Miracle-dz/UniFAR-FTUs huggingface dataset:
NH 4+ - MEDIATED P ROTEIN P HOSPHORYLATION IN R ICE R OOTS

NH 4+ is an important N-source which regulates plant growth and development. However, the underlying mecha- nism of NH 4+ uptake and its-mediated signaling is poorly understood. Here, we performed phosphoproteomic studies using the titanium dioxide (TiO 2 )-mediated phosphopeptides collection method together with LC-MS analysis. The results indicated that phosphorylation levels of 23 and 43 peptides/proteins involved in diverse aspects, including metabolism, transport and signaling pathway, were decreased and increased respectively after NH 4+ treatment in rice roots. Among 23 proteins detected, IDD10, a key transcription factor in ammonium signaling, was identified to reduce phosphorylation level of S313 residue. Further biochemical analysis using IDD10-GFP transgenic plants and immunoprecipitation assay confirmed that NH 4+ supply reduces IDD10 phosphorylation level. Phosphorylation of ammonium transporter 1;1 (AMT1;1) was increased upon NH 4+ treatment. Interestingly, phosphorylation of T446, a rice specific residue against Arabidopsis was identified. It was also established that phosphorylation of T452 is conserved with T460 of Arabidopsis AMT1;1. Yeast complementa- tion assay with transformation of phosphomimic forms of AMT1;1 (T446/D and T452/D) into 31019b strain revealed that phosphorylation at T446 and T452 residues abolished AMT1;1 activity, while their plasma mem- brane localization was not changed. Our analyses show that many proteins were phosphorylated or dephos-phorylated by NH 4+ that may provide important evidence for studying ammonium uptake and its mediated sig- naling by which rice growth and development are regulated.
Interleukin-6/interleukin-6 receptor pathway as a new therapy target in ovarian cancer

Epithelial ovarian cancer is a major problem as about 75% of patients develop recurrence after initial primary treatment and tumors are often chemoresistant. This article reviews the role of the interleukin-6 (IL-6) in chemoresistance and suppression of tumor immunity in ovarian cancer and provides the rationale for modulating the IL-6/ IL-6 receptor (IL-6R) induced pathway as a potential new target for the treatment of ovarian cancer. IL-6 is elevated in serum and ascites of ovarian cancer patients and increased IL-6 levels correlate with chemoresistance and poor prognosis in these patients. IL-6 induced Jak/Stat3, Ras/ MEK/ERK and PI3K/Ras signaling pathways lead to cell survival, proliferation, angiogenesis, and confers resistance to apoptosis induced by conventional therapies. Furthermore, IL-6 induces tumor-promoting macrophages which are known to foster tumor growth and suppress local immunity. However, direct proof of the clinical impact of IL-6 blocking on disease progression is missing necessiting further studies in which the IL-6(R) pathway is modulated and its clinical impact on (epithelial) ovarian cancer is tested. TGF-β and IL-10) and can trigger IL-6 production by APCs. IL-6 stimulates B7-H4 expression on macrophages and B7-H4 loaded macrophages cause cell cycle arrest. of NFκB and VEGF. CDDO-Me inhibited IL-6 secretion and phosphorylation and consequent Stat3 nuclear translocation in ovarian cancer cell lines, including drug resistant lines. CDDO-Me down-regulated the anti-apoptotic proteins Bcl-XL Bcl-2 paclitaxel resistant cell lines.
The Bedrock of Byzantine Fault Tolerance: A Unified Platform for BFT Protocol Design and Implementation

Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by decentralized data management systems with non-trustworthy infrastructures, e.g., permissioned blockchains. BFT protocols cover a broad spectrum of design dimensions from infrastructure settings such as the communication topology, to more technical features such as commitment strategy and even fundamental social choice properties like order-fairness. The proliferation of different BFT protocols has rendered it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. This paper presents Bedrock, a unified platform for BFT protocols design, analysis, implementation, and experiments. Bedrock proposes a design space consisting of a set of design choices capturing the trade-offs between different design space dimensions and providing fundamentally new insights into the strengths and weaknesses of BFT protocols. Bedrock enables users to analyze and experiment with BFT protocols within the space of plausible choices, evolve current protocols to design new ones, and even uncover previously unknown protocols. Our experimental results demonstrate the capability of Bedrock to uniformly evaluate BFT protocols in new ways that were not possible before due to the diverse assumptions made by these protocols. The results validate Bedrock's ability to analyze and derive BFT protocols.
Age Appropriate Design: Assessment of TikTok, Twitch, and YouTube Kids

The presence of children in the online world is increasing at a rapid pace. As children interact with services such as video sharing, live streaming, and gaming, a number of concerns arise regarding their security and privacy as well as their safety. To address such concerns, the UK's Information Commissioner's Office (ICO) sets out 15 criteria alongside a risk management process for developers of online services for children. We present an analysis of 15 ICO criteria for age appropriate design. More precisely, we investigate whether those criteria provide actionable requirements for developers and whether video sharing and live streaming platforms that are used by children of different age ranges (i.e., TikTok, Twitch and YouTube Kids) comply with them. Our findings regarding the ICO criteria suggest that some criteria such as age verification and transparency provide adequate guidance for assessment whereas other criteria such as parental controls, reporting of inappropriate content, and handling of sensitive data need further clarification. Our findings regarding the platforms themselves suggest that they choose to implement the simplest form of self-declared age verification with limited parental controls and plenty of opportunities.
A New Kind of Adversarial Example

Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human will most likely make a mistake if forced to decide (or opt not to decide at all). Existing targeted attacks can be reformulated to synthesize such adversarial examples. Our proposed attack, dubbed NKE, is similar in essence to the fooling images, but is more efficient since it uses gradient descent instead of evolutionary algorithms. It also offers a new and unified perspective into the problem of adversarial vulnerability. Experimental results over MNIST and CIFAR-10 datasets show that our attack is quite efficient in fooling deep neural networks. Code is available at https://github.com/aliborji/NKE.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning

Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans"in the wild"remains a formidable challenge. In this work we develop a real-time, open-ended dialogue system that uses reinforcement learning (RL) to power a bot's conversational skill at scale. Our work pairs the succinct embedding of the conversation state generated using SOTA (supervised) language models with RL techniques that are particularly suited to a dynamic action space that changes as the conversation progresses. Trained using crowd-sourced data, our novel system is able to substantially exceeds the (strong) baseline supervised model with respect to several metrics of interest in a live experiment with real users of the Google Assistant.
Bayesian regularization of empirical MDPs

In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learned from the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results in suboptimal performance, thus calling for solutions with better generalization performance. In this work we take a Bayesian perspective and regularize the objective function of the Markov decision process with prior information in order to obtain more robust policies. Two approaches are proposed, one based on L 1 regularization and the other on relative entropic regularization. We evaluate our proposed algorithms on synthetic simulations and on real-world search logs of a large scale online shopping store. Our results demonstrate the robustness of regularized MDP policies against the noise present in the models.
High metal content of highly accreting quasars: Analysis of an extended sample

Context. We present an analysis of UV spectra of quasars at intermediate redshifts believed to belong to the extreme Population A (xA), aimed to estimate the chemical abundances of the broad line emitting gas. We follow the approach described in a previous work, extending the sample to 42 sources. Aims. Our aim is to test the robustness of the analysis carried out previously, as well as to confirm the two most intriguing results of this investigation: evidence of very high solar metallicities and a deviation of the relative abundance of elements with respect to solar
Can gender categorization influence the perception of animated virtual humans?

Animations have become increasingly realistic with the evolution of Computer Graphics (CG). In particular, human models and behaviors were represented through animated virtual humans, sometimes with a high level of realism. In particular, gender is a characteristic that is related to human identification, so that virtual humans assigned to a specific gender have, in general, stereotyped representations through movements, clothes, hair and colors, in order to be understood by users as desired by designers. An important area of study is finding out whether participants' perceptions change depending on how a virtual human is visually presented. Findings in this area can help the industry to guide the modeling and animation of virtual humans to deliver the expected impact to the audience. In this paper, we reproduce, through CG, a perceptual study that aims to assess gender bias in relation to a simulated baby. In the original study, two groups of people watched the same video of a baby reacting to the same stimuli, but one group was told the baby was female and the other group was told the same baby was male, producing different perceptions. The results of our study with virtual babies were similar to the findings with real babies. First, it shows that people's emotional response change depending on the character gender attribute, in this case the only difference was the baby's name. Our research indicates that by just informing the name of a virtual human can be enough to create a gender perception that impact the participant emotional answer.
Bayesian Optimization with Informative Covariance

Bayesian optimization is a methodology for global optimization of unknown and expensive objectives. It combines a surrogate Bayesian regression model with an acquisition function to decide where to evaluate the objective. Typical regression models are given by Gaussian processes with stationary covariance functions. However, these functions are unable to express prior input-dependent information, including possible locations of the optimum. The ubiquity of stationary models has led to the common practice of exploiting prior information via informative mean functions. In this paper, we highlight that these models can perform poorly, especially in high dimensions. We propose novel informative covariance functions for optimization, leveraging nonstationarity to encode preferences for certain regions of the search space and adaptively promote local exploration during optimization. We demonstrate that the proposed functions can increase the sample efficiency of Bayesian optimization in high dimensions, even under weak prior information.
High-temperature self-energy corrections to x-ray absorption spectra

Effects of finite-temperature quasiparticle self-energy corrections to x-ray absorption spectra are investigated within the finite-temperature quasiparticle local density GW approximation up to temperatures $T$ of order the Fermi temperature. To facilitate the calculations, we parametrize the quasiparticle self-energy using low-order polynomial fits. We show that temperature-driven decrease in the electron lifetime substantially broadens the spectra in the near-edge region with increasing $T$. However, the quasiparticle shift is most strongly modified near the onset of plasmon excitations.
Kantorovich type topologies on spaces of measures and convergence of barycenters

We study two topologies $\tau_{KR}$ and $\tau_K$ on the space of measures on a completely regular space generated by Kantorovich--Rubinshtein and Kantorovich seminorms analogous to their classical norms in the case of a metric space. The Kantorovich--Rubinshtein topology $\tau_{KR}$ coincides with the weak topology on nonnegative measures and on bounded uniformly tight sets of measures. A~sufficient condition is given for the compactness in the Kantorovich topology. We show that for logarithmically concave measures and stable measures weak convergence implies convergence in the Kantorovich topology. We also obtain an efficiently verified condition for convergence of the barycenters of Radon measures from a sequence or net weakly converging on a locally convex space. As an application it is shown that for weakly convergent logarithmically concave measures and stable measures convergence of their barycenters holds without additional conditions. The same is true for measures given by polynomial densities of a fixed degree with respect to logarithmically concave measures.
Decay of multi-point correlation functions in $\mathbb{Z}^d$

We prove multi-point correlation bounds in $\mathbb{Z}^d$ for arbitrary $d\geq 1$ with symmetrized distances, answering open questions proposed by Sims-Warzel \cite{SW} and Aza-Bru-Siqueira Pedra \cite{ABP}. As applications, we prove multi-point correlation bounds for the Ising model on $\mathbb{Z}^d$, and multi-point dynamical localization in expectation for uniformly localized disordered systems, which provides the first examples of this conjectured phenomenon by Bravyi-K\"onig \cite{BK}.
Understanding Teenage Perceptions and Configurations of Privacy on Instagram

As teenage use of social media platform continues to proliferate, so do concerns about teenage privacy and safety online. Prior work has established that privacy on networked publics, such as social media, is complex, requiring users to navigate not only the technical affordances on the platform but also interpersonal relationships and social norms. We investigate how teenagers think about privacy on the popular image-sharing platform, Instagram. We draw on an online survey (N=144) and semi-structured interviews (N=21) with teenagers, ages 13-19, to gain a better understanding how teenagers configure privacy on the popular image-sharing platform Instagram and why they make these privacy decisions. Finally, based on our findings, we provide design recommendations towards the design of better privacy controls for promoting teenage safety online.
Ellipsis: Towards Efficient System Auditing for Real-Time Systems

—System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for Real-Time Systems (RTS). The transparency provided by system auditing would be of tremendous beneﬁt in a variety of security-critical RTS domains, ( e.g., autonomous vehicles); however, if audit mechanisms are not carefully integrated into RTS, auditing can be rendered ineffectual and violate the real-world temporal requirements of the RTS. In this paper, we demonstrate how to adapt commodity audit frameworks to RTS. Using Linux Audit as a case study, we ﬁrst demonstrate that the volume of audit events generated by commodity frameworks is unsustainable within the temporal and resource constraints of real-time (RT) applications. To address this, we present Ellipsis , a set of kernel-based reduction techniques that leverage the periodic repetitive nature of RT applications to aggressively reduce the costs of system-level auditing. Ellipsis generates succinct descriptions of RT applications’ expected activity while retaining a detailed record of unexpected activities, enabling analysis of suspicious activity while meeting temporal constraints. Our evaluation of Ellipsis , using ArduPilot (an open-source autopilot application suite) demonstrates up to 93% reduction in audit log generation. Ellipsis demonstrates a promising path forward for auditing RTS.
H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respectively. To alleviate this problem, we propose a dual camera system, in which one camera captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details, and the other captures low-spatial-resolution high-frame-rate (LSR-HFR) videos with smooth temporal details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to enhance both camera views to high spatiotemporal resolution (HSTR) for reconstructing the H2-Stereo video effectively. We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusion method in feature domain is proposed to minimize occlusion-induced warping ghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end manner using our collected high-quality Stereo Video dataset from YouTube. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods for both views on synthetic data and camera-captured real data with large disparity. Ablation studies explore various aspects, including spatiotemporal resolution, camera baseline, camera desynchronization, long/short exposures and applications, of our system to fully understand its capability for potential applications.
Analyzing social media with crowdsourcing in Crowd4SDG

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data analysis project usually requires time and technical skills. This study presents an approach that provides flexible support for analyzing social media, particularly during emergencies. Different use cases in which social media analysis can be adopted are introduced, and the challenges of retrieving information from large sets of posts are discussed. The focus is on analyzing images and text contained in social media posts and a set of automatic data processing tools for filtering, classification, and geolocation of content with a human-in-the-loop approach to support the data analyst. Such support includes both feedback and suggestions to configure automated tools, and crowdsourcing to gather inputs from citizens. The results are validated by discussing three case studies developed within the Crowd4SDG H2020 European project.
Investigating the Impact of Continuous Integration Practices on the Productivity and Quality of Open-Source Projects

Background: Much research has been conducted to investigate the impact of Continuous Integration (CI) on the productivity and quality of open-source projects. Most of studies have analyzed the impact of adopting a CI server service (e.g, Travis-CI) but did not analyze CI sub-practices. Aims: We aim to evaluate the impact of five CI sub-practices with respect to the productivity and quality of GitHub open-source projects. Method: We collect CI sub-practices of 90 relevant open-source projects for a period of 2 years. We use regression models to analyze whether projects upholding the CI sub-practices are more productive and/or generate fewer bugs. We also perform a qualitative document analysis to understand whether CI best practices are related to a higher quality of projects. Results: Our findings reveal a correlation between the Build Activity and Commit Activity sub-practices and the number of merged pull requests. We also observe a correlation between the Build Activity, Build Health and Time to Fix Broken Builds sub-practices and number of bug-related issues. The qualitative analysis reveals that projects with the best values for CI sub-practices face fewer CI-related problems compared to projects that exhibit the worst values for CI sub-practices. Conclusions: We recommend that projects should strive to uphold the several CI sub-practices as they can impact in the productivity and quality of projects.
Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data

Amid growing environmental concerns, interactive displays of data constitute an important tool for exploring and understanding the impact of climate change on the planet’s ecosystemic integrity. This paper presents Tōkyō kion-on, a query-based sonification model of Tokyo’s air temperature from 1876 to 2021. The system uses a recurrent neural network architecture known as LSTM with attention trained on a small dataset of Japanese melodies and conditioned upon said atmospheric data. After describing the model’s implementation, a brief comparative illustration of the musical results is presented, along with a discussion on how the exposed hyper-parameters can promote active and non-linear exploration of the data.
Nonuniqueness of weak solutions to the dissipative Aw-Rascle model

We prove nonuniqueness of weak solutions to multi-dimensional generalisation of the Aw-Rascle model of vehicular traﬃc. Our generalisation includes the velocity oﬀset in a form of gradient of density function, which results in a dissipation eﬀect, similar to viscous dissipation in the compressible viscous ﬂuid models. We show that despite this dissipation, the extension of the method of convex integration can be applied to generate inﬁnitely many weak solutions connecting arbitrary initial and ﬁnal states. We also show that for certain choice of data, ill posedness holds in the class of admissible weak solutions.