Miracle-dz/UniFAR-FTUs huggingface dataset:
Vancomycin-Loaded Nanoparticles Enhance Sporicidal and Antibacterial Efficacy for Clostridium difficile Infection

Current antibiotic treatments fail to eliminate the Clostridium difficile (C. difficile) spores and induce dysbiosis and intestinal inflammation via off-target effect, which causes refractory C. difficile infection raise an unmet need for a spore-specific antimicrobial treatment. We developed a sporicidal and antimicrobial vancomycin-loaded spore-targeting iron oxide nanoparticle (van-IONP) that selectively binds to C. difficile spores. Cryo-electron microscopy showed that vancomycin-loaded nanoparticles can target and completely cover spore surfaces. They not only successfully delayed the germination of the spores but also inhibited ∼50% of vegetative cell outgrowth after 48 h of incubation. The van-IONPs also inhibited the interaction of spores with HT-29 intestinal mucosal cells in vitro. In a murine model of C. difficile infection, the van-IONP significantly protected the mice from infected by C. difficile infection, reducing intestinal inflammation, and facilitated superior mucosal viability compared with equal doses of free vancomycin. This dual-function targeted delivery therapy showed advantages over traditional therapeutics in treating C. difficile infection.
Learning When-to-Treat Policies

Abstract Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may choose between postponing treatment (watchful waiting) and prescribing one of several available treatments during the many visits from a patient. We develop an “advantage doubly robust” estimator for learning such dynamic treatment rules using observational data under the assumption of sequential ignorability. We prove welfare regret bounds that generalize results for doubly robust learning in the single-step setting, and show promising empirical performance in several different contexts. Our approach is practical for policy optimization, and does not need any structural (e.g., Markovian) assumptions. Supplementary materials for this article are available online.
How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits

We significantly reduce the cost of factoring integers and computing discrete logarithms in finite fields on a quantum computer by combining techniques from Shor 1994, Griffiths-Niu 1996, Zalka 2006, Fowler 2012, Ekerå-Håstad 2017, Ekerå 2017, Ekerå 2018, Gidney-Fowler 2019, Gidney 2019. We estimate the approximate cost of our construction using plausible physical assumptions for large-scale superconducting qubit platforms: a planar grid of qubits with nearest-neighbor connectivity, a characteristic physical gate error rate of 10−3, a surface code cycle time of 1 microsecond, and a reaction time of 10 microseconds. We account for factors that are normally ignored such as noise, the need to make repeated attempts, and the spacetime layout of the computation. When factoring 2048 bit RSA integers, our construction's spacetime volume is a hundredfold less than comparable estimates from earlier works (Van Meter et al. 2009, Jones et al. 2010, Fowler et al. 2012, Gheorghiu et al. 2019). In the abstract circuit model (which ignores overheads from distillation, routing, and error correction) our construction uses 3n+0.002nlg⁡n logical qubits, 0.3n3+0.0005n3lg⁡n Toffolis, and 500n2+n2lg⁡n measurement depth to factor n-bit RSA integers. We quantify the cryptographic implications of our work, both for RSA and for schemes based on the DLP in finite fields.
Title The MK2 cascade regulates mGluR-dependent synaptic plasticity and reversal learning

The ability to either erase or update the memories of a previously learned spatial task is an essential process that is required to modify behaviour in a changing environment. Current evidence suggests that the neural representation of such cognitive flexibility involves the balancing of synaptic potentiation (acquisition of memories) with synaptic depression (modulation and updating previously acquired memories). Here we demonstrate that the p38 MAPK/MAPK-activated protein kinase 2 (MK2) cascade is required to maintain the precise tuning of long-term potentiation and long-term depression at CA1 synapses of the hippocampus which is correlated with efficient reversal learning. Using the MK2 knockout (KO) mouse, we show that mGluR-LTD, but not NMDAR-LTD, is markedly impaired in mice aged between 4-5 weeks (juvenile) to 7 months (mature adult). Although the amplitude of LTP was the same as in wildtype mice, priming of LTP by the activation of group I metabotropic receptors was impaired in MK2 KO mice. Consistent with unaltered LTP amplitude and compromised mGluR-LTD, MK2 KO mice had intact spatial learning when performing the Barnes maze task, but showed specific deficits in selecting the most efficient combination of search strategies to perform the task reversal. Findings from this study suggest that the mGluR-p38-MK2 cascade is important for cognitive flexibility by regulating LTD amplitude and the priming of LTP.
Sailaja, Neelima and Crabtree, Andy and Stenton, Phil (2017) Challenges of using personal data to drive personalised electronic programme guides. In: 2017 CHI Conference on Human Factors in Computing Systems, 6-11 May 2017, Denver, Colorado, USA

Media researchers are adopting personalisation in diverse ways to deliver increasingly context-sensitive and customised media experiences. This paper explores user attitudes towards a personalised Electronic Programme Guide which tailors media recommendations based on users’ personal data. We used scenario based exploration enabled by the use of probes to convey the functionalities of data-driven Personalised EPGs and to facilitate user discussions around its potential use. Users preferred personalised EPGs over current popular EPGs but expressed a significant lack of trust in the personal data collection that drives personalisation. Users appreciated the functionalities afforded by personalisation of media but were apprehensive about the implications of the personal data being collected about them, particularly in the context of their homes. This calls for the need to design future personalised media experiences that help enhance trust in these socio-technical settings. Author
Energy Efficient Hierarchical Resource Management for Mobile Cloud Computing

Mobile Cloud Computing (MCC) is a developing technology that assists in improving the quality of the mobile services. Since the increase in mobile resources, the researchers have taken the initiative to take into contemplation resource sharing among heterogeneous mobile devices. Therefore, to design a system architecture for mobility models and resource sharing are key issues that require utmost efforts to be solved to achieve anticipated objectives. Therefore, keeping in view the desired goals, in this paper, we present a system architecture based on the hierarchical resource sharing mechanism for MCC. The proposed system architecture is divided into three domains, such as Global Cloud Server (GCS), Local ISP Server (LIS), and Gateway Server (GWS). Also, the novel paradigm for minimizing the delay in the network based on deploying Foglets at each proposed algorithm of clustering mechanism is also present. Moreover, the fuzzy rule-based scheme is proposed to eliminate the inappropriate foglets before deciding an optimal foglet for handover. A foglet selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. To check the feasibility and performance of the proposed system architecture, the mobility scenario is considered with a different speed of a mobile node ranging from very high to very low. The simulation results and an analytical model are compared with existing scheme for foglet selection by a mobile node. From the analysis and discussion, it is shown that the proposed system architecture helps in minimizing handover delay, packet loss, average queuing delay, and device lifetime. in a network.
Dynamical continuum simulation of condensed matter from first principles

Macroscale continuum mechanics simulations rely on material properties stemming from the microscale, which are normally described using phenomenological equations of state (EOS). A method is proposed for the automatic generation of first-principles unconstrained EOSs using a Gaussian process on a set of ab initio molecular dynamics simulations, thereby closing the continuum equations. We illustrate it on a hyperelasticity simulation of bulk silicon using density-functional theory (DFT), following the dynamics of shock waves after a cylindrical region is instantaneously heated.
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese

Text classification approaches have usually required task-specific model architectures and huge labeled datasets. Recently, thanks to the rise of text-based transfer learning techniques, it is possible to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. In this work we focus on Japanese and show the potential use of transfer learning techniques in text classification. Specifically, we perform binary and multi-class sentiment classification on the Rakuten product review and Yahoo movie review datasets. We show that transfer learning-based approaches perform better than task-specific models trained on 3 times as much data. Furthermore, these approaches perform just as well for language modeling pre-trained on only 1/30 of the data. We release our pre-trained models and code as open source.
Learning Pairwise Interactions with Bayesian Neural Networks

Estimating pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of unknown form. First, the relationship between the features and the output is modelled using a Bayesian neural network, leveraging on the representation capability of deep neural networks. Second, interaction effects and their uncertainty are estimated from the trained model. For the second step we propose a simple and intuitive global interaction measure: Expected Integrated Hessian (EIH), whose uncertainty can be estimated using the predictive uncertainty. Two important properties of the Bayesian EIH are: 1. interaction estimation error is upper bounded by the prediction error of the neural network, which ensures interaction detection can be improved by training a more accurate model; 2. uncertainty of the Bayesian EIH is well-calibrated provided the prediction uncertainty is calibrated, which is easier to test and guarantee. The method outperforms the available alternatives on simulated and real-world data, and we demonstrate its ability to detect interpretable interactions also between higher-level features (at deeper layers of the neural network).
Deep Drone Racing: From Simulation to Reality With Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network. The resulting modular system is both platform independent and domain independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.
Time-Domain Mixed-Signal Vector-by-Matrix Multiplier Exploiting 1T-1R Array

The emerging mobile devices in this era of internet-of-things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing and signal processing. Vector-by-matrix multiplication (VMM) is the most prominent operation in these applications. Therefore, compact and power-efficient VMM blocks are required to perform resource-intensive computations. To this end, in this work, for the first time, we propose a time-domain mixed-signal VMM exploiting a modified configuration of 1 MOSFET-1 RRAM (1T-1R) array which overcomes the energy inefficiency of the current-mode VMM approaches based on RRAMs. In the proposed approach, the inputs and outputs are encoded in the digital domain as the duration of the pulses while the weights are realized as programmable current sinks utilizing the modified 1T-1R blocks in the analog domain. We perform a rigorous analysis of the different factors such as channel length modulation (CLM), drain-induced barrier lowering (DIBL), capacitive coupling, etc which may degrade the compute precision of the proposed VMM approach. We show that there exists a trade-off between the compute precision, dynamic range and the energy efficiency in the modified 1T-1R array based VMM approach. Therefore, we also provide the necessary design guidelines for optimizing the performance of this implementation. The preliminary results show that an effective compute precision greater than 8-bits is achievable owing to the inherent compensation effect with an energy efficiency of ~323.5 TOps/J and a throughput of 1.25 TOps/s considering the input/output (I/O) circuitry for a 200x200 VMM utilizing the proposed approach.
An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rule

We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of $\Omega \left(\frac{K\log(T)}{\epsilon} \right)$ [Shariff and Sheffet, 2018]. Our construction is based on a different algorithm, Successive Elimination [Even-Dar et al. 2002], that repeatedly pulls all remaining arms until an arm is found to be suboptimal and is then eliminated. In order to devise a private analogue of Successive Elimination we visit the problem of private stopping rule, that takes as input a stream of i.i. d samples from an unknown distribution and returns a multiplicative $(1 \pm \alpha)$-approximation of the distribution's mean, and prove the optimality of our private stopping rule. We then present the private Successive Elimination algorithm which meets both the non-private lower bound [Lai and Robbins, 1985] and the above-mentioned private lower bound. We also compare empirically the performance of our algorithm with the private UCB algorithm.
COUNTDOWN: A Run-Time Library for Performance-Neutral Energy Saving in MPI Applications

Power and energy consumption are becoming key challenges for the supercomputers’ exascale race. HPC systems’ processors waist active power during communication and synchronization among the MPI processes in large-scale HPC applications. However, due to the time scale at which communication happens, transitioning into low-power states while waiting for the completion of each communication may introduce unacceptable overhead. In this article, we present COUNTDOWN, a run-time library for identifying and automatically reducing the power consumption of the CPUs during communication and synchronization. COUNTDOWN saves energy without penalizing the time-to-completion by lowering CPUs power consumption only during idle times for which power state transition overhead is negligible. This is done transparently to the user, without requiring labor-intensive and error-prone application code modifications, nor requiring recompilation of the application. We test our methodology on a production Tier-1 system. For the NAS benchmarks, COUNTDOWN saves between 6 and 50 percent energy, with a time-to-solution penalty lower than 5 percent. In a complete production—Quantum ESPRESSO—for a 3.5K cores run, COUNTDOWN saves 22.36 percent energy, with a performance penalty below 3 percent. Energy saving increases to 37 percent with a performance penalty of 6.38 percent, if the application is executed without communication tuning.
Presupernova neutrinos in large dark matter direct detection experiments

The next Galactic core-collapse supernova (SN) is a highly anticipated observational target for neutrino telescopes. However, even prior to collapse, massive dying stars shine copiously in "pre-supernova" (pre-SN) neutrinos, which can potentially act as efficient SN warning alarms and provide novel information about the very last stages of stellar evolution. We explore the sensitivity to pre-SN neutrinos of large scale direct dark matter detection experiments, which, unlike dedicated neutrino telescopes, take full advantage of coherent neutrino-nucleus scattering. We find that argon-based detectors with target masses of $\mathcal{O}(100)$ tonnes (i.e. comparable in size to the proposed ARGO experiment) operating at sub-keV thresholds can detect $\mathcal{O}(10-100)$ pre-SN neutrinos coming from a source at a characteristic distance of $\sim$200 pc, such as Betelgeuse ($\alpha$ Orionis). Large-scale xenon-based experiments with similarly low thresholds could also be sensitive to pre-SN neutrinos. For a Betelgeuse-type source, large scale dark matter experiments could provide a SN warning siren $\sim$10 hours prior to the explosion. We also comment on the complementarity of large scale direct dark matter detection experiments and neutrino telescopes in the understanding of core-collapse SN.
Analysis of an Important Class of Non-Markov Systems

Probabilistic modeling of many types of systems generally assumes Markov behavior. However, some important practical systems exhibit memory. For example, in digital computer systems, the probability of occurrence of a transient failure is related to the time period the system has been operating correctly. Analytic methods do not yet exist that allow accurate modeling of such systems for the purpose of reliability analysis and fault-tolerant design. Methods are presented here to analyze an important class of non-Markov systems. In this class, the transition-probability-rate of an out-ward transition from a state is related to the duration the system has continuously been in that state. To analyze such systems, concept of memory profile has been introduced. Methods are first presented which enable computation of steady-state probabilities for both discrete-time and continuous-time processes with two states. These are then extended for general non-steady-state cases and also for systems with more than two states.
Constraining Lyman-alpha spatial offsets at 3 < z < 5.5 from VANDELS slit spectroscopy

We constrain the distribution of spatially offset Lyman-alpha emission (Ly α) relative to rest-frame ultraviolet emission in ∼300 high redshift (3 < z < 5.5) Lyman-break galaxies (LBGs) exhibiting Ly α emission from VANDELS, a VLT/VIMOS slit-spectroscopic survey of the CANDELS Ultra Deep Survey and Chandra Deep Field South fields (≃0.2 deg2 total). Because slit spectroscopy only provides one spatial dimension, we use Bayesian inference to recover the underlying two-dimensional Ly α spatial offset distribution. We model the distribution using a two-dimensional circular Gaussian, defined by a single parameter σr,Ly α, the standard deviation expressed in polar coordinates. Over the entire redshift range of our sample (3 < z < 5.5), we find $\sigma _{r,\mathrm{Ly}\,\alpha }=1.70^{+0.09}_{-0.08}$ kpc ($68\hbox{ per cent}$ conf.), corresponding to ∼0${^{\prime\prime}_{. }}$25 at 〈z〉 = 4.5. We also find that σr,Ly α decreases significantly with redshift. Because Ly α spatial offsets can cause slit losses, the decrease in σr,Ly α with redshift can partially explain the increase in the fraction of Ly α emitters observed in the literature over this same interval, although uncertainties are still too large to reach a strong conclusion. If σr,Ly α continues to decrease into the reionization epoch, then the decrease in Ly α transmission from galaxies observed during this epoch might require an even higher neutral hydrogen fraction than what is currently inferred. Conversely, if spatial offsets increase with the increasing opacity of the intergalactic medium, slit losses may explain some of the drop in Ly α transmission observed at z > 6. Spatially resolved observations of Ly α and UV continuum at 6 < z < 8 are needed to settle the issue.
Detecting Events of Daily Living Using Multimodal Data

Events are fundamental for understanding how people experience their lives. It is challenging, however, to automatically record all events in daily life. An understanding of multimedia signals allows recognizing events of daily living and getting their attributes as automatically as possible. In this paper, we consider the problem of recognizing a daily event by employing the commonly used multimedia data obtained from a smartphone and wearable device. We develop an unobtrusive approach to obtain latent semantic information from the data, and therefore an approach for daily event recognition based on semantic context enrichment. We represent the enrichment process through an event knowledge graph that semantically enriches a daily event from a low-level daily activity. To show a concrete example of this enrichment, we perform an experiment with eating activity, which may be one of the most complex events, by using 14 months of data for three users. In this process, to unobtrusively complement the lack of semantic information, we suggest a new food recognition/classification method that focuses only on a physical response to food consumption. Experimental results indicate that our approach is able to show automatic abstraction of life experience. These daily events can then be used to create a personal model that can capture how a person reacts to different stimuli under specific conditions.
Predictive Control for Chasing a Ground Vehicle using a UAV

We propose a high-level planner for a multirotor to chase a ground vehicle, while simultaneously respecting various state and input constraints. Assuming a minimal kinematic model for the ground vehicle, we use data collected online to generate predictions for our planner within a model predictive control framework. Our solution is demonstrated, both via simulations and experiments on a stable quadcopter platform.