Miracle-dz/UniFAR-FTUs huggingface dataset:
Curricula, Teaching Methods, and Success Metrics of Clinician–Scientist Training Programs: A Scoping Review

Purpose To describe the literature on clinician–scientist training programs to inform the development of contemporary and inclusive training models. Method The authors conducted a scoping review, searching the PubMed/MEDLINE, CINAHL, and Embase databases from database inception until May 25, 2020. Studies presenting primary research that described and evaluated clinician–scientist training programs were identified for data abstraction. On the basis of deductive and inductive methods, information about program characteristics, curricula, teaching strategies, and success metrics was extracted. The extracted variables were analyzed using descriptive statistics. Results From the initial 7,544 citations retrieved and 4,974 unique abstracts screened, 81 studies were included. Of the 81 included studies, 65 (80.2%) were published between 2011 and 2020, 54 (66.7%) were conducted in the United States, and 64 (79.0%) described programs that provided broad clinician–scientist training. Few programs provided funding or protected research time or specifically addressed needs of trainees from underrepresented minority groups. Curricula emphasized research methods and knowledge dissemination, whereas patient-oriented research competencies were not described. Most programs incorporated aspects of mentorship and used multiple teaching strategies, such as direct and interactive instruction. Extrinsic metrics of success (e.g., research output) were dominant in reported program outcomes compared with markers of intrinsic success (e.g., career fulfillment). Conclusions Although programs are providing clinician–scientists with practical skills training, opportunities exist for curricular and pedagogic optimization that may better support this complex career path. Training programs for clinician–scientists can address contemporary issues of wellness and equity by reconsidering metrics of program success and evolving the core tenets of their education models to include equity, diversity, and inclusion principles and patient-oriented research competencies.
Impact of Training and Development on Employees’ Performance in Epenal Group Ltd

: The study examined the impact of training and development on employees' performance in Epenal Group Ltd., Rivers State. Four (4) research questions guided the study. A descriptive design was adopted for the paper. A sample of 300 workers, consisting of 38 dredging workers, 55 security officers, 46 staff from the piling unit, 45 from civil construction, 56 from pipeline construction, 44 boat builders, and 16 equipment leases, was drawn using random quota sampling. The instrument used for the study was a self-structured questionnaire developed by the researcher. The instrument was validated by experts, and a reliability coefficient of 0.75 was obtained using Pearson‟s product moment correlation. The data collected was analysed using the mean and standard deviation. The results revealed a high influence of training and development on employees‟ performance. The study found that on-the-job and off-the-job training influence the productivity of employees. The study also established that training and development positively influence employees‟ job knowledge and the work quality of employees. It was recommended that employers should have compulsory training programmes for all employees in order to improve their knowledge and understanding of annual business strategy and objectives. Employees should be provided with effective training (on-the-job and off-the-job training) in order to reduce the time spent by managers on supervising employees. Employees should be provided with more training programmes in order to reduce the cost of recruiting and training new staff members.
Density-Subspace Clustering Based on Frequent Data Pattern

This paper irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Subspace clustering algorithms have been proposed to find the clusters in hidden subspaces. Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Subspace clustering is an efficient approach to clustering high dimensional data. It has recently been developed rapidly. Subspace clustering assumes that different subspace may contain different meaningful cluster. A subspace cluster is a subset of points together with a subset of attributes, such that the cluster points project onto a small range of values in each of these attributes, and are uniformly distributed in the remaining attributes. This paper proposes a subspace clustering algorithm which follows the bottom-up strategy, evaluating each dimension separately and then using only those dimensions with high density in further steps. We realize the analogy between mining frequent item sets and discovering the relevant subspace for a given cluster, proposes a methodology for finding subspaces clusters by mining frequent item sets and present heuristics that improve its quality by regarding the intervals in all dimensions as a set of unique items in frequent item set mining problem, any k-dimensional unit can be regarded as a k-item set, i.e., an item set of cardinality k. Thus, to identify the dense units satisfying the density thresholds in subspace clustering is similar to mine the frequent item sets satisfying the minimum support in frequent item set mining. Efficient algorithms to discover frequent patterns are crucial in data mining research. MFP algorithm converts a transaction database to an MFP tree through scanning the transaction database only once, then prunes the tree and at last mine the tree. Because the MFP algorithm scans a transaction database one time less than the FP growth algorithm, the MFP algorithm is more efficient under certain conditions. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a highdensity region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called “the density divergence problem” in this paper, which refers to the phenomenon that the region densities vary in different subspace cardinalities. To tackle the density divergence problem, in this paper, we devise a novel subspace clustering model to discover the clusters based on the relative region densities in the subspaces, where the clusters are regarded as regions whose densities are relatively high as compared to the region densities in a subspace. Based on this idea, different density thresholds are adaptively determined to discover the clusters in different subspace cardinalities. Keywords— Clustering, Density, Subspace Clustering, Frequent Data Pattern.
Breaking KASLR on Mobile Devices without Any Use of Cache Memory

Microarchitectural attacks utilize the performance optimization constructs that have been studied over decades in computer architecture research and show the vulnerability of such optimizations in a realistic framework. One such highly performance driven vulnerable construct is speculative execution. In this paper, we focus on the problem of breaking the kernel address-space layout randomization (KASLR) on modern mobile devices without using cache memory as a medium of observation. However, there are some challenges to breaking KASLR on ARM CPUs. The first challenge is that eviction strategies on ARM CPUs are slow, and the microarchitectural attacks exploiting the cache as a covert channel cannot be implemented on modern ARM CPUs. The second challenge is that non-canonical addresses are stored in the store buffer, although they are invalid. As a result, previous microarchitectural attacks distinguish such addresses as valid kernel addresses erroneously. In this paper, we focus on these challenges to close current gaps in the implementation of recent attacks against modern CPUs. We show how a Translation Look-aside Buffer (TLB) can be used to circumvent the cache memory as a covert channel in order to attack ASLR on both ARM and Intel CPUs. To the best of our knowledge, we are the first to break KASLR on ARM-based Android and iOS mobile devices. Furthermore, our attacks can be performed in JavaScript to break KASLR of the browser without the need for an Evict+Reload operation, which consumes a lot of time. The results of our attacks show that the attacker can distinguish whether or not the virtual address is valid in less than 0.0417 seconds and 0.0488 seconds on Android and iOS mobile devices, respectively.
A comparison of periodization models with equated volume and intensity for strength

PURPOSE: The purpose of the present investigation was to determine if significant differences exist among three different periodization programs in eliciting changes in strength. METHODS: Twenty-eight recreationally trained college-aged volunteers (mean + SD; 22.29 + 3.98) of both genders were tested for bench press, leg press, body fat %, chest circumference, and thigh circumference during initial testing. Following initial testing, subjects were randomly assigned to one of three training groups: 1) Linear Periodization (LP) (n=9), 2) Daily Undulating Periodization (DUP) (n=10), or 3) Weekly Undulating Periodization (WUP) (n=9). The training regimen for each group consisted of a 9 week, 3 day per week program with volume and intensity equated for all groups. Training loads were assigned as heavy (90%, 4RM), medium (85%, 6RM), or light (80%, 8RM) for bench press and leg press exercises. Subjects were familiarized with the CR-10 RPE scale, and instructed to achieve an 8 or 9 on the final repetition of each set for all other exercises. Subjects were then re-tested following four weeks of training. Training loads were then adjusted according to the new 1RM. Subjects were then re-tested following five more weeks of exercise. A 3x3 analysis of variance with repeated measures was conducted to examine differences between groups and to examine changes in each variable between T1 and T3. RESULTS: No significant differences (p > .05) were observed between groups for bench press, leg press, body fat %, chest circumference, or thigh circumference at all time points. Groups were then collapsed, and significant (p < .05) increases in bench press and leg press strength were demonstrated at all time points (T1-T3). Body fat decreased, while thigh circumference increased significantly (p < .05) from T1-T2 and T1-T3 with no significant change between the second two testing sessions. Chest circumference was significantly (p < .05) increased from T2-T3. CONCLUSION: No significant differences were observed between groups, however, each of the three models proved effective in increasing bench press and leg press strength over the course of nine weeks.
The Case for ML-Enhanced High-Dimensional Indexes

The case of learned indexes has recently inspired intensive research in the database community. It has been shown that learned indexes can often outperform traditional indexes such as B-tree on low-dimensional data. However, learned indexes suffer from inherent difficulties due to the “curse of dimensionality” when applied to datasets with very high dimensions (e.g., time series data). In this paper, we explore the “middle ground” between traditional and learned indexes where we can leverage advantages of both, targeting high-dimensional data. Specifically, we study ML-enhanced indexes in the context of processing 𝑘 -nearest-neighbor ( 𝑘 NN) queries over time series data. Our ML-enhanced indexes take traditional tree-based indexes as inputs, and train deep neural networks that capture the distribution of nearest neighbors among index leaf nodes. We then process 𝑘 NN queries by scanning the leaf nodes with respect to the order suggested by the neural networks (rather than their original order). Experimental evaluation shows that ML-enhanced indexes can significantly outperform their traditional counterparts in terms of the recall of the nearest neighbors with respect to the number of index leaf nodes being scanned.
Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

—— Accurate short-term load forecasting is essential to modern power system and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression (LR), auto-regressive integrated moving average (ARIMA), deep neural network, ig‐ nore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecast‐ ing method based on hybrid empirical wavelet transform (EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirec‐ tional long short-term memory (LSTM) method is trained for each sub-layer independently. For better tuning the hyperpa‐ rameters, a Bayesian hyperparameter optimization (BHO) algo‐ rithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed method. From the results, it is found that the proposed method improves the pre‐ diction accuracy compared with other load forecasting models.
Metagenomics Reveal Microbial Effects of Lotus Root–Fish Co-Culture on Nitrogen Cycling in Aquaculture Pond Sediments

Feed input leads to a large amount of nitrogen-containing sediment accumulating in the substrate in the pond culture process, threatening the safety of aquaculture production. Planting lotus roots (Nelumbo nucifera Gaertn.) in ponds can accelerate the removal of bottom nitrogen, while the role of nitrogen cycle-related microorganisms in the removal is still unclear. In this study, eight yellow catfish (Pelteobagrus fulvidraco) culture ponds with the same basic situation were divided into fishponds with planted lotus roots and ponds with only fish farming. Sediment samples were taken from the fishponds with planted lotus roots and the ponds with only fish farming before and after fish farming, marked as FPB, FPA, FOB, and FOA, respectively, and subjected to physicochemical and metagenomic sequencing analyses. The results show that the contents of NH4+, NO2−, TN, TP, and OM were significantly lower (p < 0.05) in FPA than in FOA. The abundance of metabolic pathways for inorganic nitrogen transformation and ammonia assimilation increased considerably after culture compared to the sediments before culture. A total of eight ammonia production pathways and two ammonia utilization pathways were annotated in the sediments of the experimental ponds, with a very high abundance of ammonia assimilation. Acinetobacter and Pseudomonas (34.67%, 18.02%) were the dominant bacteria in the pond sediments before culture, which changed to Thiobacillus (12.16%) after culture. The FPA had significantly higher relative abundances of Thiobacillus denitrificans and Sulfuricella denitrificans, and the FOA had significantly a higher abundance of Microcystis aeruginosa compared to other samples. The massive growth of Microcystis aeruginosa provided two new inorganic nitrogen metabolic pathways and one organic nitrogen metabolic pathway for FOA. The relative abundances of these three microorganisms were negatively correlated with NH4+ content (p < 0.01) and significantly positively correlated with AP, OM content, and pH value. Compared with ponds with only fish farming, lotus root–fish co-culture can significantly reduce the nitrogen content in sediment, increase the abundance of denitrifying bacteria, and inhibit algae growth. Still, it has little effect on the abundance of nitrogen cycle-related enzymes and genes. In summary, it is shown that, although lotus roots promote the growth of denitrifying microorganisms in the sediment, nitrogen removal relies mainly on nutrient uptake by lotus roots.
Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation—in terms of either positioning or tracking—for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.
Modeling morphological affixation with interpretable recurrent networks: sequential rebinding controlled by hierarchical attention

This paper proposes a recurrent neural network model that learns to perform morphological afﬁxation, a fundamental operation of linguistic cognition, and has interpretable relations to descriptions of morphology at the computational and algorithmic levels. The model represents morphological sequences (stems and afﬁxes) with distributed representations that support binding of symbols to ordinal positions and position-based unbinding. Construction of an afﬁxed form is controlled at the implementation level by shifting attention between morphemes and across positions within each morpheme. The model successfully learns patterns of preﬁxation, sufﬁxation, and inﬁxa-tion, unifying these at all levels of description around the theoretical notion of a pivot. Connections of the present proposal to neural coding of ordinal position, and to computational models of serial recall, are noted.
CONDITIONS FOR α-RESOLVABILITY AND AFFINE α-RESOLVABILITY OF INCOMPLETE BLOCK DESIGNS

Necessary conditions, and necessary and sufficient conditions for α-resolvable are derived in terms of the characteristic roots of NN', where N is the inci dence matrix of the design. A general necessary condition for the α-resolva bility of an incomplete block design is also obtained. Furthermore, it is shown that there does not exist an affine α-resolvable m-associate partially balanced incomplete block (PBIB) design of regular type for m≧2. Some applications of those necessary and sufficient conditions are further given.
Communicative Efficiency, Uniform Information Density, and the Rational Speech Act Theory

One major class of approaches to explaining the distribution of linguistic forms is rooted in communicative efficiency. For theories in which an utterance’s communicative efficiency is itself dependent on the distribution of linguistic forms in the language, however, it is less clear how to make distributional predictions that escape circularity. I propose an approach for these cases that involves iterating between speaker and listener in the Rational Speech Act theory. Characteristics of the fixed points of this iterative process constitute the distributional predictions of the theory. Through computer simulation I apply this approach to the well-studied case of predictabilitysensitive optional function word omission for the theory of Uniform Information Density, and show that the approach strongly predicts the empirically observed negative correlation between phrase onset probability and rate of function word use.
Broadening the Dental Hygiene Students’ Perspectives on the Oral Health Professionals: A Text Mining Analysis

Professional identity formation, an important component of education, is influenced by participation, social relationships, and culture in communities of practice. As a preliminary investigation of dental hygienists’ professional identity formation, this study examined changes in the dental hygiene students’ perceptions of oral health professionals over the three years of their undergraduate program. At a Japanese dental hygiene school, 40 students participated in surveys with open-ended questions about professional groups several times during their studies. The text data were analyzed through content analysis with text mining software. The themes that characterized their dental hygienist profession perceptions in their programs each year were identified as: “Supporters at the dental clinic”; “Engagement with interprofessional care” and “Improved problem-solving skills for clinical issues regarding the oral region”; and “Active contribution to general health” and “Recognition of the roles considering relationships” (in the first, second, and third years, respectively). The students acquired professional knowledge and recognized the significance and roles of oral health professionals in practice. They gained more learning experiences in their education, including clinical placements and interprofessional education. This study provides insight into curriculum development for professional identity formation in dental hygiene students.
Creative leaps in musical ecosystems: early warning signals of critical transitions in professional jazz

High-level cognition is often accomplished not by individuals working in isolation, but by distributed, complex cognitive systems. Examples include teams of scientists or collaboratively improvising musicians. These distributed systems can undergo critical transitions, suddenly moving from one stable pattern of activity to another. For instance, in ‘free jazz,’ where musicians improvise without a predetermined plan or a central leader, the performance will often settle into a particular texture or style before transitioning to something entirely new, often quite suddenly. When do these transitions occur? Are they foreseeable? Inspired by suggestions that cognitive systems are, in some sense, a kind of ‘ecosystem,’ we draw on recent work in quantitative ecology that has begun to describe generic early warning signals of impending critical transitions in ecosystems. We apply these techniques to a corpus of audio recordings of professional jazz quartets playing improvised music. We find that the same generic measures that have been used successfully to predict critical transitions in natural ecosystems describe the complex dynamics of improvised musical performance in the lead-up to transitions. By taking seriously the metaphor that cognition occurs in ‘ecosystems,’ we gain new insights into how stable patterns of thought can emerge suddenly in complex cognitive systems.