Miracle-dz/UniFAR-FTUs huggingface dataset:
Productive Frictions: Moving from Digital to Material Prototyping and Low-Volume Production for Design Research

In this paper we discuss the low-volume production of an interaction design research product known as the tilting bowl. The form of the tilting bowl was designed with 3D modeling tools and utilized digital fabrication for rapid prototyping. The final form was produced in a small number of glazed ceramic forms with embedded electronics and actuators. We focus on the lessons we learned from the challenges and design opportunities that arose in moving from digital processes to ceramic processes. We reflected on these lessons and developed thematic notions we refer to as frictions. These include shifting constraints, naïve expertise, manual automation, and dynamic materiality. The contributions of this paper are new design insights into the combination of digital and material processes for studio based prototyping and low-volume production and adds to the emerging relevance of digital fabrication, physical fabrication, and physical materials to interaction design and HCI research.
Infectious disease modelling and causal inference

When applying survival analysis, such as Cox regression, to data from major clinical trials or other studies, often only baseline covariates are used. This is typically the case even if updated covariates are available throughout the observation period, which leaves large amounts of information unused. The main reason for this is that such time-dependent covariates often are internal to the disease process, as they are influenced by treatment, and therefore lead to confounded estimates of the treatment effect. There are, however, methods to exploit such covariate information in a useful way. In this paper we study the method of dynamic path analysis applied to data from the Swiss HIV Cohort Study. To adjust for time-dependent confounding between treatment and the outcome ‘AIDS or death’, the analysis is done on a sequence of mimicked randomized trials constructed from the original cohort data. To analyse these trials together, regular dynamic path
Information sharing, scheduling, and awareness in community gardening collaboration

Community gardens are places where people, as a collaborative group, grow food for themselves and for others. There is a lack of studies in HCI regarding collaboration in community gardens and considering technologies to support such collaborations. This paper reports on a detailed study of collaboration in community gardens in Greater Vancouver, Canada. The goal of our study is to uncover the unique nature of such collaborative acts. As one might expect, we found considerable differences between community gardening collaboration and workplace collaboration. The contribution is the articulation of key considerations for designing technologies for community gardening collaboration. These include design considerations like volunteerism, competences and inclusion, synchronicity, and telepresence as unique aspects of community collaboration in community garden. We also articulate the complexities of community gardening collaboration, which raise issues like control, shared language, and collective ownership that exist more as conditions within which to design than "problems" to solve through technologies.
Image-based food portion size estimation using a smartphone without a fiducial marker

Abstract Objective Current approaches to food volume estimation require the person to carry a fiducial marker (e.g. a checkerboard card), to be placed next to the food before taking a picture. This procedure is inconvenient and post-processing of the food picture is time-consuming and sometimes inaccurate. These problems keep people from using the smartphone for self-administered dietary assessment. The current bioengineering study presents a novel smartphone-based imaging approach to table-side estimation of food volume which overcomes current limitations. Design We present a new method for food volume estimation without a fiducial marker. Our mathematical model indicates that, using a special picture-taking strategy, the smartphone-based imaging system can be calibrated adequately if the physical length of the smartphone and the output of the motion sensor within the device are known. We also present and test a new virtual reality method for food volume estimation using the International Food Unit™ and a training process for error control. Results Our pilot study, with sixty-nine participants and fifteen foods, indicates that the fiducial-marker-free approach is valid and that the training improves estimation accuracy significantly (P<0·05) for all but one food (egg, P>0·05). Conclusions Elimination of a fiducial marker and application of virtual reality, the International Food Unit™ and an automated training allowed quick food volume estimation and control of the estimation error. The estimated volume could be used to search a nutrient database and determine energy and nutrients in the diet.
Antiaging Treatment of the Facial Skin by Fat Graft and Adipose-Derived Stem Cells

999 In recent years, there has been an in creased interest in use of the regenerative properties of adipose tissue grafts.1–3 In this regard, some studies have suggested the rejuvenating properties of fat grafting in the skin.4,5 By this approach, studies in animal models have described an improvement in the texture of the skin and, in particular, the increased elasticity, normalization of excess in the pigmentation, and aesthetic improvement of all scars, regardless of the provoking cause.6 For this reason, fat grafting is now widely used in plastic surgery, mostly in the treatment of the aging face and hands.7,8 In the absence of standardized procedures, multiple surgical procedures and techniques are used in fat grafting, considering that adipose tissue can be purified by gravity or by different means of centrifugation.9,10 Fat has also been used in combination with autologous adiposederived mesenchymal stem cells or with enriched autologous plasma (platelet-rich plasma). To date, it is widely known that some surgeons are grafting only the stromal vascular fraction of the adipose tissue, without the mature adipocytic component.11–13 Other approaches are based exclusively on the injection of adipose-derived mesenchymal stem cells after in vitro expansion.14
CS168: The Modern Algorithmic Toolbox Lecture #2: Approximate Heavy Hitters and the Count-Min Sketch

1.1 Finding the Majority Element Let’s begin with a problem that many of you have seen before. It’s a common question in technical interviews. You’re given as input an array A of length n, with the promise that it has a majority element | a value that is repeated in strictly more than n=2 of the array’s entries. Your task is to nd the majority element. In algorithm design, the usual \holy grail" is a linear-time algorithm. For this problem, your post-CS161 toolbox already contains a subroutine that gives a linear-time solution | just compute the median of A. (Note: it must be the majority element.) So let’s be more ambitious: can we compute the majority element with a single left-to-right pass through the array? If you haven’t seen it before, here’s the solution: Initialize counter := 0, current := NULL. [current stores the frontrunner for the majority element] For i = 1 to n: { If counter == 0:
The Escherichia coli Small Protein MntS and Exporter MntP Optimize the Intracellular Concentration of Manganese

Escherichia coli does not routinely import manganese, but it will do so when iron is unavailable, so that manganese can substitute for iron as an enzyme cofactor. When intracellular manganese levels are low, the cell induces the MntH manganese importer plus MntS, a small protein of unknown function; when manganese levels are high, the cell induces the MntP manganese exporter and reduces expression of MntH and MntS. The role of MntS has not been clear. Previous work showed that forced MntS synthesis under manganese-rich conditions caused bacteriostasis. Here we find that when manganese is scarce, MntS helps manganese to activate a variety of enzymes. Its overproduction under manganese-rich conditions caused manganese to accumulate to very high levels inside the cell; simultaneously, iron levels dropped precipitously, apparently because manganese-bound Fur blocked the production of iron importers. Under these conditions, heme synthesis stopped, ultimately depleting cytochrome oxidase activity and causing the failure of aerobic metabolism. Protoporphyrin IX accumulated, indicating that the combination of excess manganese and iron deficiency had stalled ferrochelatase. The same chain of events occurred when mutants lacking MntP, the manganese exporter, were exposed to manganese. Genetic analysis suggested the possibility that MntS exerts this effect by inhibiting MntP. We discuss a model wherein during transitions between low- and high-manganese environments E. coli uses MntP to compensate for MntH overactivity, and MntS to compensate for MntP overactivity.
Happily Ever after: The Use of Stories to Promote Positive Interventions

People turn to myriad ways to achieve happiness, such as physical pleasures, relationships, or the achievement of goals. Success in these endeavors varies, however, and may not be sustainable. Recent advances in scientific research may be able to help, with a number of studies suggesting that people have the power to increase happiness through intentional activities. Narrative is one of the most pervasive and promising elements of positive interventions, and stories play a significant role both in psychological research and in application. A proposal is made that stories should be used more frequently as a vehicle to demonstrate and encourage the use of positive interventions, and a suggestion is made on how to collect and disseminate them.
Iterative learning cascaded multiclass kernel based support vector machine for neural spike data classification

In this paper, we develop an iterative learning framework based on multiclass kernel support vector machine (SVM) for adaptive classification of neural spikes. For efficient algorithm execution, we transform a multiclass problem with the Kesler's construction and extend iterative greedy optimization reduced set vectors approach with a cascaded method. Since obtained classification function is highly parallelizable, the problem is sub-divided and parallel units are instantiated for the processing of each sub-problem via energy-scalable kernels. After partition of the data into disjoint subsets, we optimize the data separately with multiple SVMs. We construct cascades of such (partial) approximations and use them to obtain the modified objective function, which offers high accuracy, has small kernel matrices and low computational complexity.
Disability and treatment of specific mental and physical disorders across the world

Background Advocates of expanded mental health treatment assert that mental disorders are as disabling as physical disorders, but little evidence supports this assertion. Aims To establish the disability and treatment of specific mental and physical disorders in high-income and low- and middle-income countries. Method Community epidemiological surveys were administered in 15 countries through the World Health Organization World Mental Health (WMH) Survey Initiative. Results Respondents in both high-income and low- and middle-income countries attributed higher disability to mental disorders than to the commonly occurring physical disorders included in the surveys. This pattern held for all disorders and also for treated disorders. Disaggregation showed that the higher disability of mental than physical disorders was limited to disability in social and personal role functioning, whereas disability in productive role functioning was generally comparable for mental and physical disorders. Conclusions Despite often higher disability, mental disorders are under-treated compared with physical disorders in both high-income and in low- and middle-income countries.
On the Fly TCP Acceleration with Miniproxy

TCP proxies are basic building blocks for many advanced middleboxes. In this paper we present Miniproxy, a TCP proxy built on top of a specialized minimalistic cloud operating system. Miniproxy's connection handling performance is comparable to that of full-fledged GNU/Linux TCP proxy implementations, but its minimalistic footprint enables new use cases. Specifically, Miniproxy requires as little as 6 MB to run and boots in tens of milliseconds, enabling massive consolidation, on-the-fly instantiation and edge cloud computing scenarios. We demonstrate the benefits of Miniproxy by implementing and evaluating a TCP acceleration use case.
UTS-CMU at THUMOS 2015

This notebook paper describes our solution from UTSCMU team in the THUMOS 2015 action recognition challenge. Our system contains two major components, video representation generated by VLAD encoding from ConvNet features and multi-skip improved Dense Trajectories. In addition, we explore optical flow ConvNet and acoustic features such as MFCC and ASR in our system. We demonstrate that our complete system can achieve state-of-the-art performance in large-scale action recognition tasks.
Understanding natural language sentences with word embedding and multi-modal interaction

Understanding and grounding human commands with natural languages have been a fundamental requirement for service robotic applications. Although there have been several attempts toward this goal, the bottleneck still exists to store and process the corpora of natural language in an interaction system. Currently, the neural- and statistical-based (N&S) natural language processing have shown potential to solve this problem. With the availability of large data-sets nowadays, these processing methods are able to extract semantic relationships while parsing a corpus of natural language (NL) text without much human design, compared with the rule-based language processing methods. In this paper, we show that how two N&S based word embedding methods, called Word2vec and GloVe, can be used in natural language understanding as pre-training tools in a multi-modal environment. Together with two different multiple time-scale recurrent neural models, they form hybrid neural language understanding models for a robot manipulation experiment.
Respiratory frequency estimation from accelerometric signals acquired by mobile phone in a controlled breathing protocol

The aim of this work was to test if the smartphone's embedded triaxial accelerometer can be used to extract respiratory frequency information from the chest movements during a controlled breathing protocol. Respiratory signals from 10 young volunteers were recorded simultaneously, by two smartphones (iPhone 4s and 6s; sampling frequency ∼100 Hz), positioned one on the sternum and one on the belly, while in supine posture. At the same time, a belt transducer was used to acquire the reference respiratory signal. A controlled breathing protocol, consisting of four consecutive phases of 12 respiratory cycles each (respiratory frequencies at 0.25, 0.17, 0.125 and 0.1 Hz), was imposed through the visualization of a moving bar on a display. After low-pass filtering (fc=0.5 Hz), the respiratory signal was obtained from both smartphones, and respiratory frequency derived for each phase. Compared to the belt transducer, the resulting error was lower than 2% for each imposed respiratory frequency, for both smartphones' positions, with better results obtained for the smartphone positioned above the belly.
Open-loop worst-case identi " cation of nonSchur plants

This paper presents an LMI based algorithm for deterministic worst-case identi"cation of nonSchur plants in an open-loop setting. Contrary to other approaches dealing with this problem, the proposed technique does not require prior knowledge of a stabilizing controller. The main result of the paper shows that, as the information is completed, the identi"ed model converges, in the ‘2-induced topology, to the actual plant. Additional results include upper bounds on the worst-case identi"cation error on the "nite horizon. The usefulness of the proposed approach is illustrated with a practical example arising in the context of robust visual tracking. 2003 Elsevier Science Ltd. All rights reserved.
Sound Identification for Fire-Fighting Mobile Robots

A structure engulfed in flames can pose an extreme danger for fire-fighting personnel as well as any people trapped inside. A companion robot to assist the fire-fighters could potentially help speed up the search for humans while reducing risk for the fire-fighters. However, robots operating in these environments need to be able to operate in very low visibility conditions because of the heavy smoke, debris and unstructured terrain. This paper develops an audio classification algorithm to identify sounds relevant to fire-fighting such as people in distress (baby cries, screams, coughs), structural failure (wood snapping, glass breaking), fire, fire trucks, and crowds. The outputs of the classifier are then used as alerts for the fire-fighter or to modify the configuration of a robot capable of navigating unstructured terrain. The approach used extracts an array of features from audio recordings and employs a single hidden layer, feed forward neural network for classification. The simplicity in network structure enables performance on limited hardware and obtains classification results with an overall accuracy of 85.7%.
The Diffusion of Crisis-Related Communication on Social Media: An Empirical Analysis of Facebook Reactions

During a crisis, authorities need to effectively disseminate information. We address the problem of deciding how crisis-related information should be published on Facebook to reach as many people as possible. We examine three recent terrorist attacks in Berlin, London and Stockholm. Our specific focus lies with official Facebook pages by municipalities and emergency service agencies. We collected posts about the events, along with the number of shares, likes and emotional reactions to them. In a regression analysis, several variables were examined that capture decisions on which information to publish and how. Posts containing condolences were found to result in three times as many emotional reactions as other posts, all other variables held constant. Images and videos positively affected the number of reactions by factors of 2.2 and 3.9, respectively, while text length negatively affected the number of shares. These results will help in the development of effective guidelines.
Comparisons of mortality and pre-discharge respiratory morbidities in small for gestational age and appropriate-for gestational age premature infants - An Indian Experience

Background: There is an assumption that fetus with restricted growth with an inappropriate intrauterine environment lies under stress. Although small-for-gestational-age (SGA) infants have higher mortality, difference in the outcome of SGA and appropriate-for-gestational-age (AGA) infants regarding respiratory morbidity is controversial. It seems that respiratory morbidities in SGA neonates is different from neonates with AGA. In this study, we intend to compare the mortality and respiratory morbidity rates between the preterm small for gestational age (SGA) and appropriate for age (AGA) neonates of less than 34 weeks of gestation. Methods: This analytical cross-sectional study was conducted on 498 preterm neonates with gestational age of < 34 weeks, admitted to the Neonatal Intensive Care Unit. These neonates were categorized into two groups of SGA (n=210) and AGA (n=286). The data analysis was performed, using Student’s t-test and Mann-Whitney U test for parametric variables and Chi-square and Fisher’s exact tests for nonparametric data. Results: According to the results of the study, the two groups were significantly different in terms of their birth weight (P<0.001), pregnancy-induced hypertension (P<0.001), and antenatal steroid usage (P=0.011). Furthermore, respiratory distress syndrome (RDS) was found to be more prevalent in the premature AGA neonates than the SGA ones (P=0.011). In addition, surfactant usage was significantly less in the SGA group (P=0.0006). Bronchopulmonary dysplasia (BPD) developed in 14% and 9% of the premature AGA and SGA neonates, respectively (P=0.094). However, there was no significant difference between the two groups regarding the mortality rate, intra-ventricular hemorrhage, and necrotizing enterocolitis. Among the survived neonates, mean length of hospital stay was significantly higher in the premature SGA newborns born within 26-36 weeks of gestation than their AGA counterparts. Conclusion: As the findings of the current study demonstrated, the mortality rate was similar in the SGA and AGA groups; however, the respiratory morbidities such as RDS and BPD were more prevalent in the AGA neonates.
Collaborative Querying Using a Hybrid Content and Results-Based Approach

Collaborative querying is a technique which makes use of past users’ search experiences in order to help the current user formulate an appropriate query. In this technique, related queries are extracted from query logs and clustered. Queries from these clusters that are related to the user’s query are then recommended to the user. This work uses a combination of query terms as well as result documents returned by queries for clustering queries. For the latter, it extracts features such as titles, URLs and snippets from the result documents. It also proposes an extended K-means clustering algorithm for clustering queries over a simple measure of overlap. Experimental results reveal that the best clusters are obtained by using a combination of these sources rather than using only query terms or only result URLs alone.
Distinguishing the Clinical Nurse Specialist From Other Graduate Nursing Roles

Purpose: Today’s healthcare environment poses diverse and complex patient care challenges and requires a highly qualified and experienced nursing workforce. To mitigate these challenges are graduate nursing roles, each with a different set of competencies and expertise. With the availability of many different graduate nursing roles, both patients and healthcare professionals can be confused in understanding the benefit of each role. To gain the maximum benefit from each role, it is important that healthcare providers and administrators are able to distinguish the uniqueness of each role to best use the role and develop strategies for effective collaboration and interprofessional interaction. The purpose of this article was to define the role, educational preparation, role differences, and practice competencies for the clinical nurse specialist (CNS), nurse practitioner, clinical nurse leader, and nurse educator/staff development educator roles. A second purpose was to provide role clarity and demonstrate the unique value the CNS brings to the healthcare environment. Description: Using evidence and reviewing role competencies established by varying organizations, each role is presented with similarities and differences among the roles discussed. In addition, collaboration among the identified roles was reviewed, and recommendations were provided for the new and practicing CNSs. Outcomes: Although there are some similarities among the graduate nursing roles such as in educational, licensing, and certification requirements, each role must be understood to gain the full role scope and benefit and glean the anticipated outcomes. Conclusions: Healthcare providers must be aware of the differences in graduate nursing roles, especially in comparing the CNS with other roles to avoid confusion that may lead to roles being underused with a limited job scope. The CNS provides a unique set of services at all system outcome levels and is an essential part of the healthcare team especially in the acute care setting.