Miracle-dz/UniFAR-FTUs huggingface dataset:
How Anticipatory Design Will Challenge Our Relationship with Technology

Anticipatory Design is an upcoming design pattern within the field of predictive user experiences (UX). The premise behind this pattern is to reduce cognitive load of users by making decisions on behalf of them. Despite its promise, little research has been done towards possible implications that may come with Anticipatory Design and predictive user experiences. Ethical challenges like data, privacy and experience bubbles could inhibit the development of predictive UX. A research has been conducted to identify both ethical as design challenges that come with the development of predictive UX and application of Anticipatory Design as design pattern. With as overarching question “How Anticipatory Design might challenge our relationship with technology”.
Chloroplastic ATP Synthase Alleviates Photoinhibition of Photosystem I in Tobacco Illuminated at Chilling Temperature

Chloroplastic ATP synthase plays a significant role in the regulation of proton motive force (pmf) and proton gradient (ΔpH) across the thylakoid membranes. However, the regulation of chloroplastic ATP synthase at chilling temperature and its role in photoprotection are little known. In our present study, we examined the chlorophyll fluorescence, P700 signal, and electrochromic shift signal at 25°C, and 6°C in tobacco (Nicotiana tabacum L. cv. Samsun). Although photosynthetic electron flow through both PSI and PSII were severely inhibited at 6°C, non-photochemical quenching and P700 oxidation ratio were largely increased. During the photosynthetic induction under high light, the formation of pmf at 6°C was similar to that at 25°C. However, the ΔpH was significantly higher at 6°C, owing to the decreased activity of chloroplastic ATP synthase (gH+). During illumination at 6°C and high light, a high ΔpH made PSI to be highly oxidized, preventing PSI from photoinhibition. These results indicate that the down-regulation of gH+ is critical to the buildup of ΔpH at low temperature, adjusting the redox state of PSI, and thus preventing photodamage to PSI. Our findings highlight the importance of chloroplastic ATP synthase in photoprotection at chilling temperature.
SeqVItA: Sequence Variant Identification and Annotation Platform for Next Generation Sequencing Data

The current trend in clinical data analysis is to understand how individuals respond to therapies and drug interactions based on their genetic makeup. This has led to a paradigm shift in healthcare; caring for patients is now 99% information and 1% intervention. Reducing costs of next generation sequencing (NGS) technologies has made it possible to take genetic profiling to the clinical setting. This requires not just fast and accurate algorithms for variant detection, but also a knowledge-base for variant annotation and prioritization to facilitate tailored therapeutics based on an individual's genetic profile. Here we show that it is possible to provide a fast and easy access to all possible information about a variant and its impact on the gene, its protein product, associated pathways and drug-variant interactions by integrating previously reported knowledge from various databases. With this objective, we have developed a pipeline, Sequence Variants Identification and Annotation (SeqVItA) that provides end-to-end solution for small sequence variants detection, annotation and prioritization on a single platform. Parallelization of the variant detection step and with numerous resources incorporated to infer functional impact, clinical relevance and drug-variant associations, SeqVItA will benefit the clinical and research communities alike. Its open-source platform and modular framework allows for easy customization of the workflow depending on the data type (single, paired, or pooled samples), variant type (germline and somatic), and variant annotation and prioritization. Performance comparison of SeqVItA on simulated data and detection, interpretation and analysis of somatic variants on real data (24 liver cancer patients) is carried out. We demonstrate the efficacy of annotation module in facilitating personalized medicine based on patient's mutational landscape. SeqVItA is freely available at https://bioinf.iiit. ac.in/seqvita.
Positive and Negative Feedback for Linearity Improvement and PVT Compensation of the Ramp Generator

Linearity of ramp signals is one of the most important aspects for many applications such as single-slope analog to digital converters (ADCs); another important aspect is the total power dissipation. Applications like high-resolution single-slope ADCs that can be used in portable devices demanded accurate ramp generator with low power dissipation. This paper presents a low power ramp generator with linearity improvement that achieved by a positive feedback circuit and negative feedback for compensation of the variations in process, voltage and temperature. Derived equations of the proposed ramp generator circuit show that linearity of the output ramp, with proper choosing of device sizes, can be enhanced significantly. Also, for proving of linearity enhancement, the circuit design and post-layout simulations were done in TSMC 0.18[Formula: see text][Formula: see text]m and 90[Formula: see text]nm CMOS technologies. Simulation results show that linearity of the circuit improved by a factor of 8 and total ramp resolution improved about 3 bit, whereas power dissipation of the circuit is about 8[Formula: see text][Formula: see text]W and entire layout core area is near 800[Formula: see text][Formula: see text]m2.
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses

We study the problem of finding a universal (image-agnostic) perturbation to fool machine learning (ML) classifiers (e.g., neural nets, decision tress) in the hard-label black-box setting. Recent work in adversarial ML in the white-box setting (model parameters are known) has shown that many state-of-the-art image classifiers are vulnerable to universal adversarial perturbations: a fixed human-imperceptible perturbation that, when added to any image, causes it to be misclassified with high probability Kurakin et al. [2016], Szegedy et al. [2013], Chen et al. [2017a], Carlini and Wagner [2017]. This paper considers a more practical and challenging problem of finding such universal perturbations in an obscure (or black-box) setting. More specifically, we use zeroth order optimization algorithms to find such a universal adversarial perturbation when no model information is revealed-except that the attacker can make queries to probe the classifier. We further relax the assumption that the output of a query is continuous valued confidence scores for all the classes and consider the case where the output is a hard-label decision. Surprisingly, we found that even in these extremely obscure regimes, state-of-the-art ML classifiers can be fooled with a very high probability just by adding a single human-imperceptible image perturbation to any natural image. The surprising existence of universal perturbations in a hard-label black-box setting raises serious security concerns with the existence of a universal noise vector that adversaries can possibly exploit to break a classifier on most natural images.
Parliamentarians in government delegations: An old question still not answered

Why do governments include parliamentarians in the delegations to international negotiations? Conduct of the diplomatic negotiations is among the most tightly controlled prerogatives of the executive, and executives have been historically dominant in the conduct of foreign policy. This article draws on the participation of members of parliaments in national delegations to the Review Conferences of the Non-Proliferation Treaty over the past 40 years. The emerging patterns show that legitimation through oversight is unlikely to be the reason for participation. Drawing on literature on institutional variation in legislative–executive relations, the data indicate that executives are more interested in co-opting the parliamentarians, in order to make them less opposed to the government’s policy.
An Algorithm for Copy-Move Forgery Detection in Image Time Series Based on the Anomalies Detection

The present work is devoted to the development and investigation of the algorithm for detection intentional distortions (forgeries) of a single digital image in an image time series (time sequences) of one scene. The proposed algorithm consists of three stages. At the ﬁrst stage, a set of errors that were calculated during reconstruction the fragments of the analyzed image by the ’neighboring’ ones are estimated. After errors were calculated throughout image, we analyze their distribution on the second stage. At the ﬁnal stage, fragments of the analyzed image that are anomalies are selected as ’suspicious’. The proposed algorithm, unlike existing algorithms, will allow uniﬁed detection of such attacks as intra-image copy-move and inter-image copy-move. Also, copy-move fragments may fall under geometric transformation, linear enhancement and other distortions. The investigation results of intra-image copy-move and inter-image copy-move detection using the proposed solution are presented.
A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.
Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-Cameras

Spectral methods such as the improved Fourier Mellin Invariant (iFMI) transform have proved to be faster, more robust and accurate than feature based methods on image registration. However, iFMI is restricted to work only when the camera moves in 2D space and has not been applied on omni-cameras images so far. In this work, we extend the iFMI method and apply a motion model to estimate an omni-camera’s pose when it moves in 3D space. In the experiment section, we compare the extended iFMI method against ORB and AKAZE feature based approaches on three datasets, showing different types of environments: office, lawn and urban scenery (MPI-omni dataset). The results show that our method reduces the error of the camera pose estimation two to three times with respect to the feature registration techniques, while offering lower processing times.
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms

Deep learning (DL) has recently achieved tremendous success in a variety of cutting-edge applications, e.g., image recognition, speech and natural language processing, and autonomous driving. Besides the available big data and hardware evolution, DL frameworks and platforms play a key role to catalyze the research, development, and deployment of DL intelligent solutions. However, the difference in computation paradigm, architecture design and implementation of existing DL frameworks and platforms brings challenges for DL software development, deployment, maintenance, and migration. Up to the present, it still lacks a comprehensive study on how current diverse DL frameworks and platforms influence the DL software development process. In this paper, we initiate the first step towards the investigation on how existing state-of-the-art DL frameworks (i.e., TensorFlow, Theano, and Torch) and platforms (i.e., server/desktop, web, and mobile) support the DL software development activities. We perform an in-depth and comparative evaluation on metrics such as learning accuracy, DL model size, robustness, and performance, on state-of-the-art DL frameworks across platforms using two popular datasets MNIST and CIFAR-10. Our study reveals that existing DL frameworks still suffer from compatibility issues, which becomes even more severe when it comes to different platforms. We pinpoint the current challenges and opportunities towards developing high quality and compatible DL systems. To ignite further investigation along this direction to address urgent industrial demands of intelligent solutions, we make all of our assembled feasible toolchain and dataset publicly available.
A systematic review on the impact of gestational Lyme disease in humans on the fetus and newborn

Lyme disease (LD), caused by bacteria of the Borrelia burgdorferi sensu lato species complex, is the most common vector-borne disease in North America and Europe. A systematic review (SR) was conducted to summarize the global literature on adverse birth outcomes associated with gestational LD in humans. The SR followed an a priori protocol of pretested screening, risk of bias, and data extraction forms. Data were summarized descriptively and random effects meta-analysis (MA) was used where appropriate. The SR identified 45 relevant studies, 29 describing 59 cases reported as gestational LD in the United States, Europe, and Asia (1969–2017). Adverse birth outcomes included spontaneous miscarriage or fetal death (n = 12), newborn death (n = 8), and newborns with an abnormal outcome (e.g. hyperbilirubinemia, respiratory distress and syndactyly) at birth (n = 16). Only one report provided a full case description (clinical manifestations in the mother, negative outcome for the child, and laboratory detection of B. burgdorferi in the child) that provides some evidence for vertical transmission of B. burgdorferi that has negative consequences for the fetus. The results of 17 epidemiological studies are included in this SR. Prevalence of adverse birth outcomes in an exposed population (defined by the authors as: gestational LD, history of LD, tick bites or residence in an endemic area) was compared to that in an unexposed population in eight studies and no difference was reported. A meta-analysis of nine studies showed significantly fewer adverse birth outcomes in women reported to have been treated for gestational LD (11%, 95%CI 7–16) compared to those who were not treated during pregnancy (50%, 95%CI 30–70) providing indirect evidence of an association between gestational LD and adverse birth outcomes. Other risk factors investigated; trimester of exposure, length of LD during pregnancy, acute vs. disseminated LD at diagnosis, and symptomatic LD vs. seropositive women with no LD symptoms during pregnancy were not significantly associated with adverse birth outcomes. This SR summarizes evidence from case studies that provide some limited evidence for transplacental transmission of B. burgdorferi. There was inconsistent evidence for adverse birth outcomes of gestational LD in the epidemiological research, and uncommon adverse outcomes for the fetus may occur as a consequence of gestational LD. The global evidence does not fully characterize the potential impact of gestational LD, and future research that addresses the knowledge gaps may change the findings in this SR. Given the current evidence; prompt diagnosis and treatment of LD during pregnancy is recommended.
Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity

In bipartite matching problems, vertices on one side of a bipartite graph are paired with those on the other. In its online variant, one side of the graph is available offline, while the vertices on the other side arrive online. When a vertex arrives, an irrevocable and immediate decision should be made by the algorithm; either match it to an available vertex or drop it. Examples of such problems include matching workers to firms, advertisers to keywords, organs to patients, and so on. Much of the literature focuses on maximizing the total relevance—modeled via total weight—of the matching. However, in many real-world problems, it is also important to consider contributions of diversity: hiring a diverse pool of candidates, displaying a relevant but diverse set of ads, and so on. In this paper, we propose the Online Submodular Bipartite Matching (OSBM) problem, where the goal is to maximize a submodular function f over the set of matched edges. This objective is general enough to capture the notion of both diversity (e.g., a weighted coverage function) and relevance (e.g., the traditional linear function)—as well as many other natural objective functions occurring in practice (e.g., limited total budget in advertising settings). We propose novel algorithms that have provable guarantees and are essentially optimal when restricted to various special cases. We also run experiments on real-world and synthetic datasets to validate our algorithms.
Zero-Knowledge Identification Scheme Using LDPC Codes

SUMMARY Recently, IoT compatible products have been popular, and various kinds of things are IoT compliant products. In these devices, cryptosystems and authentication are not treated properly, and security measures for IoT devices are not su ﬃ cient. Requirements of authentication for IoT devices are power saving and one-to-many communication. In this paper, we propose a zero-knowledge identiﬁcation scheme using LDPC codes. In the proposed scheme, the zero-knowledge identiﬁcation scheme that relies on the binary syndrome decoding problem is improved and the computational cost of identiﬁcation is reduced by using the sparse parity-check matrix of the LDPC codes. In addition, the security level, computational cost and safety of the proposed scheme are discussed in detail.
Histological analysis of post-eruption tooth wear adaptations, and ontogenetic changes in tooth implantation in the acrodontan squamate Pogona vitticeps

Teeth have been a focus of research in both extinct and extant taxa alike; a significant portion of dental literature is concerned with dental patterning and replacement. Most non-mammalian vertebrates continuously replace their dentition but an anomalous group of squamates has forgone this process in only having one tooth generation; these squamates all have apically implanted teeth, a condition known as acrodonty. Acrodont dentition and various characteristics attributed to it, including a lack of replacement, have often been defined ambiguously. This study explores this type of implantation through histology in the ontogeny of the acrodont agamid Pogona vitticeps. The non-replacing teeth of this squamate provides an opportunity to study wear adaptations, maintenance of occlusion in a non-mammalian system, and most importantly post-eruption changes in the tooth bone interface. In this study the post-eruption changes combined with dental wear likely gives the appearance of acrodont implantation.
Constructions, Frames and Event Structure

Charles Fillmore developed both the theory of construction grammar and the theory of frame semantics. Fillmore’s original frame semantic model included broad frames such as Commercial Transaction and Risk. FrameNet II (the current incarnation) has a complex lattice of frame-to-frame relations, in which the original frames are often abstract frames, and/or have been decomposed into distinct concrete frames. The frame-to-frame relations are of different types beyond simple taxonomic relations. The concrete frames are often more restricted in their argument structure construction possibilities than the original, more general frames. We argue that some frame-to-frame relations effectively represent the internal structure of the core events in the frame; the event structures are associated with different argument structure constructions. Introducing event structure into the frame semantic representation offers the potential to simplify the FrameNet lattice. Brief analyses of the Commercial Trans-action and Risk frames are presented.
Successful Liver Transplantation Case Report from a Deceased Donor with Sickle Cell Anemia

There is a worldwide problem of waiting time and mortality rate associated with remaining on the waiting list for a liver transplant. However, some situations have been encouraging in terms of determining appropriate recipients and expanding the donor criteria. We herein report a case of useful liver donor with sickle cell anemia for liver transplantation. Here we described a case of liver transplantation from a donor with sickle cell anemia to a recipient with hepatocellular carcinoma who was deemed to be at risk of tumor growth and at risk of being dropped from the waiting list. The literature reveals the importance of using safe donors, and we describe the benefits of using a safe, deceased liver donor with sickle cell anemia who was an adequate option for liver transplantation.
Finite-Time Stability of Delayed Memristor-Based Fractional-Order Neural Networks

This paper studies one type of delayed memristor-based fractional-order neural networks (MFNNs) on the finite-time stability problem. By using the method of iteration, contracting mapping principle, the theory of differential inclusion, and set-valued mapping, a new criterion for the existence and uniqueness of the equilibrium point which is stable in finite time of considered MFNNs is established when the order <inline-formula> <tex-math notation="LaTeX">$ \alpha $ </tex-math></inline-formula> satisfies <inline-formula> <tex-math notation="LaTeX">$ {0< \alpha < 1}$ </tex-math></inline-formula>. Then, when <inline-formula> <tex-math notation="LaTeX">${1< \alpha < 2}$ </tex-math></inline-formula>, on the basis of generalized Gronwall inequality and Laplace transform, a sufficient condition ensuring the considered MFNNs stable in finite time is given. Ultimately, simulation examples are proposed to demonstrate the validity of the results.
The Unlicensed and Off-Label Prescription of Medications in General Paediatric Ward: An Observational Study

Background: Unlicensed (UL) and Off-label (OL) prescription of medications is common in paediatrics and does not constitute negligent practice since there is often no approved alternative according to FDA bulary. Aim: The study aimed to determine the current frequency of UL and OL prescriptions in children from one month to 12 years of age in a Paediatric Inpatient Unit (PIU). Methods: This is an observational, prospective study, reviewing the prescriptions of all patients admitted to the PIU in a university hospital in a single week in August 2014 and a single week in January 2015. Results: We included 157 patients of median age 18 months and median length of stay 24 days. There were 1,328 prescription items (average of 8.4 items/patient) and only two patients without UL/OL use. During the winter season (August), 27% of prescriptions were classified as UL and 44.6% as OL, and during summer (January), 29.6% as UL and 45.1% as OL. We identified 188 medications, of which the most prescribed were paracetamol (11%) and dipyrone (9.5%). The most frequent OL classification was regarding drug formulation (15.8%). In the winter week, the most frequent reasons for admission were respiratory (44%), followed by other clinical causes (CC) (17.3%), while in the summer week, they were CC (26.3%), followed by surgical and gastrohepatic (23.7%). Conclusion: The OL prescription of medicines for children in Brazil is in accordance with the international literature. The higher prevalence of OL due to formulation found in this study is related to the use of formulations other than those used by the FDA.