Miracle-dz/UniFAR-FTUs huggingface dataset:
Adapting Tree Structures for Processing with SIMD Instructions

In this paper, we accelerate the processing of tree-based index structures by using SIMD instructions. We adapt the B + -Tree and prefix B-Tree (trie) by changing the search algorithm on inner nodes from binary search to k-ary search. The k-ary search enables the use of SIMD instructions, which are commonly available on most modern processors today. The main challenge for using SIMD instructions on CPUs is their inherent requirement for consecutive memory loads. The data for one SIMD load instruction must be located in consecutive memory locations and cannot be scattered over the entire memory. The original layout of tree-based index structures does not satisfy this constraint and must be adapted to enable SIMD usage. Thus, we introduce two tree adaptations that satisfy the specific constraints of SIMD instructions. We present two di↵erent algorithms for transforming the original tree layout into a SIMD-friendly layout. Additionally, we introduce two SIMD-friendly search algorithms designed for the new layout. Our adapted B + -Tree speeds up search processes by a factor of up to eight for small data types compared to the original B + -Tree using binary search. Furthermore, our adapted prefix B-Tree enables a high search performance even for larger data types. We report a constant 14 fold speedup and an 8 fold reduction in memory consumption compared to the original B + -Tree.
Running head : STEREOTYPE THREAT AS REGULATORY MISMATCH Stereotype Threat Reinterpreted as a Regulatory Mismatch

Research documents performance decrements resulting from the activation of a negative task-relevant stereotype. We combine a number of strands of work to identify causes of stereotype threat in a way that allows us to reverse the effects and improve the performance of individuals with negative task-relevant stereotypes. We draw on prior work suggesting that negative stereotypes induce a prevention focus, and other research suggesting that people exhibit greater flexibility when their regulatory focus matches the reward structure of the task. This work suggests that stereotype threat effects emerge from a prevention focus combined with tasks that have an explicit or implicit gains reward structure. We find flexible performance can be induced in individuals who have a negative task-relevant stereotype by using a losses reward structure. We demonstrate the interaction of stereotypes and the reward structure of the task using chronic stereotypes and GRE math problems (Experiment 1), and primed stereotypes and a category learning task (Experiments 2a and 2b). We discuss implications of this research for other work on stereotype threat.
On the Influence of Emotional Feedback on Emotion Awareness and Gaze Behavior

This paper examines how emotion feedback influences emotion awareness and gaze behavior. Simulating a videoconference setup, 36 participants watched 12 emotional video sequences that were selected from the SEMAINE database. All participants wore an eye-tracker to measure gaze behavior and were asked to rate the perceived emotion for each video sequence. 3 conditions were tested: (c1) no feedback, i.e., the original video-sequences, (c2) correct feedback, i.e., an emoticon is integrated in the video to show the emotion depicted by the person in the video and (c3) random feedback, i.e., the emoticon displays at random an emotional state that may or may not correspond to the one of the person. The results showed that emotion feedback had a significant influence on gaze behavior, e.g., over time random feedback led to a decrease in the frequency of episodes of gaze. No effect of emotion display was observed for emotion recognition. However, experiments on the automatic emotion recognition using gaze behavior provided good performance, with better score on arousal than valence, and a very good performance was obtained in the automatic recognition of the correctness of the emotion feedback.
CREB Modulates the Functional Output of Nucleus Accumbens Neurons

Nucleus accumbens (NAc) medium spiny neurons cycle between two states, a functionally inactive downstate and a functionally active upstate. Here, we show that activation of the transcription factor cAMP-response element-binding protein (CREB), a common molecular response to several drugs of abuse, increases both duration of the upstate and action potential firing during the upstate. This effect of CREB is mediated by enhanced N-methyl-d-aspartate glutamate receptor (NMDAR) function: increased CREB activity increases both NMDAR-mediated synaptic currents and surface level of NMDARs, while inhibition of NMDARs abolishes the effect of CREB on upstate duration. Furthermore, mimicking the effect of CREB by pharmacological enhancement of NMDAR function in the NAc in vivo suppressed novelty- and cocaine-elicited locomotor activity. These findings suggest that by enhancing NMDAR-mediated synaptic transmission, CREB activation promotes the proportion of time NAc neurons spend in the upstate. This effect, along with the CREB enhancement of NAc membrane excitability (Dong, Y., Green, T., Saal, D., Marie, H., Neve, R., Nestler, E. J., and Malenka, R. C. (2006) Nat. Neurosci. 9, 475–477), may counteract drug-induced maladaptations in the NAc and thus ameliorate the addictive state.
There is a Van Douwen MAD family

We answer a long-standing question of Van Douwen by proving in ZFC that there is a MAD family of functions in ω ω that is also maximal with respect to infinite partial functions. In Section 3 we apply the idea of trace introduced in this proof to the still open question of whether analytic MAD families exist in ω ω . Using the idea of trace, we show that any analytic MAD families that may exist in ω ω must satisfy strong combinatorial constraints. We also show that it is consistent to have MAD families in ω ω that satisfy these constraints.
HOW DO DISKS SURVIVE MERGERS?

We develop a general physical model for how galactic disks survive and/or are destroyed in mergers and interactions. Based on simple dynamical arguments, we show that gas primarily loses angular momentum to internal torques in a merger, induced by the gravity of the secondary. Gas within some characteristic radius, determined by the efficiency of this angular momentum loss (itself a function of the orbital parameters, mass ratio, and gas fraction of the merging galaxies), will quickly lose angular momentum to the stars sharing the perturbed host disk, fall to the center, and be consumed in a starburst. We use a similar analysis to determine where violent relaxation of the premerger stellar disks is efficient on final coalescence. Our model describes both the dissipational and dissipationless components of the merger, and allows us to predict, for a given arbitrary encounter, the stellar and gas content of the material that will survive (without significant angular momentum loss or violent relaxation) to re-form a disk in the merger remnant, versus being dissipationlessly and violently relaxed or dissipationally losing angular momentum and forming a compact central starburst. We test these predictions with a large library of hydrodynamic merger simulations, and show that they agree well (with small scatter) with the properties of simulated merger remnants as a function of merger mass ratio, orbital parameters, and gas distributions in simulations which span a wide range of parameter space in these properties as well as prescriptions for gas physics, stellar and active galactic nucleus (AGN) feedback, halo and initial disk structural properties, redshift, and galaxy masses. We show that, in an immediate (short-term) sense, the amount of stellar or gaseous disk that survives or re-forms following a given interaction can be understood purely in terms of simple, well understood gravitational physics, independent of the details of the interstellar medium gas physics or stellar and AGN feedback. This allows us to demonstrate and quantify how these physics are in fact important, in an indirect sense, to enable disks to survive mergers by lowering star formation efficiencies in low-mass systems (allowing them to retain large gas fractions) and distributing the gas to large radii. The efficiency of disk destruction in mergers is a strong function of gas content—our model allows us to explicitly predict and demonstrate how, in sufficiently gas-rich mergers (with quite general orbital parameters), even 1:1 mass-ratio mergers can yield disk-dominated remnants, and more realistic 1:3–1:4 mass-ratio major mergers can yield systems with <20% of their mass in bulges. We discuss a number of implications of this modeling for the abundance and morphology of bulges as a function of mass and redshift, and provide simple prescriptions for the implementation of our results in analytic or semianalytic models of galaxy formation.
Robust Incremental Neural Semantic Graph Parsing

Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms. We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments. Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser. Further, the 86.69% Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.
Transitioning From On-Campus to Online in a Master of Science Nursing Program: a Comparative Study of Academic Success

ABSTRACT This comparative study examined the academic success rates of online graduate nursing students by comparing final grades to the on-campus cohort of graduate nursing students prior to the program transitioning online. Analysis was conducted by identifying and comparing final course grades in 4 required Master of Science in Nursing (MSN) courses over a period of 6 years. Five key themes related to online student success are discussed, including the need for graduate nursing MSN programs to transition to an online format, barriers to online learning, comprehensive program evaluation of an online degree program, online student retention approaches, and exploration of whether online students have been shown to be as academically successful as on-campus students. Results reveal that both cohorts of course delivery modes yield academically successful MSN students and both course delivery modes are providing the MSN program with almost equal pass rates by the students taking the required core courses.
Contextual Influences on a Coping Skills Intervention for Adolescents in Substance Use Recovery

Co-occurring disorders of substance use and mental health conditions occur in about 75% of adolescents, with depression ranked as the second most common co-occurring disorder, in 2030% of adolescents (Hersh et al., 2014; Shrier et al., 2003). Contextual factors (i.e. parental substance use and DCF involvement) put adolescents at increased risk of substance use initiation and co-morbid depression (Clark et al., 2005; Walden et al., 2007). Results from the current literature are undetermined on how to best address co-occurring substance use disorders (SUD) and mental health diagnoses in adolescents. Researchers sought to do this in the current study through a mindfulness-based coping skills intervention, with a sample that included 57 participants, 27 in a recovery high school (RHS), and 29 non-treated comparison participants. The RHS participants engaged in a six-week mindfulness-based coping skills intervention aimed at reducing depression symptomology and emotion dysregulation. Assessments were completed at weeks one and six for RHS participants and at a single assessment time for the non-treated comparison participants. Results showed reduced depression symptomology for the sample as a whole for those indicating parental substance use and DCF involvement. RHS participants indicated high levels of depression above the clinical cut off compared to the non-treated comparison group at the initial assessment and following the six-week intervention. However, RHS participants demonstrated significantly reduced depression symptomology following the six-week mindfulness-based intervention. The current study has treatment implications for reducing depression symptomology via mindfulness-based interventions for youth in early SUD recovery who indicate parental substance use and DCF involvement. iii
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL

This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework. cltorch enables training of deep neural networks on GPUs from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch contains sufficient implementation to run models such as AlexNet, VGG, Overfeat, and GoogleNet. It is written using the OpenCL language, a portable compute language, governed by the Khronos Group. cltorch is the top-ranked hardware-agnostic machine learning framework on Chintala's convnet-benchmarks page. This paper presents the technical challenges encountered whilst creating the cltorch backend for Torch, and looks in detail at the challenges related to obtaining a fast hardware-agnostic implementation. The convolutional layers are identified as the key area of focus for accelerating hardware-agnostic frameworks. Possible approaches to accelerating the convolutional implementation are identified including: implementation of the convolutions using the implicitgemm or winograd algorithm, using a GEMM implementation adapted to the geometries associated with the convolutional algorithm, or using a pluggable hardware-specific convolutional implementation.
Do leadership-dominated parties change more?

ABSTRACT It is often assumed that political parties have more fluid party platforms than in the past because internal veto players – like party activists – have lost the power to restrain the office-seeking party elite. Several case studies subscribe to this view. However, there is no systematic assessment of this relationship. Using new data this research note investigates this relationship and finds a clear positive effect of leadership domination on party platform change. With leadership domination increasing over time, our model predicts increasing fluidity in party platforms. This note provides a motivational and a numerical argument in favor of this hypothesis, considers alternative hypotheses and provides empirical evidence for the claim using two different datasets.
Visions of Change as Visions of Continuity

Despite wide consensus that leader vision is a key vehicle for leaders to motivate followers to support organizational change, it remains far from clear what characterizes an effective vision of change. Research on organizational change that uses a social identity perspective has asserted that one important reason why followers resist change is because change may pose a threat to their subjective sense of continuity of organizational identity. Accordingly, we hypothesize that leaders that communicate visions of change can address this resistance by assuring followers that the essence of the organizational identity will remain unchanged – making their vision of change also a vision of continuity. In line with our proposition that collective continuity is valued because it serves an uncertainty-reduction function, such visions should be more predictive of vision effectiveness the higher follower work-related uncertainty. Across a field study and an experimental study we found support for these predictions.
The Time of Maximum Post-Ischemic Hyperperfusion Indicates Infarct Growth Following Transient Experimental Ischemia

After recanalization, cerebral blood flow (CBF) can increase above baseline in cerebral ischemia. However, the significance of post-ischemic hyperperfusion for tissue recovery remains unclear. To analyze the course of post-ischemic hyperperfusion and its impact on vascular function, we used magnetic resonance imaging (MRI) with pulsed arterial spin labeling (pASL) and measured CBF quantitatively during and after a 60 minute transient middle cerebral artery occlusion (MCAO) in adult rats. We added a 5% CO2 - challenge to analyze vasoreactivity in the same animals. Results from MRI were compared to histological correlates of angiogenesis. We found that CBF in the ischemic area recovered within one day and reached values significantly above contralateral thereafter. The extent of hyperperfusion changed over time, which was related to final infarct size: early (day 1) maximal hyperperfusion was associated with smaller lesions, whereas a later (day 4) maximum indicated large lesions. Furthermore, after initial vasoparalysis within the ischemic area, vasoreactivity on day 14 was above baseline in a fraction of animals, along with a higher density of blood vessels in the ischemic border zone. These data provide further evidence that late post-ischemic hyperperfusion is a sequel of ischemic damage in regions that are likely to undergo infarction. However, it is transient and its resolution coincides with re-gaining of vascular structure and function.
The Selection of ECEC Programs by Australian Families: Quality, Availability, Usage and Family Demographics

HIGH-QUALITY EARLY CHILDHOOD education and care (ECEC) programs have the potential to ameliorate socioeconomic status (SES) gradients. In the Australian ECEC market, however, there is no guarantee that children from low SES backgrounds access high-quality ECEC programs. This study tested the influence of family SES on the selection of ECEC program quality. Participants were 2494 children enrolled in up to 1427 ECEC classrooms (mean age at entry = 43 months, SD = eight months). The study controlled for a range of child, family, home and community-level background factors. Both cross-sectional (linear regression) and longitudinal (growth models) methods are used. The study confirmed that children from lower SES families were more likely to attend lower quality programs. Longitudinal modelling showed the largest quality gap before kindergarten. To narrow SES-related achievement gaps there is a need to significantly improve aspects of program quality that influence children's development, and specifically to do so in programs for younger children. There is a particular need to target ECEC programs in lower SES areas to ameliorate the observed SES quality gradient. The findings further challenge current policy directions from the Productivity Commission inquiry into child care and early learning.
MiR-146a Regulates SOD2 Expression in H2O2 Stimulated PC12 Cells

SOD2 (superoxide dismutase 2) is one of the endogenous antioxidant enzymes that protect against reactive oxygen species. While explorations of SOD2 expression regulation are mainly focused on transcriptional and post-translational activation, there are few reports about the post-transcriptional regulation of SOD2. MicroRNAs (miRNAs) are 21nt-25nt (nucleotide) small noncoding RNAs that have emerged as indispensable regulators of gene expression. Here we show that miR-146a, a widely expressed miRNA, is up-regulated by H2O2-induced stress. By sequence analysis we found a binding site for miR-146a in the sod2 mRNA 3′UTR, and a luciferase reporter assay confirmed that miR-146a can interact with this sod2 regulatory region. Our results further show that miR-146a could down-regulate the SOD2 protein expression, and antisense-miR-146a could reverse the decrease of both the SOD2 level and cell viability in H2O2 treated PC12 cells. In conclusion, here we have identified a novel function of miR-146a in the post-transcriptional regulation of SOD2 expression.
A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks (Abstract Only)

FPGA-based hardware accelerator for convolutional neural networks (CNNs) has obtained great attentions due to its higher energy efficiency than GPUs. However, it has been a challenge for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipeline (temporal parallelism) stages. Experiment results show that the proposed architecture running at 90 MHz on a Xilinx Virtex-7 FPGA achieves a computing throughput of 7.663 TOPS with a power consumption of 8.2 W regardless of the batch size of input data. This is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests (in small batch size). For processing static data (in large batch size), the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Face recognition using curvelet based PCA

This paper identifies a novel feature space to address the problem of human face recognition from still images. This is based on the PCA space of the features extracted by a new multiresolution analysis tool called Fast Discrete Curvelet Transform. Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform. Inspired by these attractive attributes of curvelets, we introduce the idea of decomposing images into its curvelet subbands and applying PCA (Principal Component Analysis) on the selected subbands in order to create a representative feature set. Experiments have been designed for both single and multiple training images per subject. A comparative study with wavelet-based and traditional PCA techniques is also presented. High accuracy rate achieved by the proposed method for two well-known databases indicates the potential of this curvelet based feature extraction method.
Handling Payoff Uncertainty in Green Security Domains with Adversary Bounded Rationality

Research on Stackelberg Security Games (SSG) has recently shifted to green security domains, for example, protecting wildlife from illegal poaching. Previous research on this topic has advocated the use of behavioral (bounded rationality) models of adversaries in SSG. As its first contribution, this paper, for the first time, provides validation of these behavioral models based on real-world data from a wildlife park. The paper’s next contribution is the first algorithm to handle payoff uncertainty – an important concern in green security domains – in the presence of such adversarial behavioral models.