LLukas22/scidocs huggingface dataset:
Machine Learning : The High-Interest Credit Card of Technical Debt

Machine learning offers a fantastically powerful toolkit f or building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt , we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is hig hlight several machine learning specific risk factors and design patterns to b e avoided or refactored where possible. These include boundary erosion, entanglem ent, hidden feedback loops, undeclared consumers, data dependencies, changes i n the external world, and a variety of system-level anti-patterns. 1 Machine Learning and Complex Systems Real world software engineers are often faced with the chall enge of moving quickly to ship new products or services, which can lead to a dilemma between spe ed of execution and quality of engineering. The concept of technical debtwas first introduced by Ward Cunningham in 1992 as a way to help quantify the cost of such decisions. Like incurri ng fiscal debt, there are often sound strategic reasons to take on technical debt. Not all debt is n ecessarily bad, but technical debt does tend to compound. Deferring the work to pay it off results in i ncreasing costs, system brittleness, and reduced rates of innovation. Traditional methods of paying off technical debt include re factoring, increasing coverage of unit tests, deleting dead code, reducing dependencies, tighten ng APIs, and improving documentation [4]. The goal of these activities is not to add new functionality, but to make it easier to add future improvements, be cheaper to maintain, and reduce the likeli hood of bugs. One of the basic arguments in this paper is that machine learn ing packages have all the basic code complexity issues as normal code, but also have a larger syst em-level complexity that can create hidden debt. Thus, refactoring these libraries, adding bet ter unit tests, and associated activity is time well spent but does not necessarily address debt at a systems level. In this paper, we focus on the system-level interaction betw e n machine learning code and larger systems as an area where hidden technical debt may rapidly accum ulate. At a system-level, a machine learning model may subtly erode abstraction boundaries. It may be tempting to re-use input signals in ways that create unintended tight coupling of otherw ise disjoint systems. Machine learning packages may often be treated as black boxes, resulting in la rge masses of “glue code” or calibration layers that can lock in assumptions. Changes in the exte rnal world may make models or input signals change behavior in unintended ways, ratcheting up m aintenance cost and the burden of any debt. Even monitoring that the system as a whole is operating s intended may be difficult without careful design.
Oral and injectable contraception use and risk of HIV acquisition among women in sub-Saharan Africa.

OBJECTIVE
To evaluate the effect of oral and injectable hormonal contraception on the risk of HIV acquisition among women in South Africa and Zimbabwe.


DESIGN
Secondary data analysis of 4913 sexually active women aged 18-49 years followed for up to 24 months in the Methods for Improving Reproductive Health in Africa (MIRA) phase III effectiveness trial of the diaphragm and lubricant gel for HIV prevention.


METHODS
Participants were interviewed quarterly about contraception and sexual behavior and were tested for pregnancy, HIV, and other sexually transmitted infections. We used a Cox proportional hazards marginal structural model, weighted by the inverse probability of hormonal contraception use, to compare the risk of HIV acquisition among nonpregnant women reporting use of combined oral contraceptive pills (COC), progestin-only pills (POP), and/or injectable hormonal contraception to women not using these methods.


RESULTS
During the study, 283 participants seroconverted. Use of oral contraceptives (POP or COC) was not associated with HIV risk [adjusted hazard ratio (HRa) = 0.86, 95% confidence interval (CI) 0.32, 1.78]. Injectable hormonal contraception was associated with a small nonsignificant risk of HIV infection (HR(a) = 1.34, 95% CI 0.75, 2.37). The effect of injectable hormonal contraception was similar in the unweighted site-adjusted only (HR(a) = 1.32, 95% CI 1.00, 1.74) and baseline factor adjusted models (HR(a) = 1.27, 95% CI 0.94, 1.72).


CONCLUSIONS
In this study, oral contraceptives were not associated with HIV acquisition. There is substantial uncertainty in the effect of injectable hormonal contraception on HIV risk. These findings underscore the importance of dual protection with condoms and the need for diverse contraceptive options for women at risk of HIV infection.
Implications of Transformed Digital Self-Representation on Online and Offline Behavior

Virtual environments allow us to dramatically alter our self-representation. More importantly, studies have shown that people infer their expected behaviors and attitudes from observing their avatar’s appearance, a phenomenon known as the Proteus Effect (Yee & Bailenson, 2007). For example, users given taller avatars negotiated more aggressively than users given shorter avatars. Two studies are reported here that extend our understanding of this effect. The first study extends the work beyond laboratory settings to an actual online community. We found that both the height and attractiveness of an avatar in an online game were significant predictors of the player’s performance. In our second study, we found that the behavioral changes stemming from the virtual environment transferred to subsequent face-to-face interactions. Participants were placed in an immersive virtual environment and were given either shorter or taller avatars. They then interacted with a confederate for about 15 minutes. In addition to causing a behavioral difference within the virtual environment, we found that participants given taller avatars negotiated more aggressively in subsequent face-to-face interactions than participants given shorter avatars. Together, these two studies show that our virtual bodies can change how we interact with others in actual avatar-based online communities as well as in subsequent face-to-face interactions.
of Achievements in Materials and Manufacturing Engineering Quality Improvement Methodologies – PDCA Cycle , RADAR Matrix , DMAIC and DFSS

Purpose: of this paper is to introduce the reader to the characteristics of PDCA tool and Six Sigma (DMAIC, DFSS) techniques and EFQM Excellence Model (RADAR matrix), which are possible to use for the continuous quality improvement of products, processes and services in organizations. Design/methodology/approach: We compared the main characteristics of the presented methodologies aiming to show the main prerequisites, differences, strengths and limits in their application. Findings: Depending on the purpose every organization will have to find a proper way and a combination of methodologies in its implementation process. The PDCA cycle is a well known fundamental concept of continuousimprovement processes, RADAR matrix provides a structured approach assessing the organizational performance, DMAIC is a systematic, and fact based approach providing framework of results-oriented project management, DFSS is a systematic approach to new products or processes design focusing on prevent activities. Research limitations/implications: This paper provides general information and observations on four presented methodologies. Further research could be done towards more detailed study of characteristics and positive effects of these methodologies. Practical implications: The paper presents condensed presentation of main characteristics, strengths and limitations of presented methodologies. Our findings could be used as solid information for management decisions about the introduction of various quality programmes. Originality/value: We compared four methodologies and showed their main characteristics and differences. We showed that some methodologies are more simple and therefore easily to understand and introduce (e.g. PDCA cycle). On the contrary Six Sigma and EFQM Excellence model are more complex and demanding methodologies and therefore need more time and resources for their proper implementation.
A Comprehensive Implementation of Conceptual Spaces

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points and concepts are represented by regions in a (potentially) high-dimensional space. Based on our recent formalization, we present a comprehensive implementation of the conceptual spaces framework that is not only capable of representing concepts with inter-domain correlations, but that also offers a variety of operations on these concepts.
Abstract Syntax and Variable Binding

Syntax and Variable Binding (Extended Abstract) Marcelo Fiore COGS Univ. of Sussex Gordon Plotkin LFCS Univ. of Edinburgh Daniele Turi LFCS Univ. of Edinburgh
Pointer Sentinel Mixture Models

Recent neural network sequence models with softmax classifiers have achieved their best language modeling performance only with very large hidden states and large vocabularies. Even then they struggle to predict rare or unseen words even if the context makes the prediction unambiguous. We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier. Our pointer sentinelLSTM model achieves state of the art language modeling performance on the Penn Treebank (70.9 perplexity) while using far fewer parameters than a standard softmax LSTM. In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus.1
Nephroprotective Effects of Metformin in Diabetic Nephropathy.

Metformin, a well-known anti-diabetic agent, is very effective in lowering blood glucose in patients with type 2 diabetes with minimal side-effects. Metformin is also being recommended in the treatment of obesity and polycystic ovary syndrome. Metformin elicits its therapeutic effects mainly via activation of AMP-activated kinase (AMPK) pathway. Renal cells under hyperglycemic or proteinuric conditions exhibit inactivation of cell defense mechanisms such as AMPK and autophagy, and activation of pathologic pathways such as mammalian target of rapamycin (mTOR), endoplasmic reticulum (ER) stress, epithelial-to-mesenchymal transition (EMT), oxidative stress, and hypoxia. As these pathologic pathways are intertwined with AMPK signaling, the potential benefits of metformin therapy in patients with type 2 diabetes would extend beyond its anti-hyperglycemic effects. However, since metformin is eliminated unchanged through the kidneys and some studies have shown the incidence of lactic acidosis with its use during severe renal dysfunction, the use of metformin was contraindicated in patients with renal disease until recently. With more studies indicating the relatively low incidence of lactic acidosis and revealing the additional benefits with metformin therapy, the US FDA has now approved metformin to be administered in patients with established renal disease based on their renal function. The purpose of this review is to highlight the various mechanisms by which metformin protects renal cells that have lost its functionality in a diabetic or non-diabetic setting and to enlighten the advantages and therapeutic potential of metformin as a nephroprotectant for patients with diabetic nephropathy and other non-diabetic forms of chronic kidney disease. J. Cell. Physiol. 232: 731-742, 2017. © 2016 Wiley Periodicals, Inc.
Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained. Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development. q 2004 Elsevier Ltd. All rights reserved.
Interactive Shadow Play Animation System

The paper describes a Chinese shadow play animation system based on Kinect. Users, without any professional training, can personally manipulate the shadow characters to finish a shadow play performance by their body actions and get a shadow play video through giving the record command to our system if they want. In our system, Kinect is responsible for capturing human movement and voice commands data. Gesture recognition module is used to control the change of the shadow play scenes. After packaging the data from Kinect and the recognition result from gesture recognition module, VRPN transmits them to the server-side. At last, the server-side uses the information to control the motion of shadow characters and video recording. This system not only achieves human-computer interaction, but also realizes the interaction between people. It brings an entertaining experience to users and easy to operate for all ages. Even more important is that the application background of Chinese shadow play embodies the protection of the art of shadow play animation. Keywords—Gesture recognition, Kinect, shadow play animation, VRPN.
Thalidomide impairs insulin action on glucose uptake and glycogen synthesis in patients with type 2 diabetes.

OBJECTIVE
To investigate the effect of thalidomide on glucose turnover (glucose production and uptake), on intracellular pathways of glucose utilization (glycogen synthesis [GS], glycolysis [GLS], carbohydrate oxidation, and nonoxidative GLS), and on free fatty acid (FFA) turnover (lipolysis, FFA oxidation, and FFA reesterification).


RESEARCH DESIGN AND METHODS
A total of 6 patients with type 2 diabetes were studied with 4-h isoglycemic-hyperinsulinemic clamps (approximately 8 mmol/l and 500-600 pmol/l, respectively) before treatment (Prestudy), after 3 weeks of thalidomide (150 mg orally at bedtime), and after 3 weeks of placebo.


RESULTS
Thalidomide reduced insulin-stimulated glucose uptake by 31% (from 27.7 to 19.2 pmol x kg(-1) x min(-1), P < 0.05) compared with the prestudy and by 21% (from 24.2 to 19.2 pmol x kg(-1) x min(-1), P < 0.05) compared with placebo. Thalidomide also reduced insulin-stimulated GS by 48% (from 14.1 to 8.2 micromol x kg(-1) x min(-1), P < 0.05) compared with the prestudy and by 40% (from 13.6 to 8.2 micromol x kg(-1) x min(-1), P < 0.5) compared with placebo. Thalidomide had no effect on rates of GLS, carbohydrate oxidation, nonoxidative GLS, lipolysis, FFA oxidation, and reesterification.


CONCLUSIONS
We conclude that thalidomide increased insulin resistance in obese patients with type 2 diabetes by inhibiting insulin-stimulated GS and that patients taking thalidomide should be monitored for possible deterioration in their glucose tolerance.
A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia (SAED).

BACKGROUND
Diabetes is a chronic disease, with high prevalence across many nations, which is characterized by elevated level of blood glucose and risk of acute and chronic complication. The Kingdom of Saudi Arabia (KSA) has one of the highest levels of diabetes prevalence globally. It is well-known that the treatment of diabetes is complex process and requires both lifestyle change and clear pharmacologic treatment plan. To avoid the complication from diabetes, the effective behavioural change and extensive education and self-management is one of the key approaches to alleviate such complications. However, this process is lengthy and expensive. The recent studies on the user of smart phone technologies for diabetes self-management have proven to be an effective tool in controlling hemoglobin (HbA1c) levels especially in type-2 diabetic (T2D) patients. However, to date no reported study addressed the effectiveness of this approach in the in Saudi patients. This study investigates the impact of using mobile health technologies for the self-management of diabetes in Saudi Arabia.


METHODS
In this study, an intelligent mobile diabetes management system (SAED), tailored for T2D patients in KSA was developed. A pilot study of the SAED system was conducted in Saudi Arabia with 20 diabetic patients for 6 months duration. The patients were randomly categorized into a control group who did not use the SAED system and an intervention group whom used the SAED system for their diabetes management during this period. At the end of the follow-up period, the HbA1c levels in the patients in both groups were measure together with a diabetes knowledge test was also conducted to test the diabetes awareness of the patients.


RESULTS
The results of SAED pilot study showed that the patients in the intervention group were able to significantly decrease their HbA1c levels compared to the control group. The SAED system also enhanced the diabetes awareness amongst the patients in the intervention group during the trial period. These outcomes confirm the global studies on the effectiveness of smart phone technologies in diabetes management. The significance of the study is that this was one of the first such studies conducted on Saudi patients and of their acceptance for such technology in their diabetes self-management treatment plans.


CONCLUSIONS
The pilot study of the SAED system showed that a mobile health technology can significantly improve the HbA1C levels among Saudi diabetic and improve their disease management plans. The SAED system can also be an effective and low-cost solution in improving the quality of life of diabetic patients in the Kingdom considering the high level of prevalence and the increasing economic burden of this disease.
Evaluation of rural stroke services: does implementation of coordinators and pathways improve care in rural hospitals?

BACKGROUND AND PURPOSE
The quality of hospital care for stroke varies, particularly in rural areas. In 2007, funding to improve stroke care became available as part of the Rural Stroke Project (RSP) in New South Wales (Australia). The RSP included the employment of clinical coordinators to establish stroke units or pathways and protocols, and more clinical staff. We aimed to describe the effectiveness of RSP in improving stroke care and patient outcomes.


METHODS
A historical control cohort design was used. Clinical practice and outcomes at 8 hospitals were compared using 2 medical record reviews of 100 consecutive ischemic or intracerebral hemorrhage patients ≥12 months before RSP and 3 to 6 months after RSP was implemented. Descriptive statistics and multivariable analyses of patient outcomes are presented.


RESULTS



SAMPLE
pre-RSP n=750; mean age 74 (SD, 13) years; women 50% and post-RSP n=730; mean age 74 (SD, 13) years; women 46%. Many improvements in stroke care were found after RSP: access to stroke units (pre 0%; post 58%, P<0.001); use of aspirin within 24 hours of ischemic stroke (pre 59%; post 71%, P<0.001); use of care plans (pre 15%; post 63%, P<0.001); and allied health assessments within 48 hours (pre 65%; post 82% P<0.001). After implementation of the RSP, patients directly admitted to an RSP hospital were 89% more likely to be discharged home (adjusted odds ratio, 1.89; 95% confidence interval, 1.34-2.66).


CONCLUSIONS
Investment in clinical coordinators who implemented organizational change, together with increased clinician resources, effectively improved stroke care in rural hospitals, resulting in more patients being discharged home.
Enamel wear caused by monolithic zirconia crowns after 6 months of clinical use.

The purpose of this study was to evaluate enamel wear caused by monolithic zirconia crowns and to compare this with enamel wear caused by contralateral natural antagonists. Twenty monolithic zirconia crowns were placed in 20 patients requiring full molar crowns. For measurement of wear, impressions of both jaws were made at baseline after crown cementation and at 6-month follow-up. Mean and maximum wear of the occlusal contact areas of the crowns, of their natural antagonists and of the two contralateral natural antagonists were measured by the use of plaster replicas and 3D laser scanning methods. Wear differences were investigated by the use of two-sided paired Student's t-tests and by linear regression analysis. Mean vertical loss (maximum vertical loss in parentheses) was 10 (43) μm for the zirconia crowns, 33 (112) μm for the opposing enamel, 10 (58) μm for the contralateral teeth and 10 (46) μm for the contralateral antagonists. Both mean and maximum enamel wear were significantly different between the antagonists of the zirconia crowns and the contralateral antagonists. Gender and activity of the masseter muscle at night (bruxism) were identified as possible confounders which significantly affected wear. Under clinical conditions, monolithic zirconia crowns seem to be associated with more wear of opposed enamel than are natural teeth. With regard to wear behaviour, clinical application of monolithic zirconia crowns is justifiable because the amount of antagonistic enamel wear after 6 months is comparable with, or even lower than, that caused by other ceramic materials in previous studies.