Miracle-dz/UniFAR-FTUs huggingface dataset:
Intraprofessional Collaboration in Learning Evidence-Based Practice

Little is known about how collaborative curricular activities can help students learn about scientific evidence in occupational therapy (OT) and occupational therapy assistant (OTA) programs. We created and measured collaborative learning in evidence-based practice activities to enhance partnership building and intraprofessional collaboration among students in OT and OTA programs. Thirty-three OT students and 26 OTA students enrolled in a didactic course (Phase I) and an intraprofessional collaborative learning activity (Phase II) participated in this quasi-experimental repeated measure study. The students’ ability of how to read scholarly articles and their perceived importance and perceived ability to engage in intraprofessional collaboration were examined at three time points. Improvements were found in the OT students’ perceived importance of intraprofessional roles/responsibilities and the OTA students’ perceived ability to engage in intraprofessional communication after completing the Phase I didactic course. Both the OT and OTA students’ perceived ability to work as a team, identify their roles/ responsibilities, communicate with peers, and their ability to read scholarly articles improved after the Phase II intraprofessional collaboration. Students valued the opportunity to gain collaboration experience and share different perspectives. They indicated they would have preferred to spend more time on the activities. The findings suggest that targeted learning activities can improve OT and OTA students’ ability to engage in evidence-based practice and their perceived importance and ability to engage in intraprofessional collaboration. Effective partnerships and intraprofessional collaboration are best introduced within academic programs.
Impact of herbicide mixtures on earth worm population, organic carbon content and β glucosidase enzyme activity in soil

Earthworm population, soil organic carbon content and β glucosidase enzyme activity in the soil are considered as the indicators of soil quality and biomarkers for toxicity and bioaccumulation assessment. Hence, field experiments were conducted during first crop season (Kharif 2014) and second crop season (Rabi 2014-15) to study the impact of premix herbicide mixtures bispyribac-sodium + metamifop and penoxsulam + cyhalofop-butyl on earth worm population , soil organic carbon content and β glucosidase enzyme activity in soil. The experiments were conducted in randomized block design with 12 treatments and three replications at Upaniyoor Padashekaram in Thiruvananthapuram district of Kerala, India. The treatments were bispyribac-sodium + metamifop at 60, 70, 80 and 90 g/ha, penoxsulam + cyhalofop-butyl at 120, 125, 130 and 135 g/ha, bispyribac-sodium applied alone 25 g/ha, penoxsulam applied alone at 22.5 g/ha, hand weeding twice and weedy check. All the herbicide treated plots registered earthworm population at par to weedy check and hand weeding. The organic carbon content in soil was higher in herbicide treated plots as compared to weedy check at all the stages of crop growth. Herbicide treated plots were comparable to non-herbicide treated plots with respect to glucosidase activity. It was concluded that the pre-mix bispyriibac-sodium + metamifop and penoxsulam + cyhalofop are environmentally safe.
CCNLab: A Benchmarking Framework for Computational Cognitive Neuroscience

CCNLab is a benchmark for evaluating computational cognitive neuroscience models on empirical data. As a starting point, its focus is classical conditioning, which studies how animals predict reward and punishment in the environment. CCNLab includes a collection of simulations of seminal experiments expressed under a common API, as wells as tools for visualizing and comparing simulated data with empirical data. CCNLab is broad, incorporating representative experiments from different categories of phenomena; ﬂexible, allowing the straightforward addition of new experiments; and easy-to-use, so researchers can focus on developing better models. We envision CCNLab as a testbed for unifying computational theories of learning in the brain. We also hope that it can broadly accelerate neuroscience research and facilitate interaction between the ﬁelds of neuroscience, psychology, and artiﬁcial intelligence.
Revocable Attribute-Based Encryption for Multi-Keyword Search in Clouds

—With the rapid advancement of cloud computing, users upload their ﬁles to the cloud server so that any user can access it remotely. To assure the data security, the data owner, typically, encrypts the data before outsourcing them to the cloud server. In addition, an encryption mechanism needs to enable the consumers to perform efﬁcient searches of such encrypted data in the cloud storages through keywords, i.e. searchable encryption. However, most of searchable encryption is improper due to several limitations, such as the requirement of an on-line fully trusted third party, poor efﬁciency, high-overhead in user revocation, support of a single keyword search, etc. To mitigate such limitations, an attribute-based encryption scheme with ﬁne-grained multi-keyword search is proposed. The new scheme supports the user revocation. In addition, the length of the ciphertext as well as the secret key do not grow linearly under the inﬂuence of the size of attribute set. The performance of the proposed scheme is better as compared to other related schemes. Hence, one can easily adopt the proposed scheme for the real life applications due to its ﬂexibility in terms of its features, security and efﬁciency.
SYNTHESIS AND CHARACTERIZATION OF POLY(SIMVASTATIN) - INCORPORATED COPOLYMERS AND BLENDS FOR BONE REGENERATION

OF DISSERTATION SYNTHESIS AND CHARACTERIZATION OF POLY(SIMVASTATIN) INCORPORATED COPOLYMERS AND BLENDS FOR BONE REGENERATION Common biodegradable polyesters such as poly(lactic acid) (PLA), poly(lacticco-glycolic acid) (PLGA) and poly(ε-caprolactone) (PCL) are used as drug delivery vehicles for tissue regenerative applications. However, they are typically bioinert, with drug loading limitations. Polymerizing the active agent or precursor into its respective biodegradable polymer would control drug loading via molar ratios of drug to initiator used for synthesis. Simvastatin was chosen due to its favorable anti-inflammatory, angiogenic, and osteogenic properties. In addition, its lactone ring lends itself to ringopening polymerization and, consequently, the synthesis of poly(simvastatin) with controlled simvastatin release. Simvastatin was first polymerized with a 5kDa methyl-terminated poly(ethylene glycol) (mPEG) initiator and catalyzed via stannous octoate to form poly(simvastatin)block-poly(ethylene glycol). Molecular weights ranged from 9.5kDa, with a polydispersity index (PDI) of 1.1 at 150 °C, to 75kDa with a PDI of 6.9 at 250 °C. Firstorder propagation rates were seen. Infrared spectroscopy showed carboxylic and methyl ether stretches unique to simvastatin and mPEG in the copolymer, respectively. Slow degradation was seen in neutral and alkaline conditions, with simvastatin, simvastatinincorporated macromolecules, and mPEG identified as degradation products. Alternatively, triazabicyclodecene (TBD) was used to mediate simvastatin polymerization. A lower temperature of 150°C led to successful polymerization using 5kDa mPEG, compared to at least 200 °C via stannous octoate. TBD was also successful for reactions using 2 or 0.55kDa mPEG. The biodegradability of poly(simvastatin)block-poly(ethylene glycol) via TBD improved, losing twice more mass in phosphatebuffered saline, pH 7.4, than the copolymer synthesized via stannous octoate. Release rates of three different copolymers synthesized demonstrated tunable simvastatin release. To further modulate degradation, poly(simvastatin)-block-poly(ethylene glycol) was blended with 5, 2, or 0.55kDa mPEG-initiated PLA copolymers. The blends showed a compressive elastic modulus ranging from 26 to 44MPa, within the magnitude of trabecular bone (approximately 50MPa). Tunability in mass loss and release was also seen due to varied ratios of incorporated PLA copolymers. Lastly, copolymer degradation byproducts inhibited HMG-CoA reductase and showed possible enhancement of osteoblastic activity in vitro. A pilot study using a rodent calvarial onlay model showed tolerability of the polymers and potential for longterm evaluations of bioactivity. Poly(simvastatin) may be useful in regenerative applications.
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). The data is collected via searching the Internet for appropriately licensed audio data with existing transcriptions. We describe our data collection methodology and release our data collection system under the Apache 2.0 license. We show that a model trained on this dataset achieves a 9.98% word error rate on Librispeech's test-clean test set.Finally, we discuss the legal and ethical issues surrounding the creation of a sizable machine learning corpora and plans for continued maintenance of the project under MLCommons's sponsorship.
Self-Improving Semantic Perception for Indoor Localisation

We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
Implementing Direct Democracy Via Representation

Nowadays, several social movements asking for direct participation of citizens in the decision-making process are emerging in western democracies. These groups argue that traditional representative systems fail to adequately represent the will of the majority (often defined as “people” by populist movements) and support direct democracy as the only political system to restore the will of the majority. In this paper we consider a setting where several decisions about independent issues have to be made, and analyze under what conditions the two systems coincide; that is, the policy implemented by the winner of an electoral competition coincides with the one that citizens would choose by means of direct democracy. We find necessary and sufficient conditions for this equivalence to hold, implying that, as long as at least one of them is not fulfilled, the equivalence between direct democracy and representative democracy ceases to exist and disaffection in representative democracy would arise. The theoretical predictions of the model state, that the more divided the electorate over the proposals to be carried out for the bundle of issues and the less polarized are the politicians, the more likely it is that the conditions fail to be satisfied. We illustrate how the failure of our conditions leads to reasons for the emergence of mistrust in systems of representative democracy. Deeply divided societies, the activity of lobbies and special-interest groups, and the failure of electoral competition stand as responsible.
The Effect of Relaxing Local Housing Market Regulations on the Number of Recipients of Federal Rental Housing Assistance Programs

The majority of U.S. households that qualify for federal rental housing assistance do not receive it. In the absence of an entitlement to housing assistance for all who qualify, an underexplored cause of the shortfall is that higher rents in some areas driven by supply-constraining local regulations drive up program costs, leaving fewer funds available to serve additional families. In this paper, we first show that because the federal government bears the full cost of any incremental increase in market rents in assisted housing units, stringent local regulations can substantially increase the federal cost of covering a fixed number of families. We then simulate the effect of deregulation that reduces home prices to the cost of producing a home in 11 substantially supply-constrained metropolitan areas, which we translate into reduced market rents and ultimately cost savings for federal rental housing assistance programs. We estimate that post-deregulation, the Housing Choice Voucher program and Section 8 Project-based assistance program could save a combined $3.9 billion, which could serve 13 percent (424,000) more families.
Tesla: Is Now the Time to Invest? An examination of Tesla, social media, and its effect on stock

Tesla is an American electric vehicle and clean energy company. They are based in Palo Alto, California and their product base consists of electric cars, battery energy storage, solar panels, and solar roof tiles. On an average day in 2021, Tesla stock sells for $700/share. We will review historical Tesla data and examine whether this particular stock is worth the investment? In addition to historical data, this research reviews the effect of traditional news and social media on human behavior. Exploring social media analytics, investor sentiment and behavior in hopes to gauge how these factors can impact Tesla and whether this should be taken into consideration prior to the investment.
A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation

LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an existing semantic segmentation network to extract semantic information and a traditional LiDAR point cloud cluster algorithm to split each instance object. We argue geometry-based traditional clustering algorithms are worth being considered by showing a state-of-the-art performance among all published end-to-end deep learning solutions on the panoptic segmentation leaderboard of the SemanticKITTI dataset. To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms. Therefore, instead of working on new models, we give a comprehensive technical survey in this paper by implementing four typical cluster methods and report their performances on the benchmark. Those four cluster methods are the most representative ones with real-time running speed. They are implemented with C++ in this paper and then wrapped as a python function for seamless integration with the existing deep learning frameworks. We release our code for peer researchers who might be interested in this problem 1.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.
Constraints on compact binary merger evolution from spin-orbit misalignment in gravitational-wave observations

The identification of the first confirmed neutron star - black hole (NS-BH) binary mergers by the LIGO, Virgo and KAGRA collaboration provides the opportunity to investigate the properties of the early sample of confirmed and candidate events. Here, we focus primarily on the tilt angle of the black hole's spin relative to the orbital angular momentum vector of the binary, and the implications for the physical processes that determine this tilt. The posterior tilt distributions of GW200115 and the candidate events GW190426_152155 and GW190917_114630 peak at significantly anti-aligned orientations (though display wide distributions). Producing these tilts through isolated binary evolution would require stronger natal kicks than are typically considered (and preferentially-polar kicks would be ruled out), and/or an additional source of tilt such as stable mass transfer. The early sample of NS-BH events are less massive than expected for classical formation channels, and may provide evidence for efficient mass transfer that results in the merger of more massive NS-BH binaries before their evolution to the compact phase is complete. We predict that future gravitational-wave detections of NS-BH events will continue to display total binary masses of $\approx 7$ M$_{\odot}$ and mass ratios of $q \sim 3$ if this interpretation is correct. Conversely, the high mass of the candidate GW191219_163120 suggests a dynamical capture origin. Large tilts in a significant fraction of merging NS-BH systems would weaken the prospects for electromagnetic detection. However, EM observations, including non-detections, can significantly tighten the constraints on spin and mass ratio.
Dynamic Orthogonal Matching Pursuit for Signal Reconstruction

—Orthogonal matching pursuit (OMP) is one of the mainstream algorithms for signal reconstruction/approximation. It plays a vital role in the development of compressed sensing theory, and it also acts as a driving force for the development of other heuristic methods for signal reconstruction. In this paper, we propose the so-called dynamic orthogonal matching pursuit (DOMP) and its two enhanced versions which are more efﬁcient than OMP in signal reconstruction from a numerical point of view, and we provide a rigorous analysis for these algorithms in terms of the restricted isometry property (RIP) of the sensing matrix. The main result claims that the reconstruction error via the proposed algorithms can be controlled and measured with the number of iterations, sparsity level of the signal and the noise level of the measurements. The analysis in this paper sufﬁciently exploits the structure of the DOMP and an auxiliary convex optimization model (called approximation counterpart) to establish a mild RIP-based performance result for DOMP and its enhanced versions. Dramatically different from existing results for OMP-like algorithms, the results established for the algorithms proposed in this paper do not need any traditional assumptions imposed on the smallest nonzero element of the target signal which is unknown before being actually reconstructed. Moveover, the ﬁnite convergence of the algorithms for a class of large-scale compressed sensing problems is also discussed.
Simulation study of Open Charm production in proton-proton collisions at $\sqrt{s}$ = 13 TeV using Pythia 8

Charm and beauty are heavy quarks with observed masses of 1.28 GeV/c and 4.18 GeV/c respectively. They are produced in initial hard scattering processes. Due to their small formation time (∆t ∼ 0.1fm/c) as compared to the formation time of QGP (∆t ∼ 0.3fm/c) at the LHC, they experience all the stages occuring during the time evolution of the hot and dense medium produced in heavy-ion collisions. Therefore, the measurement of open charm and beauty production allows probing QGP properties and investigating the color charge and mass dependence of the parton inmedium energy loss. Moreover, due to their large masses (mc,mb ΛQCD ) their pp production cross-sections are calculable within the domain of perturbative QCD constituting an excellent test of pQCD calculations. The aim of this study is to understand the processes involved in the production of charm quarks through the productions of D, D, Ds and Λ + c hadrons. Further to investigate the possibility of hadronization of the charm quarks, ratios like Λc /D , D s /D , Λc /D 0 and D s /D 0 are also measured. For the current analysis, the events are generated by using PYTHIA 8 for pp collisions at √ s = 13 TeV. PYTHIA 8 has proved to be quite successful in explaining the heavy-flavor particle production at the LHC energies.
Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.
FRUGAL: Unlocking Semi-Supervised Learning for Software Analytics

Standard software analytics often involves having a large amount of data with labels in order to commission models with acceptable performance. However, prior work has shown that such requirements can be expensive, taking several weeks to label thousands of commits, and not always available when traversing new research problems and domains. Unsupervised Learning is a promising direction to learn hidden patterns within unlabelled data, which has only been extensively studied in defect prediction. Nevertheless, unsupervised learning can be ineffective by itself and has not been explored in other domains (e.g., static analysis and issue close time). Motivated by this literature gap and technical limitations, we present FRUGAL, a tuned semi-supervised method that builds on a simple optimization scheme that does not require sophisticated (e.g., deep learners) and expensive (e.g., 100% manually labelled data) methods. FRUGAL optimizes the unsupervised learner’s configurations (via a simple grid search) while validating our design decision of labelling just 2.5% of the data before prediction.As shown by the experiments of this paper FRUGAL outperforms the state-of-the-art adoptable static code warning recognizer and issue closed time predictor, while reducing the cost of labelling by a factor of 40 (from 100% to 2.5%). Hence we assert that FRUGAL can save considerable effort in data labelling especially in validating prior work or researching new problems.Based on this work, we suggest that proponents of complex and expensive methods should always baseline such methods against simpler and cheaper alternatives. For instance, a semi-supervised learner like FRUGAL can serve as a baseline to the state-of-theart software analytics.