pathway. These results identify fibulin-3 as a new downstream mediator of KISS1R signaling and as a potential biomarker for TNBC progression and metastasis, thus revealing KISS1R and fibulin-3 as novel drug targets in TNBC.
A Video-based Attack for Android Pattern Lock

Pattern lock is widely used for identification and authentication on Android devices. This article presents a novel video-based side channel attack that can reconstruct Android locking patterns from video footage filmed using a smartphone. As a departure from previous attacks on pattern lock, this new attack does not require the camera to capture any content displayed on the screen. Instead, it employs a computer vision algorithm to track the fingertip movement trajectory to infer the pattern. Using the geometry information extracted from the tracked fingertip motions, the method can accurately infer a small number of (often one) candidate patterns to be tested by an attacker. We conduct extensive experiments to evaluate our approach using 120 unique patterns collected from 215 independent users. Experimental results show that the proposed attack can reconstruct over 95% of the patterns in five attempts. We discovered that, in contrast to most people’s belief, complex patterns do not offer stronger protection under our attacking scenarios. This is demonstrated by the fact that we are able to break all but one complex patterns (with a 97.5% success rate) as opposed to 60% of the simple patterns in the first attempt. We demonstrate that this video-side channel is a serious concern for not only graphical locking patterns but also PIN-based passwords, as algorithms and analysis developed from the attack can be easily adapted to target PIN-based passwords. As a countermeasure, we propose to change the way the Android locking pattern is constructed and used. We show that our proposal can successfully defeat this video-based attack. We hope the results of this article can encourage the community to revisit the design and practical use of Android pattern lock.
Investigation of a Co-Design Approach for a Civic Engagement Mobile Application

Given the continued state of the digital divide, the South African government has made several strides to advance the availability of broadband internet to meet objectives outlined in its National Development Plan. Government at all three levels has over the recent years been deploying broadband internet infrastructure and associated access infrastructure, such as the Smart Cape programme, to citizens in poor communities. This has provided government an opportunity to develop innovative e-service delivery initiatives. Various surveys have indicated that mobile phones are the most prolific amongst citizens in poor communities, and thus is the basis on which government can address community-based problems. Unfortunately, citizens in marginalized communities are not enjoying the e-government benefits associated with broadband internet. Mobile applications, which are mainly developed by NGOs and private companies, do exist for citizens, but are either being rejected or aren’t easily accessible or usable given the lack of citizen involvement in the application development process. In light of this there is a need to investigate co-creation methodologies which involve grassroots communities, given that traditional systems application methods may not be suitable for the development of e-government applications in such circumstances. In light of this problem, this paper reports on an investigation in which a community based co-creation approach for the development of a mobile application was investigated. For the purpose of the research a Civic Engagement problem was identified, using a group of randomly selected users in a township in the Western Cape, South Africa.
Immunomodulation as Therapy for Fungal Infection: Are We Closer?

Invasive fungal disease (IFD) causes significant morbidity in immunocompromised patients due to their weakened immune system. Immunomodulatory therapy, in synergy with existing antifungal therapy, is an attractive option to enhance their immune system and aid clearance of these opportunistic pathogens. From a scientific and clinical perspective, we explore the immunotherapeutic options to augment standard antifungal drugs for patients with an IFD. We discuss the range of immunomodulatory therapies being considered in IFD – from cytokines, including G-CSF, GM-CSF, M-CSF, IFN-γ, and cytokine agonists, to cellular therapies, consisting of granulocyte transfusion, adoptive T-cell, CAR T-cell, natural killer cell therapies, and monoclonal antibodies. Adjunct pharmaceutical agents which augment the immunity are also being considered. Lastly, we explore the likelihood of the use of probiotics and manipulation of the microbiome/mycobiome to enhance IFD treatment outcomes.