Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of trauma exposure on explicit and implicit memory

ABSTRACT Objectives: The present study aimed to determine whether explicit and implicit memory systems are modulated by the type of content (neutral, emotional trauma-related and generally-emotional) in sexual abuse victims who did not develop PTSD, compared to non-exposed controls. Design: A mixed-factorial design with Content (neutral, trauma-related, generally-emotional) as a within-subject variable and Group (victims, controls) as a between-subject variable was used in two experiments. Methods: In both experiments, participants were required to learn three stories presented orally: a neutral, an emotional trauma-related (sexual abuse) and a generally-emotional story. In Experiment 1, participants’ memory was tested with two explicit tasks (free recall and Remember/Know/Guess) and one implicit task (word-fragment completion task). In Experiment 2, a modified version of the word-fragment completion task was presented, followed by an awareness questionnaire to ensure the implicit character of the test. Results: Victims showed lower performances with neutral contents, relative to controls, in explicit and implicit tasks. However, this difference was not observed with trauma-related contents suggesting this information is preferentially processed by trauma-exposed participants (with increased attentional resources). Conclusions: Our results show that trauma exposure may itself be associated with implicit and explicit memory alterations, even for individuals who did not develop PTSD.
SEGMENT COORDINATION RESPONSE TO ALTERATIONS IN FOOTFALL PATTERN

The anatomy of the subtalar joint creates a mechanical link between foot, leg, and knee rotations. A disruption in the coordination of these rotations may predispose a runner to overuse injuries. The purpose of this study was to investigate the change in foot-leg and leg-knee coordination patterns when natural rearfoot and forefoot runners alter their footfall pattern. Foot eversion/inversion, leg internal/external rotation and knee flexion/extension were measured in a group of young, healthy runners defined as either natural rearfoot runners or natural forefoot runners. Both groups ran on a treadmill with a rearfoot running (RFR) and a forefoot running (FFR) pattern for five minutes in each condition. Mean and peak foot inversion/eversion, leg internal/external rotation and knee flexion angles were calculated for each third of the stance phase. A modified vector coding approach assessed the coordination pattern between foot inversion/eversion and leg internal/external rotation (foot-leg) and between leg internal/external rotation and knee flexion (leg-knee). FFR resulted in greater mean foot angles, foot excursion peak inversion in each third of stance as well as greater mean leg angle, peak external rotation and leg range of motion during early stance. RFR resulted in greater peak eversion in each third of stance and greater mean knee angle, peak knee flexion and knee range of motion in early and mid stance. Foot-leg coordination only differed in mean phase angle during mid stance. Leg-knee coordination was different in the frequency of coordination patterns during early and late stance. Despite differences in foot, leg and knee kinematics, the shape of the foot-leg coordination pattern was similar between RFR and FFR but coordination between leg-knee rotations was altered. Neither footfall pattern displayed kinematic nor coordination pattern that suggest one footfall pattern is more optimal at preventing overuse injuries than the other.
Predicting psychosis risk using a specific measure of cognitive control: a 12-month longitudinal study

Abstract Background Identifying risk factors of individuals in a clinical-high-risk state for psychosis are vital to prevention and early intervention efforts. Among prodromal abnormalities, cognitive functioning has shown intermediate levels of impairment in CHR relative to first-episode psychosis and healthy controls, highlighting a potential role as a risk factor for transition to psychosis and other negative clinical outcomes. The current study used the AX-CPT, a brief 15-min computerized task, to determine whether cognitive control impairments in CHR at baseline could predict clinical status at 12-month follow-up. Methods Baseline AX-CPT data were obtained from 117 CHR individuals participating in two studies, the Early Detection, Intervention, and Prevention of Psychosis Program (EDIPPP) and the Understanding Early Psychosis Programs (EP) and used to predict clinical status at 12-month follow-up. At 12 months, 19 individuals converted to a first episode of psychosis (CHR-C), 52 remitted (CHR-R), and 46 had persistent sub-threshold symptoms (CHR-P). Binary logistic regression and multinomial logistic regression were used to test prediction models. Results Baseline AX-CPT performance (d-prime context) was less impaired in CHR-R compared to CHR-P and CHR-C patient groups. AX-CPT predictive validity was robust (0.723) for discriminating converters v. non-converters, and even greater (0.771) when predicting CHR three subgroups. Conclusions These longitudinal outcome data indicate that cognitive control deficits as measured by AX-CPT d-prime context are a strong predictor of clinical outcome in CHR individuals. The AX-CPT is brief, easily implemented and cost-effective measure that may be valuable for large-scale prediction efforts.
The Psychosis-Risk Syndrome: Handbook for Diagnosis and Follow-Up

PART A: Psychosis-Risk Syndromes for First Psychosis: Background 1 1. Psychosis-Risk Syndromes for First Psychosis: A History of the Concept 3 2. Development of the Structured Interview for Psychosis-Risk Syndromes (SIPS) 10 3. Reliability and Validity of the SIPS 17 4. Symptom Classes and Factors in the SIPS 21 5. Psychosis-Risk Syndromes and Psychosis in the SIPS 24 6. The "Other" Symptoms of the Risk Syndromes: Negative, Disorganization, and General 33 7. Characteristics of SIPS Psychosis-Risk Samples 36 PART B: Psychosis-Risk Syndromes: SIPS and SOPS Evaluation 45 8. Pathways to the Risk Syndrome Clinic 47 9. Initial Interview: The SIPS and SOPS Evaluation 49 10. Initial Evaluation: Informing Patients and Families of Risk Status and Options 59 11. Rating Positive and Other Psychosis-Risk Symptoms with the SOPS 63 12. Rating Actual Cases, Baseline Assessment 78 13. Differential Diagnosis of the Psychosis-Risk Syndrome 109 14. Psychosis-Risk Patients over Time 120 15. Rating Baseline Cases for Practice 139 PART C: The PRIME Clinic: Psychosis-Risk Patients Face-to-Face 161 Bibliography 169 Appendixes A. Risk Syndrome Phone Screen 174 B. SIPS/SOPS 5.0 179 C. Informed Consent 237
'eLectrifying' ECE practicum: A pilot study on implementing ePortfolios for use in early childhood initial teacher education practicum assessment at Wintec

A pilot project was conducted to implement the use of the Mahara ePortfolio system for practicum assessment and learning activities. Following the reconceptualisation and adaptation of the current assessment activities to the online system, data was collected from those involved. Students and lecturers were interviewed, students were given a questionnaire and formal and informal anecdotal evidence from students, lecturers and mentors was included. The data was analysed in terms of five key research questions: 1. What are students’ perceptions of the ePortfolio system? 2. Does the use of Mahara strengthen the triadic relationship between student, mentor and visiting lecturer? 3. Do the ePortfolios make the assessment tasks authentic and relevant to students, industry and practicum experiences? 4. How are students using the technology? 5. How are lecturers using the supporting technology? Findings indicate that students perceived the ePortfolio system itself in a mostly positive light, enabling flexibility and use of multimedia, but that lack of training hindered their confidence levels and made the assessment tasks ‘daunting’ and ‘time consuming’. Despite the issues surrounding training, students were able to explore, experiment and problem solve with different functions and tools, showing that the system is manageable for most students. While improving student/lecturer/mentor relationships was a key reason for making the change to the online system, the pilot showed the system does have potential to do this but that more training for all three parties needs to happen in order for them to take full advantage of this feature. Mentors in particular struggled to use the system, which was due to a lack of training and possibly enthusiasm on the mentors’ behalf. The adapted assessment tasks were seen by students as relevant to their practice and enabled them to use their own work for evidence and reflection. The technology that lecturers used to support their interaction with the ePortfolio system were seen by the majority as inadequate and more consideration needs to be given to which ones to purchase for the next iteration of the project. Recommendations for the next iteration of the project include more detailed training and more of a focus on including mentors.
WIQA: A dataset for “What if...” reasoning over procedural text

We introduce WIQA, the first large-scale dataset of “What if. ” questions over procedural text. WIQA contains a collection of paragraphs, each annotated with multiple influence graphs describing how one change affects another, and a large (40k) collection of “What if. ” multiple-choice questions derived from these. For example, given a paragraph about beach erosion, would stormy weather hasten or decelerate erosion? WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no effect) perturbations. We find that state-of-the-art models achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.
Characterising the surface magnetic fields of T Tauri stars with high-resolution near-infrared spectroscopy

Aims. In this paper, we aim to characterise the surface magnetic fields of a sample of eight T Tauri stars from high-resolution near-infrared spectroscopy. Some stars in our sample are known to be magnetic from previous spectroscopic or spectropolarimetric studies. Our goals are firstly to apply Zeeman broadening modelling to T Tauri stars with high-resolution data, secondly to expand the sample of stars with measured surface magnetic field strengths, thirdly to investigate possible rotational or long-term magnetic variability by comparing spectral time series of given targets, and fourthly to compare the magnetic field modulus ⟨B⟩ tracing small-scale magnetic fields to those of large-scale magnetic fields derived by Stokes V Zeeman Doppler Imaging (ZDI) studies. Methods. We modelled the Zeeman broadening of magnetically sensitive spectral lines in the near-infrared K-band from high-resolution spectra by using magnetic spectrum synthesis based on realistic model atmospheres and by using different descriptions of the surface magnetic field. We developped a Bayesian framework that selects the complexity of the magnetic field prescription based on the information contained in the data. Results. We obtain individual magnetic field measurements for each star in our sample using four different models. We find that the Bayesian Model 4 performs best in the range of magnetic fields measured on the sample (from 1.5 kG to 4.4 kG). We do not detect a strong rotational variation of ⟨B⟩ with a mean peak-to-peak variation of 0.3 kG. Our confidence intervals are of the same order of magnitude, which suggests that the Zeeman broadening is produced by a small-scale magnetic field homogeneously distributed over stellar surfaces. A comparison of our results with mean large-scale magnetic field measurements from Stokes V ZDI show different fractions of mean field strength being recovered, from 25–42% for relatively simple poloidal axisymmetric field topologies to 2–11% for more complex fields.
CEREBRuM: a Convolutional Encoder-decodeR for Fully Volumetric Fast sEgmentation of BRain MRI

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Recently, methods deploying solutions based on Convolutional Neural Networks (CNNs) are making the direct analysis of out-of-the-scanner data feasible. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This entails a loss of global contextual information thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed by 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements clearly prefer our solution with respect to the widely adopted atlas-based FreeSurfer software.
Collective Density Matrix of Identical Multi-level Atoms and Application in Superradiance

We present a collective description of density matrix for identical multi-level atoms, which are either excited initially or driven coherently or pumped incoherently. We consider density matrix as expectation value of projection or transition operators in a basis of atom's product states and then identify identical matrix elements with several integers, which specify uniquely the involved transition or projection operators. To remove the redundancy of these identical elements, we treat them as single quantity and derive the equation for this quantity by mapping the transition or projection operators to a single vector specified with the integers in Liouville space and work out the actions of the master equation constitutions (as superoperators) on these vectors. As a result, the number of necessary elements increases polynomially with the number of atoms rather than exponentially as expected if the redundancy is not removed. This numerical advantage is explored to study superradiance from hundreds of two-level atoms. Our simulation identifies the conditions of observing the superradiance with and without dipole involved.
Automated Spectral Kernel Learning

The generalization performance of kernel methods is largely determined by the kernel, but spectral representations of stationary kernels are both input-independent and output-independent, which limits their applications on complicated tasks. In this paper, we propose an efficient learning framework that incorporates the process of finding suitable kernels and model training. Using non-stationary spectral kernels and backpropagation w.r. t. the objective, we obtain favorable spectral representations that depends on both inputs and outputs. Further, based on Rademacher complexity, we derive data-dependent generalization error bounds, where we investigate the effect of those factors and introduce regularization terms to improve the performance. Extensive experimental results validate the effectiveness of the proposed algorithm and coincide with our theoretical findings.
Learning Team-Optimality for Decentralized Stochastic Control and Dynamic Games

In the study of stochastic dynamic team problems, analytical methods for finding optimal policies are often inapplicable due to lack of prior knowledge of the cost function or the state dynamics. Reinforcement learning offers a possible solution to such coordination problems. Existing learning methods for coordinating play either rely on control sharing among controllers or otherwise, in general, do not guarantee convergence to optimal policies. In a recent paper, we provided a decentralized algorithm for finding equilibrium policies in weakly acyclic stochastic dynamic games, which contain team games as an important special case. However, stochastic dynamic teams can in general possess suboptimal equilibrium policies whose cost can be arbitrarily higher than a team optimal policy's cost. In this paper, we present a reinforcement learning algorithm and its refinements, and provide probabilistic guarantees for convergence to globally optimal policies in team games as well as a more general class of coordination games. The algorithms presented here are strictly decentralized in that they require only access to local information such as cost realizations, previous local actions, and state transitions.
QuTiBench

Neural Networks have become one of the most successful universal machine-learning algorithms. They play a key role in enabling machine vision and speech recognition and are increasingly adopted in other application domains. Their computational complexity is enormous and comes along with equally challenging memory requirements in regards to capacity and access bandwidth, which limits deployment in particular within energy constrained, embedded environments. To address these implementation challenges, a broad spectrum of new customized and heterogeneous hardware architectures have emerged, often accompanied with co-designed algorithms to extract maximum benefit out of the hardware. Furthermore, numerous optimization techniques are being explored for neural networks to reduce compute and memory requirements while maintaining accuracy. This results in an abundance of algorithmic and architectural choices, some of which fit specific use cases better than others. For system-level designers, there is currently no good way to compare the variety of hardware, algorithm, and optimization options. While there are many benchmarking efforts in this field, they cover only subsections of the embedded design space. None of the existing benchmarks support essential algorithmic optimizations such as quantization, an important technique to stay on chip, or specialized heterogeneous hardware architectures. We propose a novel benchmark suite, QuTiBench, that addresses this need. QuTiBench is a novel multi-tiered benchmarking methodology (Ti) that supports algorithmic optimizations such as quantization (Qu) and helps system developers understand the benefits and limitations of these novel compute architectures in regard to specific neural networks and will help drive future innovation. We invite the community to contribute to QuTiBench to support the full spectrum of choices in implementing machine-learning systems.
High-performance Cryptographically Secure Pseudo-random Number Generation via Bitslicing

In this paper, a high-throughput Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) using the bitslicing technique is proposed. In such technique, instead of the conventional row-major data representation, column-major data representation is employed which allows the bitsliced implementation to take full advantage of all the available datapath of the hardware platform. We use LFSR-based (Linear Feedback Shift Register) PRNG for our implementation since its register oriented architecture perfectly suits the GPU's many-core structure and allows for usage of bitslicing technique which can further improve its performance. In our GPU implementation, each GPU thread is capable of generating a remarkable number of 32 pseudo-random bits in each LFSR clock cycle. In order to obtain cryptographically suitable properties, we propose an SIMD vectorized fully parallel bitsliced implementation of the LFSR-based, cryptographically secure MICKEY 2.0 stream cipher algorithm for CSPRNG. To the best of our knowledge, our method not only achieves better performance, but also significantly outperforms optical solutions in terms of performance per cost while maintaining an acceptable measure of randomness. It should be mentioned that our implementation successfully passes the NIST test for statistical randomness and bitwise correlation criteria. Our proposed methodology significantly outperforms the current best implementations in the literature for computer-based PRNG. Moreover, our evaluations show 6.6x improvement over the Nvidia's proprietary high-performance PRNG, cuRAND library, achieving 5.2 Tb/s of throughput on the affordable Nvidia GTX 980 Ti.
Optimality of the Subgradient Algorithm in the Stochastic Setting

We show that the Subgradient algorithm is universal for online learning on the simplex in the sense that it simultaneously achieves $O(\sqrt N)$ regret for adversarial costs and $O(1)$ pseudo-regret for i.i. d costs. To the best of our knowledge this is the first demonstration of a universal algorithm on the simplex that is not a variant of Hedge. Since Subgradient is a popular and widely used algorithm our results have immediate broad application.
Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration

To engender safe and efficient human-robot collaboration, it is critical to generate high-fidelity predictions of human behavior. The challenges in making accurate predictions lie in the stochasticity and heterogeneity in human behaviors. This paper introduces a method for human trajectory and intention prediction through a multi-task model that is adaptable across different human subjects. We develop a nonlinear recursive least square parameter adaptation algorithm (NRLS-PAA) to achieve online adaptation. The effectiveness and flexibility of the proposed method has been validated in experiments. In particular, online adaptation can reduce the trajectory prediction error by more than 28% for a new human subject. The proposed human prediction method has high flexibility, data efficiency, and generalizability, which can support fast integration of HRC systems for user-specified tasks.
Interferometric Closure Phase Uncertainties in the Low Signal-to-noise Ratio Regime

Closure phases are critical in astronomical interferometry. However, their uncertainties are difficult to compute numerically. We provide a method to efficiently compute interferometric closure phase distributions in terms of an approximate distribution that is valid in the low signal-to-noise ratio regime. This is done by first showing that the true phase distribution is well approximated by the von Mises distribution, then performing a convolution of three von Mises distributions. The resulting approximation superior to the normal distribution for all signal-to-noise ratios and, being fully analytic, allows for fast computations in statistical algorithms.