Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired everyday executive functions and cognitive strategy use on the Weekly Calendar Planning Activity in individuals with stroke undergoing acute inpatient rehabilitation

Executive dysfunction is common in stroke patients undergoing acute inpatient rehabilitation. However, comprehensive neuropsychological evaluation of executive functions is often not feasible in this setting. Objective, standardized, and performance-based measures of cognitively-based instrumental activities of daily living (C-IADL) can capture everyday executive functions and inform rehabilitation planning and interventions. The goal of this study was to compare performance of individuals with stroke to healthy age-matched adults in performance on the 10-item Weekly Calendar Planning Activity (WCPA). 77 stroke inpatients undergoing rehabilitation and 77 healthy control participants completed the WCPA, a C-IADL measure involving scheduling appointments that requires planning, working memory, shifting, and inhibitory control. Compared to the control group, stroke patients had significantly worse accuracy, made more errors, used fewer cognitive strategies, followed fewer rules, took more time to complete the task, and were less efficient. 83% of stroke patients were less accurate than predicted by their age, and 64% used less strategies than their age prediction. Among 28 participants who screened as having 'normal' cognitive function on the Montreal Cognitive Assessment, the majority had deficits on the WCPA. Our results indicate that the WCPA is sensitive to executive dysfunction in stroke patients undergoing acute inpatient rehabilitation and underscores deficits in C-IADL accuracy, efficiency, and strategy use at this stage of stroke recovery. The WCPA may be a useful measure to ascertain executive dysfunction and to incorporate in cognitive rehabilitation.
Characterization of secondary organic aerosol from heated-cooking-oil emissions: evolution in composition and volatility

Abstract. Cooking emissions account for a major fraction of urban organic aerosol. It is therefore important to understand the atmospheric evolution in the physical and chemical properties of organic compounds emitted from cooking activities. In this work, we investigate the formation of secondary organic aerosol (SOA) from oxidation of gas-phase organic compounds from heated cooking oil. The chemical composition of cooking SOA is analyzed using thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS). While the particle-phase composition of SOA is a highly complex mixture, we adopt a new method to achieve molecular speciation of the SOA. All the GC-elutable material is classified by the constituent functional groups, allowing us to provide a molecular description of its chemical evolution upon oxidative aging. Our results demonstrate an increase in average oxidation state (from −0.6 to −0.24 ) and decrease in average carbon number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil, suggesting that fragmentation reactions are key processes in the oxidative aging of cooking emissions within 2 d equivalent of ambient oxidant exposure. Moreover, we estimate that aldehyde precursors from cooking emissions account for a majority of the SOA formation and oxidation products. Overall, our results provide insights into the atmospheric evolution of cooking SOA, a majority of which is derived from gas-phase oxidation of aldehydes.
A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naive Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used.
Characterizing the origins of dissolved organic carbon in coastal seawater using stable carbon isotope and light absorption characteristics

Abstract. In order to determine the origins of dissolved organic matter (DOM) occurring in the seawater of Sihwa Lake, we measured the stable carbon isotope ratios of dissolved organic carbon (DOC-δ13C) and the optical properties (absorbance and fluorescence) of DOM in two different seasons (March 2017 and September 2018). Sihwa Lake is enclosed by a dike along the western coast of South Korea, and the water is exchanged with the Yellow Sea twice a day through the sluice gates. The DOC concentrations were generally higher in lower-salinity waters in both periods, and excess of DOC was also observed in 2017 in high-salinity waters. Here, the excess DOC represents any DOC concentrations higher than those in the incoming open-ocean seawater. The excess DOC occurring in the lower-salinity waters originated mainly from marine sediments of tidal flats, based on the DOC-δ13C values (-20.7±1.2 ‰) and good correlations among the DOC, humic-like fluorescent DOM (FDOMH), and NH4+ concentrations. However, the origins of the excess DOC observed in 2017 appear to be from two different sources: one mainly from marine sources such as biological production based on the DOC-δ13C values (−19.1 ‰ to −20.5 ‰) and the other mainly from terrestrial sources by land–seawater interactions based on its depleted DOC-δ13C values (−21.5 ‰ to −27.8 ‰). This terrestrial DOM source observed in 2017 was likely associated with DOM on the reclaimed land, which experienced extended exposure to light and bacterial degradation as indicated by the higher spectral slope ratio (SR) of light absorbance and no concurrent increases in the FDOMH and NH4+ concentrations. Our study demonstrates that the combination of these biogeochemical tools can be a powerful tracer of DOM sources and characteristics in coastal environments.
Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate

The spread in global mean precipitation among climate models is explored in two ensembles using the complementary perspectives of surface evaporation and energy budgets. Models with higher global mean precipitation have stronger oceanic evaporation, driven by drier near‐surface air. The drier surface conditions occur alongside increases in near‐surface temperature and moisture at 925 hPa, which point to stronger boundary layer mixing. Correlations suggest that the degree of lower tropospheric mixing explains 18%–49% of the intermodel precipitation variance. To test this hypothesis, the degree of mixing is indirectly varied in a single‐model experiment by adjusting the relative humidity threshold that controls low‐cloud fraction. Indeed, increasing lower tropospheric mixing results in more global mean precipitation. Energetically, increased precipitation rates are associated with more downwelling longwave radiation to the surface and weaker sensible heat fluxes. These results highlight how lower‐tropospheric processes must be better constrained to reduce the precipitation discrepancy among climate models.
A 2-decade (1988–2009) record of diatom fluxes in the Mauritanian coastal upwelling: impact of low-frequency forcing and a two-step shift in the species composition

Abstract. Eastern Boundary Upwelling Ecosystems (EBUEs) are among the most productive marine regions in the world's oceans. Understanding the degree of interannual to decadal variability in the Mauritania upwelling system is key for the prediction of future changes of primary productivity and carbon sequestration in the Canary Current EBUE as well as in similar environments. A multiannual sediment trap experiment was conducted at the mooring site CBmeso (= 'Cape Blanc mesotrophic', ca. 20° N, ca. 20°40' W) in the high-productive Mauritanian coastal waters. Here, we present results on fluxes and the species-specific composition of the diatom assemblage for the time interval between March 1988 and May 2009. The temporal dynamics of diatom populations allows to propose three main diatom productivity/flux intervals: (i) early 1988–late 1996; (ii) 1997–1999, and (iii) early 2002–mid 2009. The impact of the Atlantic Multidecadal Oscillation appears to be an important forcing of the long-term dynamics of diatom population. The impact of cold (1988–1996) and warm AMO phases (2001–2009) is reflected by the outstanding shifts in species-specific composition. This AMO-impacted, long-term trend is interrupted by the occurrence of the strong 1997 ENSO. The extraordinary shift in the relative abundance of benthic diatoms in May 2002 suggests the strengthening of offshore advective transport within the uppermost layer of filament waters, and in the subsurface and in deeper and bottom‐near layers. It is hypothesized that the dominance of benthic diatoms was the response of the diatom community to the intensification of the slope and shelf poleward undercurrents. This dominance followed the intensification of the warm phase of AMO and the associated changes of the Atlantic Meridional Overturning Circulation. Transported valves (siliceous remains) from shallow coastal waters into the deeper bathypelagial should be considered for the calculation and model experiments of bathy- and pelagial nutrients budgets (especially Si), the burial of diatoms and the paleoenvironmental signal preserved in downcore sediments. Our 1988–2009 data set contributes to the distinction between climate-forced and intrinsic variability of populations of diatoms and will be especially helpful for establishing the scientific basis for forecasting and modelling future states of this ecosystem and its decadal changes.
The Rise of Dual-Class Stock Ipos

We create a novel dataset to examine the nature and determinants of dual-class IPOs. We document that dual-class firms have different types of controlling shareholders and wedges between voting and economic rights. We find that the founders' wedge is largest when founders have stronger bargaining power. The increase in founder wedge over time is due to increased willingness by venture capitalists to accommodate founder control and technological shocks that reduced firms' needs for external financing. Greater founder bargaining power is also associated with a lower likelihood of sunset provisions that eliminate dual-class structures within specified periods.
Arrhythmic Effects of Chronic Pentamidine in Human Cardiac Stem Cell Electrophysiology

It is evident that some clinical compounds with proarrhythmic effects exert these effects through a chronic reduction in hERG expression. Pentamidine has been found to have no acute effects on hERG current (<100 μM) but with 16-48 hours incubation reduces hERG function in a cell by disruption of hERG protein trafficking (1). The chronic effect of pentamidine has been investigated in HEK-293 overexpressing cell lines and isolated guinea pig ventricular myocytes, but not in human ventricular cardiac cells. We find that the hERG selective blocker E-4031 prolongs the action potential duration (APD) in adult human cardiac stem cells at concentrations ranging from 3-100 nM. E-4031 blocks the endogenous IKr (hERG current) in these human cells with an IC50 of 17 nM. We find acute application (4 min) of pentamidine at concentrations up to 30 μM had no effect on human action potentials whereas chronic incubation (24 hr) of pentamidine significantly increases human APDs at concentrations of 1μM, 3μM, and 10μM (see below). Substantial proarrhythmic activity in the form of early afterdepolarizations (EADs) was seen at both the 3 μM and 10 μM dose. The peak serum concentration of pentamidine seen in the clinic for the treatment of Pneumocystis pneumonia range from 1-5 μM suggesting a connection between the EADs seen here to the long Q-T and TdP tachycardia seen in patients (2,3,4). Chronic pentamidine APD60/APD90 data: control = 502 ± 138/616 ± 162ms,1 μM pentamidine = 802 ± 87/988 ± 86ms, 3 μM pentamidine = 3367 ± 586/3747 ± 466ms,10 μM pentamidine = 3205/3678ms.
Human Sentence Processing: Recurrence or Attention?

Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.
Type-X two-Higgs-doublet model in light of the muon 
g−2
: Confronting Higgs boson and collider data

The recent Fermilab measurement of the muon anomalous magnetic moment yields $4.2 \sigma$ deviations from the SM prediction when combined with the BNL E821 experiment results. In the Type-X two Higgs doublet model, we study the consequence of imposing the observed muon $g-2$, along with the constraints from theoretical stabilities, electroweak oblique parameters, Higgs precision data, and direct searches. For a comprehensive study, we scan the whole parameter space in two scenarios, the normal scenario where $h_{\rm SM} = h$ and the inverted scenario where $h_{\rm SM}=H$, where $h$ ($H$) is the light (heavy) CP-even Higgs boson. We found that large $\tan\beta$ (above 100) and light pseudoscalar mass $M_A$ are required to explain the muon $g-2$ anomaly. This breaks the theoretical stability unless the scalar masses satisfy $M_A^2 \simeq M_{H^\pm}^2 \simeq m_{12}^2 \tan\beta \approx M_{H/h}^2$. The direct search bounds at the LEP and LHC exclude the light $A$ window with $M_A \lesssim 62.5~$GeV. We also show that the observed electron anomalous magnetic moment is consistent with the model prediction, but the lepton flavor universality data in the $\tau$ and $Z$ decays are not. For a separate exploration of the model, we propose the golden mode $pp \to A h/AH \to 4 \tau$ at the HL-LHC.
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering

We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating function to partition an unlabeled dataset into subsets according to the latent semantics and multiple experts to discriminate distinct subsets of instances assigned to them in a contrastive learning manner. To solve the nontrivial inference and learning problems caused by the latent variables, we further develop a scalable variant of the Expectation-Maximization (EM) algorithm for MiCE and provide proof of the convergence. Empirically, we evaluate the clustering performance of MiCE on four widely adopted natural image datasets. MiCE achieves significantly better results than various previous methods and a strong contrastive learning baseline.
DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge

The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of deep learning and deep neural networks, proliferate various computer vision driven mobile and IoT applications deployed on the edge. This paper focuses on applications which make soft real time requests to perform inference on their data - they desire prompt responses within designated deadlines, but occasional deadline misses are acceptable. Supporting soft real time applications on a multi-tenant edge server is not easy, since the requests sharing the limited GPU computing resources of an edge server interfere with each other. In order to tackle this problem, we comprehensively evaluate how latency and throughput respond to different GPU execution plans. Based on this analysis, we propose a GPU scheduler, DeepRT, which provides latency guarantee to the requests while maintaining high overall system throughput. The key component of DeepRT, DisBatcher, batches data from different requests as much as possible while it is proven to provide latency guarantee for requests admitted by an Admission Control Module. DeepRT also includes an Adaptation Module which tackles overruns. Our evaluation results show that DeepRT outperforms state-of-the-art works in terms of the number of deadline misses and throughput.
Software Engineering for Blockchain Based Software Systems: Foundations, Survey, and Future Directions

Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from a software engineering perspective. We characterize BBS engineering from the theoretical foundations, processes, models, and roles and discuss a rich repertoire of key development activities, principles, challenges, and techniques. The focus and depth of this survey not only gives software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpins a starting point for further research in this field.
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks

Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequently, there is a growing need for an extensible simulation framework that can perform architectural explorations with SNNs, including both platform-based design of today’s hardware, and hardware-software co-design and design-technology co-optimization of the future. We present NeuroXplorer, a fast and extensible framework that is based on a generalized template for modeling a neuromorphic architecture that can be infused with the specific details of a given hardware and/or technology. NeuroXplorer can perform both low-level cycle-accurate architectural simulations and high-level analysis with data-flow abstractions. NeuroXplorer’s optimization engine can incorporate hardware-oriented metrics such as energy, throughput, and latency, as well as SNN-oriented metrics such as inter-spike interval distortion and spike disorder, which directly impact SNN performance. We demonstrate the architectural exploration capabilities of NeuroXplorer through case studies with many state-of-the-art machine learning models.
Simulation and estimation of a point-process market-model with a matching engine

The extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a financial market and the importance of this with regards to the statistical robustness of models and resulting inference remains an unanswered concern for market models. Here we consider a 10-variate Hawkes process with simple rules to simulate common order types which are submitted to a matching engine. Hawkes processes can be used to model the time and order of events, and how these events relate to each other. However, they provide a freedom with regards to implementation mechanics relating to the prices and volumes of injected orders. This allows us to consider a reference Hawkes model and two additional models which have rules that change the behaviour of limit orders. The resulting trade and quote data from the simulations are then calibrated and compared with the original order generating process to determine the extent with which implementation rules can distort model parameters. Evidence from validation and hypothesis tests suggest that the true model specification can be significantly distorted by market mechanics to the extent that the models are no longer statistically equivalent. It can be argued that the matching mechanism merely blurs the estimation and identification of the process and this is a triviality. However, we argue that this can lead to fundamental changes in the nature of decisions when using these types of models.
Convergence of first‐order finite volume method based on exact Riemann solver for the complete compressible Euler equations

Recently developed concept of dissipative measure‐valued solution for compressible flows is a suitable tool to describe oscillations and singularities possibly developed in solutions of multidimensional Euler equations. In this paper we study the convergence of the first‐order finite volume method based on the exact Riemann solver for the complete compressible Euler equations. Specifically, we derive entropy inequality and prove the consistency of numerical method. Passing to the limit, we show the weak and strong convergence of numerical solutions and identify their limit. The numerical results presented for the spiral and the Richtmyer‐Meshkov problem are consistent with our theoretical analysis.
Two-layer neural networks with values in a Banach space

We study two-layer neural networks whose domain and range are Banach spaces with separable preduals. In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space. As the nonlinearity we choose the lattice operation of taking the positive part; in case of R d -valued neural networks this corresponds to the ReLU activation function. We prove inverse and direct approximation theorems with Monte-Carlo rates for a certain class of functions, extending existing results for the ﬁnite-dimensional case. In the second part of the paper, we study, from the regularisation theory viewpoint, the problem of ﬁnding optimal representations of such functions via signed measures on a latent space from a ﬁnite number of noisy observations. We discuss regularity conditions known as source conditions and obtain convergence rates in a Bregman distance for the representing measure in the regime when both the noise level goes to zero and the number of samples goes to inﬁnity at appropriate rates.