Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Lymphocytic Leukemia (CLL) with Borderline Immunoglobulin Heavy Chain Mutational Status, a Rare Subgroup of CLL with Variable Disease Course

Simple Summary In this study, we present a retrospective analysis of a large cohort of CLL patients with a particular focus on those with a borderline immunoglobulin heavy variable chain mutational status (BL-IGHV) and their disease course. BL-IGHV patients represent a relatively unexplored small fraction of CLL patients whose prognosis is still largely debated. Our work aims to provide further evidence regarding BL-IGHV CLL. The key findings of our research highlight differences in time to first treatment (TTFT) and overall survival (OS) between our cohort of borderline mutated patients and previous experiences, but also the relative similarities shared by BL-IGHV patients across the literature Abstract Chronic lymphocytic leukemia (CLL) exhibits substantial variability in disease course. The mutational status of the B-cell receptor immunoglobulin heavy variable (IGHV) chain is a critical prognostic factor, categorizing patients into mutated (M-IGHV) and unmutated (U-IGHV) groups. Recently, a third subgroup with borderline mutational status (BL-IGHV) has been identified, comprising approximately 5% of CLL cases. This study retrospectively analyzes the outcomes of 30 BL-IGHV mutated patients among a cohort of 653 CLL patients, focusing on time to first treatment (TTFT) and overall survival (OS). BL-IGHV patients had a short TTFT similar to U-IGHV patients (median 30.2 vs. 34 months; p = 0.9). Conversely, the OS of BL-IGHV patients resembled M-IGHV patients (median NR vs. 258 months; p = 1). Despite a similar incidence in unfavorable prognostic factors, the TTFT was shorter compared to other published cohorts. However, striking similarities with other experiences suggest that BL-IGHV mutated patients share common biological characteristics, biased IGHV gene usage and BCR subset frequency. These findings also underscore the need for multicentric efforts aggregating data on BL-IGHV CLL in order to elucidate its disease course and optimize therapeutic approaches for this rare subgroup. Until then, predicting outcomes and optimal management of BL-IGHV CLL will remain challenging.
Sharing analysis of arrays, collections, and recursive structures

Precise modeling of the program heap is fundamental for understanding the behavior of a program, and is thus of significant interest for many optimization applications. One of the fundamental properties of the heap that can be used in a range of optimization techniques is the sharing relationships between the elements in an array or collection. If an analysis can determine that the memory locations pointed to by different entries of an array (or collection) are disjoint, then in many cases loops that traverse the array can be vectorized or transformed into a thread-parallel version. This paper introduces several novel sharing properties over the concrete heap and corresponding abstractions to represent them. In conjunction with an existing shape analysis technique, these abstractions allow us to precisely resolve the sharing relations in a wide range of heap structures (arrays, collections, recursive data structures, composite heap structures) in a computationally efficient manner. The effectiveness of the approach is evaluated on a set of challenge problems from the JOlden and SPECjvm98 suites. Sharing information obtained from the analysis is used to achieve substantial thread-level parallel speedups.
A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.
Can smart devices assist in geometric model building?

The creation of precise three dimensional geometric models produced from visual information from low cost smart devices has many potential applications. There is a need to establish the capability of such devices, to determine if they are suitable. We explain how a typical smart device could be used in the creation of precise 3D geometric models from visual information, and establish a benchmark for evaluating such devices via a simulator which allows for parametric exploration of device capabilities. Various vision based algorithms (e.g. SLAM) could benefit from auxiliary sensor inputs, e.g. to solve the problem of determining the scale of the object being sensed. We discuss how the ancillary sensors in smart devices can assist in 3D mensuration to underpin 3D scene generation and 3D model building. We describe a number of tests performed on the inertial sensors integrated into consumer smart devices to ascertain their performance characteristics. These findings are then incorporated in our simulations. We also explore the extent to which future improvements to technologies are likely to be beneficial. Our simulator allows modelling of a 3D scene, various sensors and the motion trajectory of the device whilst capturing data. We assess individual sensor outputs, and the quality of the fused sensor outputs in the context of 3D model building requirements.
Significance analysis by minimizing false discovery rate

False discovery rate (FDR) control is widely practiced to correct for multiple comparisons in selecting statistically significant features from genome-wide datasets. In this paper, we present an advanced significance analysis method called miFDR that minimizes FDR when the number of the required significant features is fixed. We compared our approach with other well-known significance analysis approaches such as Significance Analysis of Microarrays [1-3], the Benjamini-Hochberg approach [4] and the Storey approach [5]. The results of using both simulated data sets and public microarray data sets demonstrated that miFDR is more powerful.
Experiences of hypoglycaemia unawareness amongst people with Type 1 diabetes: A qualitative investigation

Objectives To explore the experiences of people who have hypoglycaemia unawareness and its impact on their everyday lives. Methods In-depth interviews with 38 people with Type 1 diabetes who have hypoglycaemia unawareness. Data analysis used an inductive, thematic approach. Results Participants reported imposed and self-imposed changes to their lives following onset of hypoglycaemia unawareness including: leaving employment, curtailing pastimes and spending more time at home or being supervised by others. However, some reported getting on with life by downplaying the significance and impact of their condition, which could put their health and safety at risk. Many relied on frequent self-monitoring of blood glucose and/or prompting from others to detect hypoglycaemia. Some expressed concerns about becoming a burden on family and/or responding in irrational and aggressive ways to others’ suggestions to test for and treat hypoglycaemia. Participants reported responding best to composed and directive prompts from family. Health professionals mainly advised on clinical aspects, and did not enquire about the emotional and psychosocial impact of hypoglycaemia unawareness. Discussion Hypoglycaemia unawareness can have a profound impact on people’s confidence, careers and personal relationships. Healthcare professionals should pay more attention during consultations to the emotional and social aspects of living with hypoglycaemia unawareness.
Detection of Runtime Conflicts among Services in Smart Cities

The populations of large cities around the world are growing rapidly. Cities are beginning to address this problem by implementing significant sensing and actuation infrastructure and building services on this infrastructure. However, as the density of sensing and actuation increases and as the complexities of services grow there is an increasing potential for conflicts across Smart City services. These conflicts can cause unsafe situations and disrupt the benefits that the services were originally intended to provide. Although some of the conflicts can be detected and avoided during designing the services, many can still occur unpredictably during runtime. This paper carefully defines and enumerates the main issues regarding the detection and resolution of runtime conflicts in smart cities. In particular, it focuses on conflicts that arise across services. This issue is becoming more and more important as Smart City designs attempt to integrate services from different domains (transportation, energy, public safety, emergency, medical, and many others). Research challenges are identified and then addressed that deal with uncertainty, dynamism, real-time, mobility and spatio-temporal availability, duration and scale of effect, efficiency, and ownership. A watchdog architecture is also described that oversees the services operating in a Smart City. This watchdog solution detects and resolves conflicts, it learns and adapts, and it provides additional inputs to decision making aspects of services. Using data from a Smart City dataset, an emulated set of services and activities using those services are created to perform a conflict analysis. A second analysis hypothesizes 41 future services across 5 domains. Both of these evaluations demonstrate the high probability of conflicts in smart cities of the future.
Case study: long-term potentiation of neuroleptics with transdermal nicotine in Tourette's syndrome.

Sixteen Tourette's syndrome patients, aged 9 to 15 years, whose symptoms were not controlled with neuroleptics, were followed for various lengths of time after the application of a transdermal nicotine patch (TNP) (7 mg/24 hours) as part of an ongoing case study. While there was a broad range in individual response, application on the TNP produced significant reductions (p < .001) in Yale Global Tic Severity Scale scores relative to baseline, with an average duration of effect lasting between 1 and 2 weeks. Side effects, for the most part, were transient. Clinical implications for the use of TNP as an adjunct to neuroleptic treatment of Tourette's syndrome are discussed.
Deadzone compensation in motion control systems using neural networks

A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN): one to estimate the unknown deadzone, and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented form containing extra neurons whose activation functions provide a 'jump function basis set' for approximating piecewise continuous functions. Closed-loop stability analysis for the deadzone compensator is provided, and yield tuning algorithms for the weights of the two NN. The technique provides a general procedure for using NN to determine the pre-inverse of an unknown right-invertible function.
Task-Oriented Kindergarten Behavior Pays Off in Later Childhood

Objective: Research has traditionally neglected child-learning skills as important when entering kindergarten. In this article, we consider a novel dimension of school readiness by examining prospective associations between early classroom engagement skills, reflecting self-regulation and the ability to remain on task, and later academic adjustment in emerging adolescence. Methods: Kindergarten teachers rated classroom engagement skills of 960 children from the Québec Longitudinal Study of Child Development. Outcomes measured at 10-years-old children include a direct assessment of achievement in mathematics and fourth-grade teacher ratings of academic achievement, teacher-child conflict, inattention, victimization, proactive and indirect aggression, and antisocial behavior in the classroom. Results: Multiple regression analyses revealed that kindergarten classroom engagement skills were associated with better fourth-grade math test scores and teacher-rated academic success. Early classroom engagement also predicted less teacher-child conflict, inattention, victimization by peers, proactive and indirect aggression, and antisocial behavior in fourth grade. Conclusion: Easily measurable, context-based assessments of task orientation and focus represent robust components of children’s readiness to learn at school entry.
Imagining Ordinary Citizens? Conceptualized and Actual Participants for Deliberations on Emerging Technologies

In this paper, we explore conceptualizations of ‘ordinary’ citizens common in public engagement forums on emerging technologies and assumptions from deliberative theory that ordinary people are more likely to be appropriately ‘changed’ through deliberative processes facilitated by experts. Looking at a large US public forum event [the National Citizens Technology Forum (NCTF)], we asked: What were the goals for this exercise and how did they shape conceptualizations of ordinariness and representativeness? Whose goals and conceptualizations were they? Were the engaged citizens ordinary and representative—and were they changed by the exercise? Our exploration revealed that exercise organizers conceived of ordinary citizens as people lacking science and technology backgrounds, without advocacy or business connections to the technologies at hand, and demographically reflecting the US population. Exercise materials also implied that ideal ordinary participants would lack strong opinions and emotions about these technologies. Actual NCTF participants, however, tended to be more educated, have higher incomes, and to be more liberal than the US public, and participants from all backgrounds had a range of relevant knowledge, experiences and opinions about science and technology. They were changed by the exercise in complex and conflicting ways—based as much on their own knowledge and reflections on relational dynamics as on exercise processes, interactions with experts, and information provided in the exercise. We argue that inadequately explored ideas about ordinary citizens are highly problematic. Further, invisible assumptions about what is ‘normal’ among experts and status quo institutions serve to reify the lay–expert divide that engagement exercises are intended to counteract.
The Use of Relationship Maintenance Behaviors as a Mechanism to Explain the Decline in Marital Satisfaction Among Parents

This study hypothesized that parents would use less maintenance behavior than nonparents, and that parents with children present in the home would use less maintenance behavior than either nonparents or parents whose children are not living at home. The alternative hypothesis was that relationship length affects maintenance use. Survey data were collected from 177 married individuals. Results indicate significant differences in both self-reported maintenance and the perception of the partner's use of maintenance, with nonparents reporting more self and partner maintenance efforts than individuals with children living at home. However, these differences were not significant when relationship length was controlled.
The Determinants of Spouses' Normative Preferences for Family Roles

This paper examines both the determinants of spouses' normative preferences for family roles and determinants of changes in these preferences over time. Both a static and a dynamic model are developed which hypothesize effects of background characteristics, socioeconomic factors and family life-cycle stages on three dimensions of normative preferences. The most significant background characteristics are found to be ethnic origin and education. The earnings of wives are shown to be related to less traditional preferences of both spouses. Moreover, the results indicate that spouses make rapid mutual adjustments, implying that each spouse is a strong socializing agent for the other. The most striking result is the failure of the husband's preferences to dominate the wife's, a finding which contradicts generalizations regarding husbands' presumed dominance in family role relations.
Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks sub-optimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy and group cohesion, in 44 groups (n=204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multi-dimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with greater than 70% cross-validation accuracy—significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.
Reinforcement learning with recurrent neural networks

Controlling a high-dimensional dynamical system with continuous state and action spaces in a partially unknown environment like a gas turbine is a challenging problem. So far often hard coded rules based on experts' knowledge and experience are used. Machine learning techniques, which comprise the field of reinforcement learning, are generally only applied to sub-problems. A reason for this is that most standard reinforcement learning approaches still fail to produce satisfactory results in those complex environments. Besides, they are rarely data-efficient, a fact which is crucial for most real-world applications, where the available amount of data is limited. In this thesis recurrent neural reinforcement learning approaches to identify and control dynamical systems in discrete time are presented. They form a novel connection between recurrent neural networks (RNN) and reinforcement learning (RL) techniques. Thereby, instead of focusing on algorithms, neural network architectures are put in the foreground. RNN are used as they allow for the identification of dynamical systems in form of high-dimensional, non-linear state space models. Also, they have shown to be very data-efficient. In addition, a proof is given for their universal approximation capability of open dynamical systems. Moreover, it is pointed out that they are, in contrast to an often cited statement, well able to capture long-term dependencies. As a first step towards reinforcement learning, it is shown that RNN can well map and reconstruct (partially observable) Markov decision processes. In doing so, the resulting inner state of the network can be used as a basis for standard RL algorithms. This so-called hybrid RNN approach is rather simple but showed good results for a couple of applications. The further developed recurrent control neural network combines system identification and determination of an optimal policy in one network. It does not only learn from data but also integrates prior knowledge into the modelling in form of architectural concepts. Furthermore, in contrast to most RL methods, it determines the optimal policy directly without making use of a value function. This distinguishes the approach also from other works on reinforcement learning with recurrent networks. The methods are tested on several standard benchmark problems. In addition, they are applied to different kinds of gas turbine simulations of industrial scale. III Acknowledgement
Nested Quantization Index Modulation for Reversible Watermarking and Its Application to Healthcare Information Management Systems

Digital watermarking has attracted lots of researches to healthcare information management systems for access control, patients' data protection, and information retrieval. The well-known quantization index modulation-(QIM-) based watermarking has its limitations as the host image will be destroyed; however, the recovery of medical images is essential to avoid misdiagnosis. In this paper, we propose the nested QIM-based watermarking, which is preferable to the QIM-based watermarking for the medical image applications. As the host image can be exactly reconstructed by the nested QIM-based watermarking. The capacity of the embedded watermark can be increased by taking advantage of the proposed nest structure. The algorithm and mathematical model of the nested QIM-based watermarking including forward and inverse model is presented. Due to algorithms and architectures of forward and inverse nested QIM, the concurrent programs and special processors for the nested QIM-based watermarking are easily implemented.
A tool for the visualisation of public opinion

The rise in popularity of the web and social media has significantly changed the way voters communicate and form their opinions. National governments are also affected by the hype of social media, so they launch new debate tools and open social platforms where citizens are able to communicate, collaborate and exchange opinions. When the amount of opinions increases, then it becomes difficult to process and interpret them manually. In this case, opinion–mining techniques and information visualisation tools can be employed to depict the public opinion and give comprehensive visual summaries. In this work, we present an information visualisation tool for surveys, which allows users to select from a variety of graphs, drill down to selected periods or roll–up to a larger scale and supports input from both closed–end and open–end questions. In the latter case, the tool employs opinion–mining techniques to quantify voter's opinion.