Miracle-dz/UniFAR-FTUs huggingface dataset:
What Students Don't Know Will Hurt Them: A Frank View from the Field on How to Better Prepare Our Clinic and Externship Students

This Article investigates the areas in which law students are under-prepared for the externship and clinical experience as identified by those in the best position to know: their field supervisors. Through surveys and interviews, the authors learned what qualities supervisors hope to see in students, what abilities they want from students, and the level of competence they will accept. The article begins with an examination of the authors' own perceptions of student inexperience and the factual basis, if any, for the concern that law students need to be better prepared for their first field placements and clinics. Next, the authors describe the survey of nearly three dozen judges, law clerks, government agency attorneys and public interest attorneys with experience supervising students from schools across the county. Also discussed are the more detailed accounts offered by several supervisors who presented their opinions at the authors' Externship 3: Learning from Practice conference presentation in March, 2006. The authors analyze the results by type of skill (writing, research, office etiquette, etc.), as well as by type of placement (judicial, government and public interest). The article concludes with a discussion of the tools that the authors have developed to better prepare their students and their plans for future program improvements.
Word-level Lexical Normalisation using Context-Dependent Embeddings

Lexical normalisation (LN) is the process of correcting each word in a dataset to its canonical form so that it may be more easily and more accurately analysed. Most lexical normalisation systems operate at the character-level, while word-level models are seldom used. Recent language models offer solutions to the drawbacks of word-level LN models, yet, to the best of our knowledge, no research has investigated their effectiveness on LN. In this paper we introduce a word-level GRU-based LN model and investigate the effectiveness of recent embedding techniques on word-level LN. Our results show that our GRU-based word-level model produces greater results than character-level models, and outperforms existing deep-learning based LN techniques on Twitter data. We also find that randomly-initialised embeddings are capable of outperforming pre-trained embedding models in certain scenarios. Finally, we release a substantial lexical normalisation dataset to the community.
Dynamics of a model of tumor-immune interaction with time delay and noise

We propose a model of tumor-immune interaction with time delay in immune reaction and noise in tumor cell reproduction. Immune response is modeled as a non-monotonic function of tumor burden, for which the tumor is immunogenic at nascent stage but starts inhibiting immune system as it grows large. Without time delay and noise, this system demonstrates bistability. The effects of response time of the immune system and uncertainty in the tumor innate proliferation rate are studied by including delay and noise in the appropriate model terms. Stability, persistence and extinction of the tumor are analyzed. We find that delay and noise can both induce the transition from low tumor burden equilibrium to high tumor equilibrium. Moreover, our result suggests that the elimination of cancer depends on the basal level of the immune system rather than on its response speed to tumor growth.
Exploring organizational influences on patient symptom management in hospitals

CYNTHIA THORNTON BACON: Exploring organizational influences on patient symptom management in hospitals (Under the direction of Dr. Barbara Mark) Nurses' abilities to effectively manage hospitalized patients' symptoms may be influenced by organizational factors on the nursing units, particularly the size of the nursing unit, work complexity and nurses' participation in decision making. Guided by Structural Contingency Theory (SCT) which assumes that structural forms and context must match in order for effectiveness to be achieved in organizations, this study assumes that achieving effective management of patient symptoms in hospitals depends on identification of nursing unit structures that are best suited to the technological contexts in which nursing units operate. The purpose of this study was to investigate the interrelationships among the nursing unit's context (unit size and work complexity), structure (nurses' participation in decision making) and effectiveness (symptom management). Mixed-effects linear models were used to test the data according to the hypothesized statements. The hypothesized relationship between increased unit size, operationalized as number of beds, and increased work complexity was supported, but the relationship between increased unit size, operationalized as number of nurses, and increased work complexity was not supported. The hypothesized relationship between increased work complexity and increased nurses' participation in decision making was not supported. Work complexity was significantly associated with nurses' participation in decision making but in the opposite iii direction from that which was hypothesized. The hypothesized relationship between increased nurse participation in decision making and patient ratings of symptom management was not supported. Despite the limitations of the study, this research highlights the importance of the effects of unit size on work complexity for nurses that is often not accounted for when determining nurse staffing in hospitals. This study also illustrates the important effects of work complexity on nurses' ability to participate in decision making on nursing units. Nurses' participation in decision making was found to be significantly negatively impacted by increasing work complexity which reinforces the importance of nurse leaders' facilitation of work conditions that support nurses' full participation in decisions on nursing units. Finally, this study provides support for continued research to identify organizational contexts and structures that foster the delivery of hospital care that is consonant with patients’ expectations for symptom management.
What do you mean, BERT? Assessing BERT as a Distributional Semantics Model

Contextualized word embeddings, i.e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models. In this work, we focus on BERT, a deep neural network that produces contextualized embeddings and has set the state-of-the-art in several semantic tasks, and study the semantic coherence of its embedding space. While showing a tendency towards coherence, BERT does not fully live up to the natural expectations for a semantic vector space. In particular, we find that the position of the sentence in which a word occurs, while having no meaning correlates, leaves a noticeable trace on the word embeddings and disturbs similarity relationships.
Monk: Mortgages in a New-Keynesian Model

We propose a tractable framework for monetary policy analysis in which both short - and long-term debt affect equilibrium outcomes. This objective is motivated by observations from two literatures suggesting that monetary policy contains a dimension affecting expected future interest rates and thus the costs of long-term financing. In New-Keynesian models, however, long-term loans are redundant assets. We use the model to address three questions: what are the effects of statement vs. action policy shocks; how important are standard New-Keynesian vs. cash flow effects in their transmission; and what is the interaction between these two effects?
Remarks on the spectrum of a nonlocal Dirichlet problem

In this paper we analyse the spectrum of nonlocal Dirichlet problems with non-singular kernels in bounded open sets. The novelty is the continuity of eigenvalues with respect to domain perturbation via Lebesgue measure. Also, under additional smooth condition on the kernel and domain, we prove differentiability of simple eigenvalues computing their first derivative.
Predicting Unobserved Space for Planning via Depth Map Augmentation

Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robot's environment. On Micro Air Vehicles (MAVs) this is commonly achieved using low-cost sensors, such as stereo or RGB-D cameras. These sensors may fail to provide depth measurements in textureless or IR-absorbing areas and have limited effective range. In path planning, this results in inefficient trajectories or failure to recognize a feasible path to the goal, hence significantly impairing the robot's mobility. Recent advances in deep learning enables us to exploit prior experience about the shape of the world and hence to infer complete depth maps from color images and additional sparse depth measurements. In this work, we present an augmented planning system and investigate the effects of employing state-of-the-art depth completion techniques, specifically trained to augment sparse depth maps originating from RGB-D sensors, semi-dense methods and stereo matchers. We extensively evaluate our approach in online path planning experiments based on simulated data, as well as global path planning experiments based on real world Micro Air Vehicle (MAV) data. We show that our augmented system, provided with only sparse depth perception, can reach on-par performance to ground truth depth input in simulated online planning experiments. On real world MAV data the augmented system demonstrates superior performance compared to a planner based on very dense RGB-D depth maps.
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks

Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is a well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN. Our study is the largest to date and systematically done based on 100 DNN models and 25 metrics. One of our findings is that there is limited correlation between coverage and robustness, i.e., improving coverage does not help improve the robustness. Our dataset and implementation have been made available to serve as a benchmark for future studies on testing DNN.
Partial-Order, Partially-Seen Observations of Fluents or Actions for Plan Recognition as Planning

This work aims to make plan recognition as planning more ready for real-world scenarios by adapting previous compilations to work with partial-order, half-seen observations of both fluents and actions. We first redefine what observations can be and what it means to satisfy each kind. We then provide a compilation from plan recognition problem to classical planning problem, similar to original work by Ramirez and Geffner, but accommodating these more complex observation types. This compilation can be adapted towards other planning-based plan recognition techniques. Lastly we evaluate this method against an "ignore complexity" strategy that uses the original method by Ramirez and Geffner. Our experimental results suggest that, while slower, our method is equally or more accurate than baseline methods; our technique sometimes significantly reduces the size of the solution to the plan recognition problem, i.e, the size of the optimal goal set. We discuss these findings in the context of plan recognition problem difficulty and present an avenue for future work.
Impact of Clinical Pharmacist-Provided Education on Medication Adherence Behaviour in ESRD Patients on Haemodialysis

The aim of this research is to assess the impact of clinical pharmacist-provided education on medication adherence in haemodialysis patients. Ninety haemodialysis patients were randomized into two groups. Brief medication questionnaire (BMQ), a tool for screening adherence was administered to both groups at the baseline to assess adherence to antihypertensives, vitamin D analogues, phosphate binders and folic acid in haemodialysis patients. The intervention group received education regarding their medications by a clinical pharmacist over seven weeks. The control group received standard care. At the end of eight weeks, BMQ was once again administered to patients of both groups. At the baseline, there was no significant difference in the mean BMQ regimen, belief, recall and the medication access screen scores of the control and intervention groups. At the end of the eighth week, there was a significant drop (p<0.05) in the mean scores of regimen, belief and recall screens of BMQ in the intervention group, indicating an improved level of medication adherence. For the control group, there was no significant drop in the mean BMQ screen scores. The reported level of non-adherence was the highest for phosphate binders. We conclude that clinical pharmacist-provided education was effective in improving adherence behaviour of haemodialysis patients.
MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models

Recent state-of-the-art language models utilize a two-phase training procedure comprised of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. More recently, many studies have been focused on trying to improve these models by enhancing the pre-training phase, either via better choice of hyperparameters or by leveraging an improved formulation. However, the pre-training phase is computationally expensive and often done on private datasets. In this work, we present a method that leverages BERT's fine-tuning phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training. Our method allows the model to converge to an optimal number of parallel classifiers, depending on the given dataset at hand. We conduct an extensive inter- and intra-dataset evaluations, showing that our method improves the robustness of BERT, sometimes leading to a +9\% gain in accuracy. These results highlight the importance of a proper fine-tuning procedure, especially for relatively smaller-sized datasets. Our code is attached as supplementary and our models will be made completely public.
A scalable weakly-synchronous algorithm for solving partial differential equations

Synchronization overheads pose a major challenge as applications advance towards extreme scales. In current large-scale algorithms, synchronization as well as data communication delay the parallel computations at each time step in a time-dependent partial differential equation (PDE) solver. This creates a new scaling wall when moving towards exascale. We present a weakly-synchronous algorithm based on novel asynchrony-tolerant (AT) finite-difference schemes that relax synchronization at a mathematical level. We utilize remote memory access programming schemes that have been shown to provide significant speedup on modern supercomputers, to efficiently implement communications suitable for AT schemes, and compare to two-sided communications that are state-of-practice. We present results from simulations of Burgers' equation as a model of multi-scale strongly non-linear dynamical systems. Our algorithm demonstrate excellent scalability of the new AT schemes for large-scale computing, with a speedup of up to $3.3$x in communication time and $2.19$x in total runtime. We expect that such schemes can form the basis for exascale PDE algorithms.
Economic and Mathematical Modelling for Evaluation of Potential Recreational Forest Utilization

The need to identify the potential of recreational forest management as one of the priority development courses of the tourist and recreational industry. Based on studies of domestic and foreign scientists, it was established that the potential of recreational forest management is a four-component phenomenon and includes a resource-based, economic, social and innovation and investment components. Considering this aspect, a method of economic evaluation of the potential of recreational forest management was proposed, based on taxonomic analysis methods for economic and mathematical modelling of integral indicator of the potential level of recreational forest management. This method was tested by the example of the recreation area of the Western Ukrainian region. It was determined that the integral indicator of the level of potential of forest management potential at the investigated sites is low. Its main reserves are to improve its economic, innovation and investment component, but it is necessary to maintain a high level of resource and social component. Measures and ways to increase the level of recreational forestry potential are proposed.
Recent Advances in Algorithmic High-Dimensional Robust Statistics

Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all known efficient unsupervised learning algorithms were very sensitive to outliers in high dimensions. In particular, even for the task of robust mean estimation under natural distributional assumptions, no efficient algorithm was known. Recent work in theoretical computer science gave the first efficient robust estimators for a number of fundamental statistical tasks, including mean and covariance estimation. Since then, there has been a flurry of research activity on algorithmic high-dimensional robust estimation in a range of settings. In this survey article, we introduce the core ideas and algorithmic techniques in the emerging area of algorithmic high-dimensional robust statistics with a focus on robust mean estimation. We also provide an overview of the approaches that have led to computationally efficient robust estimators for a range of broader statistical tasks and discuss new directions and opportunities for future work.
Efficiency of Some Bioagents and Potassium Humate in Controlling Tomato wilt Disease caused by Fusarium Oxysporum f.sp. Lycopersici

Fusarium oxysporum f.sp. lycopersici (FOL) is one of the most important fungi causing vascular wilt disease of tomato plants. Many different agents i.e., Bacillus subtilis, Trichoderma harzianum, T. viride, alga (Sparulina platensis), Mycorrhiza (Glomus sp.) and potassium humate were investigated for their antagonistic effects on the growth of FOL in vitro. T. harzianum and potassium humate were the most effective treatments, followed by B. subtilis and alga extract. The previously mentioned treatments were studied under greenhouse conditions and natural infection in the field. T. harzianum and potassium humate were the most effective treatments against FOL followed by B. subtilis and alga extract. Mycrrohiza was the lowest effect treatment under greenhouse and field conditions. T. harzianum and potassium humate decreased the activities of hydrolyzing enzymes and increased all of total phenols and plant parameters (fresh, dry weight and length/ plant) of treated tomato plants.
Exploring Millennials' Perception of Greening Hotels in Egypt

"Millennials" is a distinctive and dominant consumer group whose behavior, preferences and purchase decisions are broadly explored but not fully understood yet. Making up the largest market segment in the world, and in Egypt, they have the power to reinvent the hospitality industry and contribute to forming prospective demand for green hotels by showing willingness to adopting their environmental-friendly practices. The current study is one of the first contributions towards exploring the impact of Millennials on accepting greening hotels in Egypt. Relations among Millennials environment awareness, their acceptance of green practices and willingness to pay more were hypnotized. The study aims to enhance better understanding Millennials' perception of greening initiatives and to increase the prediction power of their intentions. Millennials' profile, their preferences and environmental decision-making process are brought under light to stimulate actions of hospitality decision-makers and hoteliers. The study questionnaire was composed of four sections and it was distributed to random Egyptian travelers' blogs and facebook groups, with approximately 8000 members. ANOVA test was used examine the study variables. The findings indicated that environmental awareness of Egyptian Millennials will not be a significant factor in their acceptance of hotel green practices, as well as, their willingness to pay more for them. However, Millennials' acceptance of hotel green practices level will have an impact on their willingness to pay more. Millennials were found to have noticeable level of environmental awareness but lack commitment to tolerating hotel green practices and their associated high prices.
Alejandro, Serguei and Valdés, Héctor and Manero, Marie- Hélène and Zaror, Claudio A. Oxidative regeneration of toluene- saturated natural zeolite by gaseous ozone: The influence of zeolite

In this study, the effect of zeolite chemical surface characteristics on the oxidative regeneration of toluene saturated-zeolite samples is investigated. A Chilean natural zeolite (53% clinoptilolite, 40% mordenite and 7% quartz) was chemically modified by acid treatment with hydrochloric acid and by ion-exchange with ammonium sulphate. Thermal pre-treatments at 623 and 823 K were applied and six zeolite samples with different chemical surface characteristics were generated. Chemical modification of natural zeolite followed by thermal out-gassing allows distinguishing the role of acidic surface sites on the regeneration of exhausted zeolites. An increase in Brønsted acid sites on zeolite surface is observed as a result of ammonium-exchange treatment followed by thermal treatment at 623 K, thus increasing the adsorption capacity toward toluene. High ozone consumption could be associated to a high content of Lewis acid sites, since these could decompose ozone into atomic active oxygen species. Then, surface oxidation reactions could take part among adsorbed toluene at Brønsted acid sites and surface atomic oxygen species, reducing the amount of adsorbed toluene after the regenerative oxidation with ozone. Experimental results show that the presence of adsorbed oxidation by-products has a negative impact on the recovery of zeolite adsorption capacity.