Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Deep Neural Networks Ensemble for Speech Bandwidth Extension

In this paper, we propose a subband-based ensemble of sequential deep neural networks (DNNs) for bandwidth extension (BWE). First, the narrow-band spectra are folded into the high-band (HB) region to generate the high-band spectra, and then the energy levels of the HB spectra are adjusted using the DNN-based on the log-power spectra feature. For this, we basically build the multiple DNNs, which is responsible for each subband of the HB and the DNN ensemble is sequentially connected from lower to higher subbands. This sequential structure for the DNN ensemble carries out the denoising and HB regression to better estimate the HB energy levels. In addition, we use the voiced/unvoiced (V/UV) classification to differently apply the DNN ensemble depending on either V/UV sounds. To demonstrate the performance of the proposed BWE algorithm, we compare it with a speech production model-based BWE system and a DNN-based BWE system in which the log-power spectra in the HB are estimated directly. The experimental results show that the proposed approach provides better speech quality than conventional approaches.
Towards a Better Understanding of Availability and Interruptibility with Mobile Availability Probes

In cooperative work shared awareness on mutual availability is important for the overall performance of the team. There has been great research on quantitatively analysing users’ behaviour and automatically detecting their interruptibility. In this paper we present our approach towards a better qualitative understanding of availability of users. Leveraging on experience sampling and cultural probes we developed a mobile tool to collect Mobile Availability Probes. We motivate the need for a better qualitative understanding of availability, introduce our approach and the Mobile Availability Probes, and present and discuss initially collected availability data.
Process evaluation of pyramidal and cone-shaped nanoparticle deposition (NPD) bumps using a thermally resistant resist

In this paper, an i-line resist with possible high thermal stability is evaluated to ascertain the effect of temperature on the resist profile during the nanoparticle deposition process. Apart from cone-shaped bumps, pyramid bumps were also evaluated in terms of the exposure parameters and the bump profile formed after the NPD process. Using the i-line resist, 5 μm and 7 μm bumps were realized in a single nanoparticle deposition process, with an aspect ratio of 1.2.
Shooting from the heap: ultra-scalable static analysis with heap snapshots

Traditional whole-program static analysis (e.g., a points-to analysis that models the heap) encounters scalability problems for realistic applications. We propose a ``featherweight'' analysis that combines a dynamic snapshot of the heap with otherwise full static analysis of program behavior. The analysis is extremely scalable, offering speedups of well over 3x, with complexity empirically evaluated to grow linearly relative to the number of reachable methods. The analysis is also an excellent tradeoff of precision and recall (relative to different dynamic executions): while it can never fully capture all program behaviors (i.e., it cannot match the near-perfect recall of a full static analysis) it often approaches it closely while achieving much higher (3.5x) precision.
Self-Heating Assessment on Bulk FinFET Devices Through Characterization and Predictive Simulation

This paper describes three different measurement methodologies for the electrical characterization of FinFET self-heating at wafer-level. Finite element simulations of heat transport are used to interpret heater-sensor temperature gradients and validate the measurements. The different sensor types were designed to use the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\text {T}}$ </tex-math></inline-formula>) of an adjacent FET, the forward bias (<inline-formula> <tex-math notation="LaTeX">${V} _{\text {D}}$ </tex-math></inline-formula>) of an adjacent pn-junction or the gate resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{\text {G}}$ </tex-math></inline-formula>) of the device itself. We report that self-heating is underestimated by 35% when sensed at a neighboring device. We also confirm that heat from local and surrounding sources are additive.
Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients

Background Nursing notes have not been widely used in prediction models for clinical outcomes, despite containing rich information. Advances in natural language processing have made it possible to extract information from large scale unstructured data like nursing notes. This study extracted the sentiment—impressions and attitudes—of nurses, and examined how sentiment relates to 30-day mortality and survival. Methods This study applied a sentiment analysis algorithm to nursing notes extracted from MIMIC-III, a public intensive care unit (ICU) database. A multiple logistic regression model was fitted to the data to correlate measured sentiment with 30-day mortality while controlling for gender, type of ICU, and SAPS-II score. The association between measured sentiment and 30-day mortality was further examined in assessing the predictive performance of sentiment score as a feature in a classifier, and in a survival analysis for different levels of measured sentiment. Results Nursing notes from 27,477 ICU patients, with an overall 30-day mortality of 11.02%, were extracted. In the presence of known predictors of 30-day mortality, mean sentiment polarity was a highly significant predictor in a multiple logistic regression model (Adjusted OR = 0.4626, p < 0.001, 95% CI: [0.4244, 0.5041]) and led to improved predictive accuracy (AUROC = 0.8189 versus 0.8092, 95% BCI of difference: [0.0070, 0.0126]). The Kaplan Meier survival curves showed that mean sentiment polarity quartiles are positively correlated with patient survival (log-rank test: p < 0.001). Conclusions This study showed that quantitative measures of unstructured clinical notes, such as sentiment of clinicians, correlate with 30-day mortality and survival, thus can also serve as a predictor of patient outcomes in the ICU. Therefore, further research is warranted to study and make use of the wealth of data that clinical notes have to offer.
The Relationship of Frailty and Hospitalization Among Older People: Evidence From a Meta‐Analysis

OBJECTIVES This research explored the relationship between the stages of frailty and risk for hospitalization in older adults and evaluated the risk for hospitalization among the elderly in relation to various frailty assessment indexes. METHODS A systematic literature review and meta-analysis were carried out. A total of 32,998 older people, 8,666 of whom were hospitalized, were included in this study. Two of the researchers independently collected and reviewed the literature. The key search terms used were "frailty" or "frail," "hospitalization," and "older people" or "older" or "geriatric" or "senior. " Data were recorded from January 2001 to July 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were adopted for quality assessment. A systematic search was carried out using Embase and Scopus to analyze the collected literature. FINDINGS A meta-analysis was conducted on eight studies that discussed the relationship between frailty and hospitalization risk in older adults 65 years of age or older. The results showed that frail older people exhibited the highest risk for hospitalization, following by prefrail and robust older people. Next, different frailty assessment indicators were used to predict the risk for hospitalization among older people. All of these indexes also showed that older persons with frailty had the highest risk for hospitalization, followed by prefrail older people. CONCLUSIONS Frailty is a vital issue in geriatric care and is a crucial factor in the hospitalization of older people. Frail older people were at the highest risk for hospitalization, following by prefrail and robust older people. Assessing frailty as early as possible can reduce the hospitalization risk among older people. CLINICAL RELEVANCE Professional nursing staff should use frailty indicators in a timely fashion to assess the status of frailty in older people and should effectively develop frailty prevention strategies to decrease the risk for hospitalization and to enhance quality of life for older adults.
Individual and couple utility of children

Models specifying the utility of another child as either a couple or an individual experience and incorporating variations in the measurement properties of responses about child utility and expectations are estimated with data from the U.S. Value of Children survey. Full information, maximum likelihood analyses indicate that child utility is an individual experience of wives and husbands rather than a shared couple experience. That is, differences in the measurement properties of wives’ and husbands’ responses do not account entirely for the less than perfect correlations between their responses. These findings demonstrate the importance of estimating errors in measurement in the analysis of wives’ and husbands’ responses about the utility of children, and they challenge the assumption that marital partners share the utility of children.
Prevalence rate of PTSD, depression and anxiety symptoms among Saudi firefighters

Background: Firefighters have a high likelihood of being exposed to a variety of traumatic events. Traumatic events may occur during any rescue, such as providing aid to seriously injured victims or seeing a colleague injured. The psychological cost of this exposure may increase the risk of long-term problems such as PTSD symptoms, anxiety and depression. Moreover, firefighters who are injured in the line of duty sometimes have to retire as a consequence of their injury. Objective: The aim of this study was to investigate the prevalence of traumatic events, PTSD symptoms, anxiety and depression, and assess related variables such as coping strategies and social support among Saudi firefighters. Method: Two hundred Saudi firefighters completed the Firefighters Trauma History Screen (FTHS) to measure the number of traumatic events; the Screen for Post-traumatic Stress Symptoms (SPTSS) scale to assess the prevalence of PTSD symptoms; the Hospital Anxiety and Depression Scale (HADS) to assess depression and anxiety; the Brief COPE (BC) Scale to measure coping strategies used; and the Social Support Scale to evaluate the support that firefighters receive. Results: The results showed that 57% of firefighters fully met DSM-IV criteria for PTSD symptoms; the prevalence of anxiety and depression was 44.4% and 53.3% respectively. PTSD symptoms were significantly correlated with, anxiety, depression and passive coping strategies but not with active coping or social support. Conclusion: These results suggest that firefighters who experience multiple traumatic events as a result of their work environment may develop related symptoms, and they should not be neglected.
Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability

A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.
Integrating Temporal Information Into Knowledge Tracing: A Temporal Difference Approach

Bayesian knowledge tracing (BKT), which is used to diagnose examinees’ knowledge state quantitatively, plays an important role in intelligent tutoring systems. Unfortunately, the temporal difference information of performance data, namely development of cognitive level, is not considered in BKT model, resulting in obvious cognitive diagnosis deviation. To eliminate the defect of BKT model so as to achieve accurate cognitive diagnosis, we proposed a temporal difference Bayesian knowledge tracing model (TD-BKT) to incorporate temporal difference information into knowledge tracing. Our model extracts temporal difference information by detecting cognitive inflection points where examinees’ cognitive level changes a lot, and then integrates those information into refined BKT model to quantify knowledge state accurately. Experiments were done on Junyi academy math practicing log dataset, comparing diagnostic precision of knowledge state between TD-BKT model and existing knowledge tracing models. The result turns out that our proposed TD-BKT model shows great improvement in assessing online examinees’ knowledge state quantitatively.
A Note on N in Bayesian Information Criterion for Item Response Models

This brief report derives the N in the penalty term of the Schwarz’s (1978) Bayesian information criterion (BIC) for two-parameter logistic item response models. The results in this study show that the N is the number of persons for fixed item models, whereas it is the number of observations (the Number of Persons times the Number of Items) for random item models. Given these results, the authors recommend researchers to calculate the BIC or to validate the BIC value that shows in the output of software instead of accepting the output value without a further check of implicit assumptions made for the software.
Performance Impact of Emerging Memory Technologies on Big Data Applications: A Latency-Programmable System Emulation Approach

This paper presents a performance analysis framework for studying emerging memories. The key component of the framework is a memory-latency programmable emulator, which is based on a FPGA-attached server system. The emulator allows users extend read and/or write latency. In addition, we use regression models to enable system performance studies for memory latencies beyond hardware limitations. Finally, we demonstrate Spark application case studies, analyzing the impact of two key characteristics of emerging memories: extended memory access times and enlarged memory capacities. Results show that the benefit of high capacity memory could outweigh the performance loss due to longer memory latency.
Levels of emotional awareness during psychotherapy among gynecologic cancer patients

Abstract Objective Emotional awareness is the ability to recognize, describe, and attend to emotions. A known correlate is emotional processing, the ability to orient to and use inner experiences for information. The goal was to examine emotional awareness during therapy among gynecologic cancer patients, identify baseline predictors, and explore the relationship between in-session emotional awareness and processing. Method Psychotherapy and baseline data from a randomized controlled trial comparing a supportive counseling (SC) intervention and a cognitive-behavioral coping and communication (CCI) intervention were used. The sample was patients with gynecologic cancers randomized to either therapy (N = 246). Emotion episode transcripts from the first, middle, and sixth of seven in-person sessions were coded for emotional awareness using the Program for Open-Ended Scoring and emotional processing using the Experiencing Scale. Descriptive and regression analyses were conducted. Result Participants had moderate in-session emotional awareness. SC participants exhibited higher levels of awareness in the first (p < 0.001) and sixth (p = 0.002) sessions than CCI participants. Awareness was positively correlated with emotional processing in the first and sixth SC sessions (r = 0.25 and 0.24, respectively) and all CCI sessions (r = 0.29–0.31). Baseline negative emotion expression was associated with awareness during the sixth SC session. Baseline cancer-specific distress was associated with awareness during the sixth CCI session. Significance of results SC may facilitate emotional awareness. Greater emotional awareness in therapy may facilitate emotional processing, which is an important component of most psychotherapies. Patients who are psychologically distressed may exhibit more awareness than others. Similarly, greater emotional awareness may signal greater patient distress.
ABA Type Copolymers of Lactide with Poly(ethylene glycol). Kinetic, Mechanistic, and Model Studies

Block copolymers of lactide and poly(ethylene glycol) with various mclar ratios were synthesized. Tin(II) bis(2-ethylhexanoate) has been used as a catalyst. Kinetic measurement and mechanistic studies suggest that the reactivity of the initiator, a hydroxyl group bearing reagent, is an important parameter on the polymerization course. In the case of primary and secondary alcohols, i.e., poly(ethylene glycol) and methyl lactate, it is found that when the initiator concentration exceeds the catalyst concentration, the number of chains formed exceeds the number of catalyst molecules. The chains are propagated through shifts of catalysts from one chain to another. In the case of tertiary alcohols, it appears that the number of chains formed is about the number of catalyst molecules. Therefore, by choosing an appropriate initiator, reaction course and molecular weight as well as molecular weight distribution can be designed. A model based on the assumptions of fast initiation reaction and random propagation reaction is established. Both initiator and catalyst are found to have an influence on the increase of molecular weight and molecular weight distribution.
A ΔΣ DPLL with 1b TDC, 4b DTC and 8-tap FIR filter for low-voltage clock generation/modulation systems

This paper describes a low-complexity digital-intensive in-band noise and out-of-band noise reduction method for ΔΣ fractional-N bang-bang digital PLLs (BB-DPLLs). The use of only a 4b digital-to-time converter (DTC) with auto-tuned delay cells provides a moderate way to mitigate the phase folding effect in the fractional-N BB-DPLL for decent in-band noise performance. An 8-tap hybrid finite-impulse response (FIR) filter is also utilized to suppress the out-of-band noise caused by the ΔΣ modulation in a wideband PLL. A prototype ΔΣ BB-DPLL is implemented in 65nm CMOS. Experimental results show that the proposed methods reduce the in-band phase noise by about 11dB and both the out-of-band noise and the fractional spurs by about l8dB with the maximum linearity requirements of 4b and 3b for the DTC and the FIR filter.
The Ventral Anterior Temporal Lobe has a Necessary Role in Exception Word Reading

Abstract An influential account of reading holds that words with exceptional spelling-to-sound correspondences (e.g., PINT) are read via activation of their lexical-semantic representations, supported by the anterior temporal lobe (ATL). This account has been inconclusive because it is based on neuropsychological evidence, in which lesion-deficit relationships are difficult to localize precisely, and functional neuroimaging data, which is spatially precise but cannot demonstrate whether the ATL activity is necessary for exception word reading. To address these issues, we used a technique with good spatial specificity—repetitive transcranial magnetic stimulation (rTMS)—to demonstrate a necessary role of ATL in exception word reading. Following rTMS to left ventral ATL, healthy Japanese adults made more regularization errors in reading Japanese exception words. We successfully simulated these results in a computational model in which exception word reading was underpinned by semantic activations. The ATL is critically and selectively involved in reading exception words.
Decision support based on weak signals-overcoming the implementation gap of strategic early warning systems

Strategic early warning systems are the company's eyes and ears to the surrounding world. The sensitivity and the perception of weak signals announcing changes in the rules of the game are vital elements of strategic management competencies. It is not only to have eyes and ears, it is also to understand the language of the weak signals. How demanding the development of this competence is, was underestimated by many companies. That lead to an implementation gap. The Fraunhofer IFF developed a concept to overcome this gap with a dualistic approach: increasing the participation on the one hand and the efficiency of the early warning process with adequate methods on the other hand.
Neuroimmunology of Behavioral Comorbidities Associated With Cancer and Cancer Treatments

Behavioral comorbidities (depression, anxiety, fatigue, cognitive disturbances, and neuropathic pain) are prevalent in cancer patients and survivors. These mental and neurological health issues reduce quality-of-life, which is a significant societal concern given the increasing rates of long-term survival after various cancers. Hypothesized causes of behavioral comorbidities with cancer include tumor biology, stress associated with the cancer experience, and cancer treatments. A relatively recent leading mechanism by which these causes contribute to changes in neurobiology that underlie behavior is inflammation. Indeed, both basic and clinical research indicates that peripheral inflammation leads to central inflammation and behavioral changes in other illness contexts. Given the limitations of assessing neuroimmunology in clinical populations, this review primarily synthesizes evidence of neuroimmune and neuroinflammatory changes due to two components of cancer (tumor biology and cancer treatments) that are associated with altered affective-like or cognitive behaviors in rodents. Specifically, alterations in microglia, neuroinflammation, and immune trafficking to the brain are compiled in models of tumors, chemotherapy, and/or radiation. Evidence-based neuronal mechanisms by which these neuroimmune changes may lead to changes in behavior are proposed. Finally, converging evidence in clinical cancer populations is discussed.
On Successive Refinement for the Kaspi/Heegard-Berger Problem

Consider a source, {Xi, Yi, Zi}i=1 infin, producing independent copies of a triplet of jointly distributed random variables (RVs). The {Xi} part of the process is observed at the encoder, and is supposed to be reproduced at two decoders, decoder 1 and decoder 2 , where the {Yi} and the {Zi} parts of the process are observed, respectively, in either a causal or a non-causal manner. The communication between the encoder and the decoders is carried in two successive communication stages. In the first stage, the transmission is available to both decoders and the decoders reconstruct the source according to the received stream and individual side information ({Zi} or {Yi}). In the second stage, additional information is sent to both decoders and the decoders refine the reconstruction of the source according to the side information available to it and the transmissions in both stages. It is desired to find the necessary and sufficient conditions on communication between the encoder and decoders, so that the distortions incurred (at each stage) will not exceed given thresholds. For such a multi-decoder coding setting with successive refinement and non-causal degraded side information, we derive inner and outer bounds to the achievable rate-distortion region. Then, for the case of general causal side information at the decoders, we derive a single-letter characterization of the achievable region for a multi-decoder source-coding problem with successive refinement.