Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistent Deficits in Self-Regulation as a Mediator between Childhood Attention-Deficit/Hyperactivity Disorder Symptoms and Substance Use Disorders

Abstract Background: The link between attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUDs) has been largely demonstrated. Some scholars have hypothesized that self-regulation mechanisms might play a key role in explaining this association. Objective(s): The current study tested the hypothesis that retrospective childhood ADHD symptoms might lead to more severe SUDs and this association should be mediated by current self-ratings of behavioral disinhibition, inattention, and emotional dysregulation among 204 treatment-seeking adults (male: 67.3%; female: 32.7%) with a primary diagnosis of alcohol use disorder and other SUDs. Methods: The mediational model was estimated through self-report measures of childhood ADHD symptoms (independent variable; WURS), current self-regulation mechanisms (mediators)—behavioral disinhibition (BIS-11 motor subscale), difficulties with attention regulation (MAAS) and emotion regulation (DERS)—and severity of SUDs (dependent variable; SPQ alcohol, illicit and prescribed drugs). Results: The analysis showed that alterations in the self-regulation system fully mediated the association between the severity of childhood ADHD symptoms and SUDs in adulthood. Behavioral disinhibition and difficulties in attention regulation were the most representative alterations in self-regulation processes that explained this association. Conclusions: These findings suggest it is useful to implement several therapeutic approaches (e.g. behavioral, mindfulness-based, and pharmacological) to increase the self-regulation abilities of children and adolescents with ADHD in order to reduce the probability of SUD onset in adulthood. However, future longitudinal neuroimaging and neuropsychological studies are needed to further support the role of self-regulation mechanisms in explaining the prospective association between childhood ADHD symptoms and SUDs in adulthood.
How to make better forensic decisions

Much of forensic practice today involves human decisions about the origins of patterned sensory evidence, such as tool marks and fingerprints discovered at a crime scene. These decisions are made by trained observers who compare the evidential pattern to an exemplar pattern produced by the suspected source of the evidence. The decision consists of a determination as to whether the two patterns are similar enough to have come from the same source. Although forensic pattern comparison disciplines have for decades played a valued role in criminal investigation and prosecution, the extremely high personal and societal costs of failure—the conviction of innocent people—has elicited calls for caution and for the development of better practices. These calls have been heard by the scientific community involved in the study of human information processing, which has begun to offer much-needed perspectives on sensory measurement, discrimination, and classification in a forensic context. Here I draw from a well-established theoretical and empirical approach in sensory science to illustrate the vulnerabilities of contemporary pattern comparison disciplines and to suggest specific strategies for improvement.
Long-slit échelle spectroscopy of galactic outflows: The case ofNGC 4449

At high redshift the ubiquity of outflows and winds in strongly star-forming galaxies has been demonstrated using rest frame UV absorption lines. In the cases with optical emission lines, the studies mostly had to rely on low and intermediate dispersion spectra. This implies that for detailed studies of galactic wind physics we have to use local objects. In particular, dwarf galaxies are well suited to extrapolation to high redshift protogalaxies. Several kinematic studies of strongly starforming dwarf galaxies using Fabry-Perot and IFU spectrographs exist. Unfortunately, similar as for high redshift galaxies the employed spectral resolution is often significantly higher that the thermal line width. As a result faint high velocity features and details of the turbulent motion are hidden or unresolved. Here we will present an analysis of the ionized gas kinematics of the prototypical star-forming irregular galaxy NGC 4449 using long-slit, high-dispersion echelle spectra. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
SATViz: Real-Time Visualization of Clausal Proofs

Visual layouts of graphs representing SAT instances can highlight the community structure of SAT instances. The community structure of SAT instances has been associated with both instance hardness and known clause quality heuristics. Our tool SATViz visualizes CNF formulas using the variable interaction graph and a force-directed layout algorithm. With SATViz, clause proofs can be animated to continuously highlight variables that occur in a moving window of recently learned clauses. If needed, SATViz can also create new layouts of the variable interaction graph with the adjusted edge weights. In this paper, we describe the structure and feature set of SATViz. We also present some interesting visualizations created with SATViz.
Spacecraft Attitude Dynamics

Introduction Rotational Kinematics Attitude Motion Equations Attitude Dynamics of a Rigid Body Effect of Internal Energy Dissipation on the Directional Stability of Spinning Bodies Directional Stability of Multispin Vehicles Effect of Internal Energy Dissipation on the Directional Stability of Gyrostats Spacecraft Torques Gravitational Stabilization Spin Stabilization in Orbit Dual-Stabilization in Orbit: Gyrostats and Bias Momentum Satellites Appendixes References Index.
Integration of 3-Level MoS Multibridge Channel FET With 2D Layered Contact and Gate Dielectric

Multi-bridge channel field effect transistor (MBCFET) provides several advantages over FinFET technology and is an attractive solution for sub-5 nm technology nodes. MBCFET is a natural choice for devices that use semiconducting layered materials (such as, MoS2) as the channel due to their dangling-bond-free ultra-thin nature and the possibility of layer-by-layer transfer. MoS2-based MBCFET is thus an attractive proposition for drive current boost without compromising on the electrostatics and footprint. Here we demonstrate a 3-level MoS2 MBCFET, where each vertically stacked channel is dual-gated to achieve a saturation current of <inline-formula> <tex-math notation="LaTeX">$174.9~ \boldsymbol \mu \text{A}$ </tex-math></inline-formula> (which translates to <inline-formula> <tex-math notation="LaTeX">$90~ \boldsymbol \mu \text{A}$ </tex-math></inline-formula> per <inline-formula> <tex-math notation="LaTeX">$\boldsymbol \mu \text{m}$ </tex-math></inline-formula> footprint width (@<inline-formula> <tex-math notation="LaTeX">$2.7~ \boldsymbol \mu \text{m}$ </tex-math></inline-formula> channel length), a near-ideal sub-threshold slope of 63 mV/dec, and an on-off ratio lpzrptGreater108. This work sets the benchmark for layer-material-based MBCFET in terms of the number of parallel channels integrated, simultaneously providing high drive current and excellent electrostatic control.
Understanding Time Variations of DNN Inference in Autonomous Driving

—Deep neural networks (DNNs) are widely used in autonomous driving due to their high accuracy for perception, decision, and control. In safety-critical systems like autonomous driving, executing tasks like sensing and perception in real-time is vital to the vehicle’s safety, which requires the application’s execution time to be predictable. However, non-negligible time variations are observed in DNN inference. Current DNN infer- ence studies either ignore the time variation issue or rely on the scheduler to handle it. None of the current work explains the root causes of DNN inference time variations. Understanding the time variations of the DNN inference becomes a fundamental challenge in real-time scheduling for autonomous driving. In this work, we analyze the time variation in DNN inference in ﬁne granularity from six perspectives: data, I/O, model, runtime, hardware, and end-to-end perception system. Six insights are derived in understanding the time variations for DNN inference.
Statistical analysis of Al iii and Ciii] emission lines as virial black hole mass estimators in quasars

We test the usefulness of the intermediate ionisation lines AlIII 1860 and CIII] 1909 as reliable virial mass estimators for quasars. We identify a sample of 309 quasars from the SDSS DR16 in the redshift range 1.2<z<1.4 to have [OII] 3728 recorded on the same spectrum of AlIII 1860, SiIII 1890, and CIII] 1909. We set the systemic quasar redshift using careful measurements of [OII]. We then classified the sources as Population A, extreme Population A (xA) and Population B, and analysed the 1900\AA\ blend using multi-component models to look for systematic line shifts of the AlIII and CIII] along the quasar main sequence. We do not find significant shifts of the AlIII line peak in Pop. B and the wide majority of Pop. A. For Pop. xA, a small median blueshift of -250 km/s was observed, motivating a decomposition of the AlIII line profile into a virialized component centred at rest-frame and a blueshifted component for an outflow emission. For Pop. B objects, we proved the empirical necessity to fit a redshifted very broad component (VBC), clearly seen in CIII], and analysed the physical implications on a Pop. B composite spectrum using CLOUDY simulations. We find consistent black hole mass estimations using AlIII and CIII] as virial estimators for the bulk of Population A. AlIII (and even CIII]) is a reliable virial black hole mass estimator for Pop. A and B objects. xA sources deserve special attention due to the significant blueshifted excess observed in the line profile of AlIII, although not as large as those observed in CIV 1549.
A Guide to Employ Hyperspectral Imaging for Assessing Wheat Quality at Different Stages of Supply Chain in Australia: A Review

Wheat is one of the major staple crops across the globe. Therefore, it is mandatory to measure, maintain, and improve the wheat quality for human consumption. Traditional wheat quality measurement methods are mostly invasive, destructive, and limited to small samples of wheat. In a typical supply chain of wheat, there are many receival points where bulk wheat arrives, gets stored, and then gets forwarded as per the requirements. In these receival points, the application of traditional quality measurement methods is difficult and often very expensive. Therefore, there is a need for non-invasive, non-destructive real-time methods for wheat quality assessments. One such method that fulfils the abovementioned criteria is hyperspectral imaging (HSI) for food quality measurement and it can also be applied to bulk samples. In this article, we have investigated how HSI has been used in literature for assessing stored wheat quality. So that the required information to implement real-time quality assessment methods at the different stages of the Australian supply chain can be made available in a single and compact document.
Adaptive inference over Besov spaces in the white noise model using $p$-exponential priors

In many scientific applications the aim is to infer a function which is smooth in some areas, but rough or even discontinuous in other areas of its domain. Such spatially inhomogeneous functions can be modelled in Besov spaces with suitable integrability parameters. In this work we study adaptive Bayesian inference over Besov spaces, in the white noise model from the point of view of rates of contraction, using $p$-exponential priors, which range between Laplace and Gaussian and possess regularity and scaling hyper-parameters. To achieve adaptation, we employ empirical and hierarchical Bayes approaches for tuning these hyper-parameters. Our results show that, while it is known that Gaussian priors can attain the minimax rate only in Besov spaces of spatially homogeneous functions, Laplace priors attain the minimax or nearly the minimax rate in both Besov spaces of spatially homogeneous functions and Besov spaces permitting spatial inhomogeneities.
An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Convolutional Neural Networks

Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating adversarial instances are costly, often using gradient estimation or training a replacement network. This paper introduces Qu ery-Efficient Evolutionary Attack—QuEry Attack—an untargeted, score-based, black-box attack. QuEry Attack is based on a novel objective function that can be used in gradient-free optimization problems. The attack only requires access to the output logits of the classifier and is thus not affected by gradient masking. No additional information is needed, rendering our method more suitable to real-life situations. We test its performance with three different, commonly used, pretrained image-classifications models—Inception-v3, ResNet-50, and VGG-16-BN—against three benchmark datasets: MNIST, CIFAR10 and ImageNet. Furthermore, we evaluate QuEry Attack’s performance on non-differential transformation defenses and robust models. Our results demonstrate the superior performance of QuEry Attack, both in terms of accuracy score and query efficiency.
Narrowband Transmit Beam Pattern in medical ultrasound: a stochastic approach to delays optimization

Transmit Beam Pattern (TBP) optimization is an important task in medical ultrasound especially in some advanced applications like continuous wave Doppler or shear wave generation in acoustic radiation force impulse elastography. Standard TBP is based on transmission focused at a ﬁxed focal depth: this results in well-known drawbacks like non-uniform beam width over depth, presence of signiﬁcant side lobes and quick energy drop out after the focal depth. To overcome these limitations, in this work we present a novel optimization approach for TBP by focusing the analysis on the narrow band approximation of the TBP and considering transmit delays as free variables instead of linked to a speciﬁc focal depth. We formulate the problem as a non linear Least Squares problem to minimize the diﬀerence between the TBP corresponding to a set of delays and the desired one, modeled as a 2D rectangular shape elongated in the direction of the beam axis. The narrow band case leads naturally to reformulate the problem in the frequency domain, with a signiﬁcant computational saving with respect to time domain. The optimized narrowband beam patterns have been compared with a large set of standard ones, showing an overall improvement of desired features, thus demonstrating the eﬀectiveness of the proposed approach. Moreover, in order to allow a quantitative evaluation of the improvement, a novel set of metrics is introduced.
Understanding variability in HASDM to support space traffic management

With more commercial constellations planned, the number of Low Earth Orbit (LEO) objects is set to TRIPLE in two years. The growth in LEO objects directly increases the probability of unintentional collisions between objects due to accumulating space debris. Effective space traffic management needs accurate knowledge of the variability in upper atmosphere densities. Data assimilative modeling, where physics-based models are informed by measurements, supplies the best capability today for specifying and predicting space weather. The foundation for this modeling comes from the SET High Accuracy Satellite Drag Model (HASDM) density database. We report on studies to understand the variabilities in HASDM. We identify two thermospheric features from the SET HASDM density database. First, we have confirmed that the time scale is very rapid (1-hour) for molecular conduction above 200 km to transfer energy vertically in the thermosphere. This results couples with a longer timescale for conduction in the 100-200 km region where it takes up to 2 days for energy to transition across that region via molecular conduction. We now have an excellent picture of the timescales of energy change throughout the thermosphere. Second, the SET HASDM density data display a common range of variability despite the level of daily averaged geomagnetic activity as represented by Ap. During higher levels of daily averaged geomagnetic activity, the density mean and median values increase at all altitude levels. However, the relative range of variability is consistent from one daily average of Ap to the next. The reason is likely to be that the underlying pre-storm density of the thermosphere is determined by the solar EUV and FUV irradiances that create a thermal foundation of the upper atmosphere. The daily averaged geomagnetic activity is a perturbation upon that foundation.
Multimer analysis method reveals higher-order topology in quasicrystals

: Higher-order topological insulator (HOTI) has been extended from crystals to quasicrystals. However, due to the difference in spatial symmetry, the realization mechanism and analysis method of HOTI in crystals and quasicrystals are not universal. In this Letter, the multimer analysis method (MAM) is strictly defined and improved, and the higher-order topology (HOT) in quasicrystals is revealed by MAM and its universality in crystals is verified. The Stampfli-type higher-order topological quasicrystalline insulator (HOTQI) protected by long-range C 12 symmetry dominated by C 6 symmetry is constructed. Based on MAM, the topological phase transition of HOTQI is investigated, and a real-space topological index is proposed. In addition, photonic and phononic analogs of HOTQI are constructed based on MAM. This work provides a universal method for revealing the HOT in various systems (crystals and quasicrystals, electronics, photonics and phononics).
Mandator and Sporades: Robust Wide-Area Consensus with Efficient Request Dissemination

Consensus algorithms are deployed in the wide area to achieve high availability for geographically replicated applications. Wide-area consensus is challenging due to two main reasons: (1) low throughput due to the high latency overhead of client request dissemination and (2) network asynchrony that causes consensus protocols to lose liveness. In this pa-per, we propose Mandator and Sporades , a modular state machine replication algorithm that enables high performance and resiliency in the wide-area setting. To address the high client request dissemination overhead challenge, we propose Mandator , a novel consensus-agnostic asynchronous dissemination layer. Mandator sepa-rates client request dissemination from the critical path of consensus to obtain high performance. Composing Mandator with Multi-Paxos (Mandator-Paxos) delivers signiﬁcantly high throughput under synchronous networks. However, under asynchronous network conditions, Mandator-Paxos loses liveness which results in high latency. To achieve low latency and robustness under asynchrony, we propose Sporades , a novel omission fault-tolerant consensus algorithm. Sporades consists of two modes of operations – synchronous and asynchronous – that always ensure liveness. Sporades leverages the synchronous network to minimize the communication cost to O ( n ) and matches the lower bound of O ( n 2 ) at worse-case executions. The combination of Mandator and Sporades (Mandator-Sporades) provides a robust and high-performing state machine replication system. We implement and evaluate Mandator-Sporades in a wide-area deployment running on Amazon EC2. Our evaluation shows that in the synchronous execution, Mandator-Sporades achieves 300k tx/sec throughput in less than 900ms latency, outperforming Multi-Paxos, EPaxos and Rabia by 650% in throughput, at a modest expense of latency. Furthermore, we show that Mandator-Sporades outperforms Mandator-Paxos, Multi-Paxos, and EPaxos in the face of targeted distributed denial-of-service attacks.
Proving a directed analogue of the Gyárfás-Sumner conjecture for orientations of P4

An oriented graph is a digraph that does not contain a directed cycle of length two. An (oriented) graph $D$ is \emph{$H$-free} if $D$ does not contain $H$ as an induced sub(di)graph. The Gyárfás-Sumner conjecture is a widely-open conjecture on simple graphs, which states that for any forest $F$, there is some function $f$ such that every $F$-free graph $G$ with clique number $\omega(G)$ has chromatic number at most $f(\omega(G))$. Aboulker, Charbit, and Naserasr [Extension of Gyárfás-Sumner Conjecture to Digraphs; E-JC 2021] proposed an analogue of this conjecture to the dichromatic number of oriented graphs. The \emph{dichromatic number} of a digraph $D$ is the minimum number of colors required to color the vertex set of $D$ so that no directed cycle in $D$ is monochromatic. Aboulker, Charbit, and Naserasr‘s $\dichi$-boundedness conjecture states that for every oriented forest $F$, there is some function $f$ such that every $F$-free oriented graph $D$ has dichromatic number at most $f(\omega(D))$, where $\omega(D)$ is the size of a maximum clique in the graph underlying $D$. In this paper, we perform the first step towards proving Aboulker, Charbit, and Naserasr‘s $\dichi$-boundedness conjecture by showing that it holds when $F$ is any orientation of a path on four vertices.
Secure and Fault Tolerant Decentralized Learning

—Federated learning (FL) has emerged as a promising paradigm for training a global model over the data distributed across multiple data owners without centralizing clients’ raw data. However, sharing of its local model update with the FL server during training can also reveal information of a client’s local dataset. As a result, trusted execution environments (TEEs) within the FL server have been recently deployed in production lines of companies like Meta for secure aggregation of local updates. However, secure aggregation can suffer from performance degradation due to error-prone local updates sent by clients that become faulty during training due to underlying software and hardware failures. Moreover, data heterogeneity across clients makes fault mitigation quite challenging, as even the updates from the normal clients are quite dissimilar. Thus, most of the prior fault tolerant methods, which treat any local update differing from the majority of other updates as faulty, perform poorly. We propose DiverseFL that makes model aggregation secure as well as robust to faults when data is heterogeneous. In DiverseFL, any client whose local model update diverges from its associated guiding update is tagged as being faulty. To implement our novel per-client criteria for fault mitigation, DiverseFL creates a TEE-based secure enclave within the FL server, which in addition to performing secure aggregation for carrying out the global model update step, securely receives a small representative sample of local data from each client only once before training, and computes guiding updates for each participating client during training. Thus, DiverseFL provides security against privacy leakage as well as robustness against faulty clients. In experiments, DiverseFL consistently achieves signiﬁcant improvements (up to ∼ 39% ) in absolute test accuracy over prior fault mitigation benchmarks. DiverseFL also performs closely to OracleSGD , where server combines updates only from the normal clients. We also analyze the convergence rate of Di- verseFL under non-IID data and standard convexity assumptions.