Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Wakening Effectiveness of Fire Alarms in Residential Areas.

The key aspects and features of the report 'Improving the wakening effectiveness of fire alarms in residential areas' for the IPENZ Conference by Thomas Grace (Masters in Fire Engineering, University of Canterbury) are discussed. The report describes the sleep characteristics of humans and shows the difference in sleep patterns with age and gender; and also includes fire alarm characteristics, and how different frequencies, loudness and sound patterns affect human arousal during sleep.
Gaussian-Enveloped Tones (GET): a vocoder that can simulate pulsatile stimulation in cochlear implants

Acoustic simulations of cochlear implants allow comprehensive evaluation of not only perceptual performance under impoverished listening conditions but also relative contributions of classical spectral and temporal cues to speech recognition. Conventional simulations use continuous sinusoidal or noise carriers, lacking the vital pulsatile characteristics in a typical cochlear-implant processing strategy. The present study employed Gaussian-enveloped tones (GETs) as a discrete carrier to simulate the electric pulse train in modern cochlear implants. Two types of GET vocoders were implemented and evaluated in normal-hearing listeners to compare their performance to actual cochlear-implant performance. In the first implementation, GETs with different durations were used to simulate electric current interaction across channels that produced vowel and consonant recognition similar to the actual cochlear-implant result. In the second implementation, a direct mapping from electric pulses to GETs was developed to simulate a widely-used clinical n-of-m strategy in cochlear implants. The GET processing simulated the actual implant speech in noise perception in terms of the overall trend, the absolute mean scores, and their standard deviations. The present results demonstrated that the pulsatile GET vocoders simulate the cochlear implant performance more accurately than the conventional sinusoid or noise vocoders.
A Corpus of Spatial Reasoning Problems

Spatial reasoning is an important part of common-sense reasoning but has most often been examined in isolation from other areas of common sense knowledge representation. Spatial reasoning also has been a prominent area of research in linguistic semantics. In this work, we aim to create a corpus of common sense reasoning problems formalized in the context of a large and comprehensive theory of world knowledge, in the hopes that it will be more generally reusable on a wide variety of practical reasoning problems. An additional goal is to demonstrate the computational sufficiency of this work by employing automated reasoning to solve the problems. Finally, we also state the problems first in natural language, to provide a set of test cases for theories of linguistic semantics and computational linguistics, with a computational representation that can help to validate whether any method or process of interpreting language into a computational logic representation is correct or sufficient. This is an ambitious set of goals, and it will not be possible to provide solutions for all the problems that could be formulated in natural language. In particular, we do not attempt to create an implementation, at least at this time, for a system that can translate all of the stated problems from language to logic. Many of the problems also appear to require a logic and reasoning system beyond first order logic. It is important however, to have challenges that are unsolved in order to motivate research. We utilize the Suggested Upper Merged Ontology (SUMO)[5, 7], a comprehensive ontology of around 20,000 concepts and 80,000 hand-authored logical statements in a higher-order logic, that has an associated integrated development environment[9] integrated with leading theorem provers such as Eprover [10] Vampire [4] and LEO-II [1], and manually-created links[6] to the WordNet lexico-semantic database[3]. We described [9] elsewhere how to translate SUMO to the strictly first order language of TPTP [12], as well as TF0 [8] and THF[2].
The Attributions To Human Resource Policy: Implications With Work Family Person Environment Fit (WFPEF)

The attributions to HR policy have been widely used in developing a theoretical foundation for person environment fit and work family practices. This research construes the theories of attribution and conceptualized that with the cognitive process under the domain of work family practice which is highly assimilated with perceived work family person environment fit (WEPEF). Moreover, this study has differentiated the attributions under work family practices with a particular environmental source and certain attributions to the intent’s source while adopting a specific practice. This study has also argued that available supplies with an employee to integrate or segment under the domains of work and family significantly indulged with attributions. Moreover, the study links the attribution process with different employees’ attitudes and behaviors pertained to different levels of environments (supervisor and Webology (ISSN: 1735-188X) Volume 19, Number 2, 2022 968 http://www.webology. org organization) to have a fit mechanism of work family practices. Finally, this research contributed a consistent picture of work family fit which would also be helpful for future research.
GABAergic and cholinergic basal forebrain inputs

165 words) 14 The olfactory bulb (OB), the first relay for odor processing in the brain, receives dense 15 GABAergic and cholinergic long-range projections from basal forebrain (BF) nuclei that 16 provide information about the internal state and behavioral context of the animal. However, 17 the targets, impact and dynamic of these afferents are still unclear. I studied how BF 18 synaptic inputs modulate activity in diverse subtypes of periglomerular (PG) interneurons 19 using optogenetic stimulation and loose cell-attached or whole-cell patch-clamp recording in 20 OB slices from adult mice. GABAergic BF inputs potently blocked PG cells firing except in a 21 minority of calretinin-expressing cells in which GABA release elicited spiking. Parallel 22 cholinergic projections excited a previously overlooked PG cell subtype via synaptic 23 activation of M1 muscarinic receptors. Low frequency stimulation of the cholinergic axons 24 drove persistent firing in these PG cells thereby increasing tonic inhibition in principal 25 neurons. Taken together, these findings suggest that modality-specific BF inputs can 26 orchestrate synaptic inhibition in OB glomeruli using multiple, potentially independent, 27 inhibitory or excitatory target-specific pathways. 28
Understanding EFL Students’ Reading Motivation and Online Reading Practice in Relation to Their Reading Achievement in a Full Online Learning Context

The study aims to reveal the correlation between students' reading motivation, online reading practice, and reading proficiency. The participants of the study are students of Academic Reading Class in one of Islamic University in Kediri, East Java Indonesia. The data are collected through reading motivation questionnaire based on the Motivation for Reading English Questionnaire (MREQ), online reading practice questionnaire adapted from Putro and Lee (2017) and reading proficiency test. The result shows that high-reading motivation students do not necessarily have high reading achievement. On the other hand, students who do more frequent reading online have higher reading achievement. As a result of multiple regression analysis, both reading motivation and online reading practice give a potential contribution to the students' reading proficiency. EFL teachers and students are suggested to maximize the internet facilities to explore the information provided to support the students' reading activity.
Development of Pavement Slippery Index and Its Application in Traffic Operation Management

The concept of pavement slippery index was put forward,grading of slippery indexes and their threshold values were proposed based on previous researches,and the link between practical pavement friction coefficient and descriptive phenomena of slippery road was built.A method for determining free flow speed under slippery pavement condition was provided based on theoretical analysis of stop sight distance.Combined with speed-flow relationship in highway capacity study,the recommended speed limit was determined under the condition of different slippery roads and given traffic flow.Furthermore,application of pavement slippery index in variable speed limit control on expressway,advisory and/or warning information provision for drivers on road were proposed based on the study results.
Differences in Genetic Correlations between Posttraumatic Stress Disorder and Alcohol Use Disorder-Related Phenotypes Compared to Alcohol Consumption-Related Phenotypes

Background: Posttraumatic Stress Disorder (PTSD) tends to co-occur with greater alcohol consumption as well as alcohol use disorder (AUD). However, it is unknown whether the same etiologic factors that underlie PTSD-AUD comorbidity also contribute to PTSD- alcohol consumption. Methods: We used summary statistics from large-scale genome-wide association studies (GWAS) of European-ancestry (EA) and African-ancestry (AA) participants to estimate genetic correlations between PTSD (both the diagnosis and re-experiencing symptoms) and a range of alcohol consumption-related and AUD-related phenotypes (e.g., drinks per week, max drinks, consumption, AUD). Results: In EAs, there were positive genetic correlations between PTSD phenotypes and AUD-related phenotypes (i.e., Alcohol Use Disorders Identification Test (AUDIT) problem score, maximum alcohol intake, AUD, and alcohol dependence) (rGs: .132-.533, all FDR adjusted p<.05). However, the genetic correlations between PTSD phenotypes and alcohol consumption -related phenotypes (i.e., drinks per week, AUDIT consumption score, AUDIT total score, and a combination of consumption and problems) were negatively associated or non-significant (rGs: -.417- -.042, FDR adjusted p: <.05-NS). For AAs, the direction of correlations was sometimes consistent and sometimes inconsistent with that in EAs, and the ranges were larger (rGs for AUD--related: -.275 -.266, FDR adjusted p: NS, alcohol consumption-related: .145-.699, FDR adjusted p: NS). Conclusions: These findings illustrate that the genetic associations between consumption and problem alcohol phenotypes and PTSD differ in both strength and direction. Thus, the genetic factors that may lead someone to develop PTSD and consume large quantities of alcohol are not the same as those that lead someone to develop PTSD-AUD.
Multiple sclerosis cortical lesion detection with deep learning at ultra‐high‐field MRI

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter‐rater reliability. To accelerate this task, we developed a deep‐learning‐based framework (CLAIMS: Cortical Lesion AI‐Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T2*‐weighted GRE, and 0.5 mm T2*‐weighted EPI. The second dataset consisted of 20 scans including only 0.75 × 0.75 × 0.9 mm3 MP2RAGE. CLAIMS was first evaluated using sixfold cross‐validation with single and multi‐contrast 0.5 mm MRI input. Second, the performance of the model was tested on 0.7 mm MP2RAGE images after training with either 0.5 mm MP2RAGE×4, 0.7 mm MP2RAGE, or alternating the two. Third, its generalizability was evaluated on the second external dataset and compared with a state‐of‐the‐art technique based on partial volume estimation and topological constraints (MSLAST). CLAIMS trained only with MP2RAGE×4 achieved results comparable to those of the multi‐contrast model, reaching a CL true positive rate of 74% with a false positive rate of 30%. Detection rate was excellent for leukocortical and subpial lesions (83%, and 70%, respectively), whereas it reached 53% for intracortical lesions. The correlation between disability measures and CL count was similar for manual and CLAIMS lesion counts. Applying a domain‐scanner adaptation approach and testing CLAIMS on the second dataset, the performance was superior to MSLAST when considering a minimum lesion volume of 6 μL (lesion‐wise detection rate of 71% versus 48%). The proposed framework outperforms previous state‐of‐the‐art methods for automated CL detection across scanners and protocols. In the future, CLAIMS may be useful to support clinical decisions at 7 T MRI, especially in the field of diagnosis and differential diagnosis of MS patients.
Low Haemoglobin Levels are Associated with Reduced Psychomotor and Language Abilities in Young Ugandan Children.

Children living in Sub-Saharan Africa are vulnerable to developmental delay, particularly in the critical first five years due to various adverse exposures including disease and nutritional deficiencies. Anaemia and iron deficiency (ID) are highly prevalent in pregnant mothers and young children and are implicated in abnormal brain development. However, available evidence on the association between anaemia, ID and neurodevelopment in Sub-Saharan Africa is limited. Using data from the Entebbe Mother and Baby Study prospective birth cohort, we examined the effect of maternal and child haemoglobin (Hb) levels and child iron status on developmental scores in 933 and 530 pre-school Ugandan children respectively. Associations between Hb levels, iron status and developmental scores were assessed using regression analyses adjusting for potential confounders. Lower maternal and child Hb levels were associated with reduced psychomotor scores at 15 months, while only lower Hb levels in infancy were associated with reduced language scores. We found no evidence that anaemia or ID was associated with cognitive or motor scores at five years. This study emphasizes the importance of managing anaemia in pregnancy and infancy and highlights the need for further studies on the effects of anaemia and ID in children living in Sub-Saharan Africa.
Thermodynamics and optimal protocols of multidimensional quadratic Brownian systems

We characterize finite-time thermodynamic processes of multidimensional quadratic overdamped systems. Analytic expressions are provided for heat, work, and dissipation for any evolution of the system covariance matrix. The Bures-Wasserstein metric between covariance matrices naturally emerges as the local quantifier of dissipation. General principles of how to apply these geometric tools to identify optimal protocols are discussed. Focusing on the relevant slow-driving limit, we show how these results can be used to analyze cases in which the experimental control over the system is partial.
Zvonkin’s transform and the regularity of solutions to double divergence form elliptic equations

Abstract We study qualitative properties of solutions to double divergence form elliptic equations (or stationary Kolmogorov equations) on It is shown that the Harnack inequality holds for nonnegative solutions if the diffusion matrix A is nondegenerate and satisfies the Dini mean oscillation condition and the drift coefficient b is locally integrable to some power p > d. We establish new estimates for the Lp -norms of solutions and obtain a generalization of the known theorem of Hasminskii on the existence of a probability solution to the stationary Kolmogorov equation to the case where the matrix A satisfies Dini’s condition or belongs to the class VMO. These results are based on a new analytic version of Zvonkin’s transform of the drift coefficient.
Dense Voxel Fusion for 3D Object Detection

Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations, improving expressiveness in low point density regions. To enhance multi-modal learning, we train directly with projected ground truth 3D bounding box labels, avoiding noisy, detector-specific 2D predictions. Both DVF and the multi-modal training approach can be applied to any voxel-based LiDAR backbone. DVF ranks 3rd among published fusion methods on KITTI’s 3D car detection benchmark without introducing additional trainable parameters, nor requiring stereo images or dense depth labels. In addition, DVF significantly improves 3D vehicle detection performance of voxel-based methods on the Waymo Open Dataset.
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally eﬃcient algorithm that attains an optimal √ 𝑇 regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.
A characterization of $\dot W^{1,p}(\mathbb R^d)$

For 1 < p < ∞ we give a characterization of the Sobolev space ˙ W 1 ,p ( R d ) in terms of the oscillations of a function on balls of varying centers and radii. Our work is motivated both by the study of trace ideal properties of commutators with singular integral operators and by work of Nguyen and by Brezis, Van Schaftingen and Yung on derivative-free characterizations of Sobolev spaces.
Supply chain management strategies for small fast fashion firms: the case of the Dongdaemun Fashion District in South Korea

ABSTRACT This paper contributes to the growing literature on fast fashion by examining how small fashion firms efficiently manage their supply chains in order to boost the supply efficiency of fast fashion. The results of a survey of manufacturing wholesalers in the Dongdaemun Fashion District (DFD) in South Korea suggest that lead times and wholesaler–retailer relationships which were linked to business performance had significant effects on supplier–wholesaler relationships. In turn, small fast fashion firms in the DFD retaining closer relationships with their buyers and suppliers were able to produce fast fashion more rapidly than were other rival firms. This paper provides useful implications for both practitioners and scholars in the fashion industry.
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem

The traveling salesman problem is a fundamental combinatorial optimization problem with strong exact algorithms. However, as problems scale up, these exact algorithms fail to provide a solution in a reasonable time. To resolve this, current works look at utilizing deep learning to construct reasonable solutions. Such efforts have been very successful, but tend to be slow and compute intensive. This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network. We show that we can construct a model capable of learning without supervision and inferences significantly faster than current autoregressive approaches. We also empirically evaluate the benefits of including optimal transport algorithms within deep learning models to enforce assignment constraints during end-to-end training.
Knock, knock. Who's there? - Identifying football player jersey numbers with synthetic data

Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is still challenging due to changing camera angles, low video resolution, small object size in wide-range shots and transient changes in the player's posture and movement. In this paper we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically (accuracy increase of ~9% overall, ~18% on low frequency numbers) making our approach achieve state of the art results.
Analysis of Final Year Students’ Satisfaction with Information Resources and Services in Four Nigerian Federal University Libraries

This paper examined user satisfaction amongst final year students in four federal university libraries in South-East Zone of Nigeria. A rating scale designed for this study was validated by two experts while its reliability test using Cronbach Alpha produced a coefficient index of r = 0.86. The availability sampling technique was used to administer 200 copies of the research instrument to respondents in each of the institutions surveyed. A combination of descriptive and inferential statistics was used for data analysis while frequency tables were used to present the results. The level of significance was tested at 0.05% through independent sample t-test. The findings revealed that graduating students are satisfied with the traditional library resources, reference services, photocopying services, as well as ergonomic-related facilities provided by libraries in their respective institutions. However, majority of these students expressed disappointment with the level of access to computers and other ICTs, irregular internet services, outdated textbooks, among others. The recommendations include: provision of access to computers and other ICT facilities and provision of adequate financial resources.
Model-agnostic out-of-distribution detection using combined statistical tests

We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentiable generative model. The idea is to combine a classical parametric test (Rao's score test) with the recently introduced typicality test. These two test statistics are both theoretically well-founded and exploit different sources of information based on the likelihood for the typicality test and its gradient for the score test. We show that combining them using Fisher's method overall leads to a more accurate out-of-distribution test. We also discuss the benefits of casting out-of-distribution detection as a statistical testing problem, noting in particular that false positive rate control can be valuable for practical out-of-distribution detection. Despite their simplicity and generality, these methods can be competitive with model-specific out-of-distribution detection algorithms without any assumptions on the out-distribution.