Miracle-dz/UniFAR-FTUs huggingface dataset:
Sugar-Sweetened Beverage Attitudes and Consumption During the First 1000 Days of Life

Objectives To examine the relationship of parental sugar-sweetened beverage (SSB) attitudes with SSB consumption during the first 1000 days of life-gestation to age 2 years. Methods We studied 394 Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)-enrolled families during the first 1000 days of life in northern Manhattan, New York, in 2017. In regression models, we assessed cross-sectional relationships of parental SSB attitude scores with habitual daily parent SSB calories and infant SSB consumption, adjusting for demographic and socioeconomic characteristics. Results Each point higher parental SSB attitude score was associated with lower parental SSB consumption (-14.5 median kcals; 95% confidence interval [CI] = −22.6, −6.4). For infants, higher parental SSB attitude score was linked with lower odds of infant SSB consumption (adjusted odds ratio [AOR] = 0.84; 95% CI = 0.71, 0.99), and adjustment for socioeconomic factors slightly attenuated results (AOR = 0.85; 95% CI = 0.71, 1.02). Conclusions During the first 1000 days of life, greater negativity in parental attitudes toward SSB consumption was associated with fewer parental calories consumed from SSBs and lower likelihood of infant SSB consumption. Public Health Implications Parental attitudes toward SSBs should be targeted in future childhood obesity interventions during pregnancy and infancy.
Feedforward Phase Noise Cancellation Exploiting a Sub-Sampling Phase Detector

This brief presents a feedforward phase noise cancellation technique to reduce phase noise of the output clock signal of a phase-locked loop (PLL). It uses a sub-sampling phase detector to measure the phase noise and a variable time delay for cancellation. Both phase noise and spurs are reduced. Analytical expressions have been derived that characterize the performance of this technique and show its fundamental limitations. A sub-sampling PLL with the cancellation technique as a built-in feature is described. The feedforward technique has no stability requirements in contrast to conventional PLL architectures. The phase noise reduction bandwidth is increased to almost a third of the reference frequency—3x the maximal bandwidth for 3rd order type-II PLLs. The proposed analytical model shows a phase noise reduction of 9 dB at a frequency offset of $ {{f_{\text {ref}}/10}}$ . The total rms jitter is improved by 7.2 dB. The analytical results are verified by simulations.
Spectral Estimation Methods for Evaluating iPPG Pulse Rate Variability

Non-contact measurement of physiological parameters, like pulse rate variability (PRV), has numerous applications in medicine and affective computing. PRV is an informative measure of autonomic nervous system activity. Spectral estimation from unevenly sampled, non-stationary data is integral to pulse rate variability frequency-domain analysis. We present the first comparison of results of PRV computation using the Lomb-Scargle method and Bayesian Spectral Estimation. The Lomb-Scargle method performs well, even in the presence of missing beats. However, the Bayesian Spectral Estimation method has advantages when tracking changes in amplitude and frequency. We illustrate these characteristics with results from synthetic data and real non-contact imaging photoplethysmography measurements.
Using Deep Learning to Count Garbage Bags

The information of daily garbage diposal can be used to develop many appealing applications in smart cities. This poster introduces DeepCounter, an automotive sensing system to providing a finegrained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city Japan.
Social-Behavioral Aware Optimization of Energy Consumption in Smart Homes

Residential energy consumption is skyrocketing, as residential customers in the U.S. alone used 1.4 trillion kilowatt-hours in 2014 and the consumption is expected to increase in the next years. Previous efforts to limit such consumption have included demand response and smart residential environments. However, recent research has shown that such approaches can actually increase the overall energy consumption due to the numerous complex human psychological processes that take place when interacting with electrical appliances. In this paper we propose a social-behavioral aware framework for energy management in smart residential environments. We envision a smart home where appliances are interconnected using the paradigm of the Internet of Things and where users have a maximum energy budget, for example to reduce their energy bills. Using an experimental and interdisciplinary approach, we define social behavioral models to understand how users perceive different appliances, and how the use of some appliances are contingent on the use of others. We make use of large scale online surveys involving 1500 users to gather data and quantify such models. Based on these models we define a social behavioral aware user utility that is adopted as the objective function of a Mixed Integer Linear Programming problem. The problem looks for a set of appliances that maximizes the user utility while ensuring that the energy budget constraint is met. We show that the problem is NP-Hard and provide a heuristic method to efficiently find a solution. Results on synthetic and real data show that our approach outperforms previously proposed solutions that do not consider the social-behavioral implications, and it requires few iterations to converge towards a final solution.
A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging

Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient’s progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal.Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.
Encephalization and longevity evolved in a correlated fashion in Euarchontoglires but not in other mammals

Across mammals, encephalization and longevity show a strong correlation. It is not clear, however, whether these traits evolved in a correlated fashion within mammalian orders, or when they do, whether one trait drives changes in the other. Here, we compared independent and correlated evolutionary models to identify instances of correlated evolution within six mammalian orders. In cases of correlated evolution, we subsequently examined transition patterns between small/large relative brain size and short/long lifespan. In four mammalian orders, these traits evolved independently. This may reflect constraints related to energy allocation, predation avoidance tactics, and reproductive strategies. Within both primates and rodents, and their parent clade Euarchontoglires, we found evidence for correlated evolution. In primates, transition patterns suggest relatively larger brains likely facilitated the evolution of long lifespans. Because larger brains prolong development and reduce fertility rates, they may be compensated for with longer lifespans. Furthermore, encephalization may enable cognitively‐complex strategies that reduce extrinsic mortality. Rodents show an inverse pattern of correlated evolution, whereby long lifespans appear to have facilitated the evolution of relatively larger brains. This may be because longer lived organisms have more to gain from investment in encephalization. Together, our results provide evidence for the correlated evolution of encephalization and longevity, but only in some mammalian orders.
Dynamic Models for the Formal Verification of Big Data Applications Via Stochastic Model Checking

Big Data Applications (BDAs) manage so much data to require a cluster of machines for computation and storage. Their execution often has temporal constraints, such as deadlines to process the data. BDAs are executed within Big Data Frameworks (BDFs), that provide mechanisms to automatically manage the complexity of the computation distribution. For a BDA to fulfill its deadline when executed in a BDF, online dynamic resource allocation policies should be in place. The introduction of control for such resource allocation calls for formal verification of the closed-loop system. Model checkers verify the correct behaviour of programs, and in principle they could be used to prove properties on the BDF execution. However, the complexity of BDFs makes it infeasible to directly model the BDAs and BDFs. We propose a formalism to associate the execution of a BDA with afirst-principle dynamic simulation model that can be used for model checking in the place of the real application, making the verification viable in practice. We introduce our formalism, apply it to a well assessed framework, and test its capabilities. We show that our solution is able to capture the dynamics and prove properties of the BDA execution using a stochastic model checker.
Time Savings and Related Economic Benefits of Suction-Curette Device for Transforaminal Lumbar Interbody Fusion Discectomy

ABSTRACT Background: Performing an adequate transforaminal lumbar interbody fusion (TLIF) discectomy requires numerous instrument passes, increasing surgical time and the risk of complications. The purpose of this study was to evaluate the efficacy and efficiency of discectomy and endplate preparation during TLIF using traditional manual instrumentation versus a novel suction discectomy curette. The direct economic benefit with use of the suction discectomy curette is calculated. Methods: Three experienced, spine-fellowship-trained surgeons performed TLIF discectomies on 3 cadaveric specimens from T12 to S1 using either traditional manual discectomy instruments or CONCORDE Clear (Xtool) devices supplemented with manual discectomy instruments. For each level in which a discectomy was performed, the following were measured: elapsed time, number of instrument passes and the number of instrument exchanges, and estimated tissue volume. Results: Transforaminal lumbar interbody fusion discectomy times improved on average 11:32 minutes per level, which equates to an estimated procedural time savings of 15:85 minutes, using 1.4 levels per TLIF, the average number of levels in a large series. Usage of the CONCORDE Clear significantly reduced instrument passes compared to traditional, with a mean of 62.0 for traditional versus 7.1 for CONCORDE Clear, an 8.7-fold improvement. Instrument exchanges showed a 5.9-fold improvement, with means of 26.8 and 4.6, respectively. Wet discectomy tissue volume was measured for each discectomy, with a mean of 5.4 cc for traditional versus 12.9 cc for CONCORDE Clear, a 2.4-fold improvement. Conclusions: This study estimates that, in a typical TLIF procedure, over 15 minutes should be saved by using the CONCORDE Clear l device (a quarter of the time of a traditional discectomy), and by considering the direct cost-benefit associated with this time savings as well as reduced sterilization costs, it is estimated that a hospital could save approximately $1300 in operating room time and sterilization cost with the use of the CONCORDE Clear device in a typical 1-level TLIF procedure.
Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn? ”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.
Transient and sustained control mechanisms supporting novel instructed behavior

The success of humans in novel environments is partially supported by our ability to implement new task procedures via instructions. This complex skill has been associated with the activity of control-related brain areas. Current models link fronto-parietal and a cingulo-opercular networks with transient and sustained modes of cognitive control, based on observations during repetitive task settings or rest (Dosenbach et al. 2008). The current study extends this dual model to novel instructed tasks. We employed a mixed design and an instruction-following task to extract phasic and tonic brain signals associated with the encoding and implementation of novel verbal rules. We also performed a representation similarity analysis to capture consistency in task-set encoding within trial epochs. Our findings show that both networks are involved while following novel instructions: transiently, during the implementation of the instruction, and in a sustained fashion, across novel trials blocks. Moreover, the multivariate results showed that task representations in the cingulo-opercular network were more stable than in the fronto-parietal one. Our data extend the dual model of cognitive control to novel demanding situations, highlighting the high flexibility of control-related regions in adopting different temporal profiles.
Design and assessment of stimulation parameters for a novel peripheral nerve interface

Bi-directional interfaces for peripheral nerve stimulation and recording aim to improve control and acceptance of sensorized prosthetic limbs. The implantable multimodal peripheral recording and stimulation system (IMPRESS) is an intraneural interface technology supporting a high-density transverse intrafascicular multichannel electrode (hd-TIME). Herein we report on in vivo selectivity studies using a passive hd-TIME, and computational modeling towards optimal stimulation parameters for fiber recruitment.
NeuroImage: Clinical

The clinical presentation of Alzheimer's disease (AD) is not unitary as heterogeneity exists in the disease's clinical and anatomical characteristics. MRI studies have revealed that heterogeneous gray matter atrophy patterns are associated with specific traits of cognitive decline. Although white matter (WM) impairment also contributes to AD pathology, its heterogeneity remains unclear. The Latent Dirichlet Allocation (LDA) method is a suitable framework to study heterogeneity and allows to identify latent impairment factors of AD instead of simply mapping an overall disease effect. By exploring whole brain WM skeleton images by using LDA, three latent factors were revealed in AD: a temporal-frontal impairment factor (temporal and frontal lobes, especially hippocampus and para-hippocampus), a parietal factor (parietal lobe, especially precuneus), and a long fibre bundle factor (corpus callosum and superior longitudinal fasciculus). As revealed by longitudinal analysis, the latent factors have distinct impact on cognitive decline: for executive function (EF), the temporal-frontal factor was more strongly associated with baseline EF compared with the parietal factor, while the long-fibre bundle factor was most associated with decline rate of EF; for memory, the three factors showed almost equal effect on the baseline memory and decline rate. For each participant, LDA estimates his/her composition profile of latent impairment factors, which indicates disease subtype. We also found that the APOE genotype affects the AD subtype. Specifically, APOE ε 4 was more associated with the long fibre bundle factor and APOE ε 2 was more associated with temporal-frontal factor. By investigating heterogeneity and subtypes of AD through white matter impairment factors, our study could facilitate precision medicine.
A Distributed Descriptor Characterizing Structural Irregularity of EEG Time Series for Epileptic Seizure Detection

This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.
A Hybrid Hierarchical Framework for Free Weight Exercise Recognition and Intensity Measurement with Accelerometer and ECG Data Fusion

Accurate recognition and effective monitoring of physical activities (PA) in daily life is a goal of many healthcare fields. Existing PA recognition approaches are mostly designed for specific scenarios and often lack extensibility for application in other areas, thereby limiting their usefulness. In this paper, we present a hybrid hierarchical framework that successfully combines two of the main specific-sensor-based PA methods into an effective hybrid solution for general weight exercise applications. The fusion solution separates free weight and non-free weight activities and then further classifies free weight exercises, whilst measuring quantities of repetitions and sets, thus providing a measure of intensity. By fusing accelerometer and electrocardiogram (ECG) data, a One Class Support Vector Machine (OC-SVM) and a Hidden Markov Model (HMM) are applied for exercise recognition and we use semantic inference for determining the intensity of the exercise. The results are based on data from 10 healthy subjects (age: 30 ± 5; BMI: 25 ± 5.5 kg/m^2; body fat: 20.5 ± 5.4), which shows good accuracy in exercise recognition and intensity measurement. This framework can be extended to support additional types of PA recognition in complex applications.
Cerebrovascular function in patients with chronic obstructive pulmonary disease: the impact of exercise training.

This study examined cerebral blood flow (CBF) and its regulation before and after a short-term periodized aerobic exercise training intervention in patients with chronic obstructive pulmonary disease (COPD). Twenty-eight patients with COPD (forced expiratory volume in 1 s/forced vital capacity < 0.7 and <lower limit of normal) and 24 healthy control subjects participated in the study. Extracranial CBF (duplex ultrasound), middle cerebral artery velocity (MCAv; transcranial Doppler), cerebrovascular reactivity to hypocapnia and hypercapnia, and dynamic cerebral autoregulation (transfer function analysis) were quantified. These tests were repeated in both patients with COPD ( n = 23) and control subjects ( n = 20) after 8 wk of periodized upper and lower body aerobic exercise training (3 sessions/wk). At baseline, global extracranial CBF was comparable between the COPD and control groups (791 ± 290 vs. 658 ± 143 ml/min, P = 0.25); however, MCAv was lower in patients with COPD compared with control subjects (46 ± 9 vs. 53 ± 10 cm/s, P = 0.05). Although there were no group differences in dynamic cerebral autoregulation or the MCAv response to hypercapnia, patients with COPD had a lower MCAv response to hypocapnia compared with control subjects (-1.1 ± 1.5 vs. -1.6 ± 1.3 cm·s-1·mmHg-1, P = 0.02). After aerobic training, absolute peak O2 consumption increased in both groups, with a greater improvement in control subjects (1.7 ± 0.4 vs. 4.1 ± 0.2 ml·kg-1·min-1, respectively, P = 0.001). Despite these improvements in peak O2 consumption, there were no significant alterations in CBF or any measures of cerebrovascular function after exercise training in either group. In conclusion, patients with COPD have a blunted cerebrovascular response to hypocapnia, and 8 wk of aerobic exercise training did not alter cerebrovascular function despite significant improvements in cardiorespiratory fitness. NEW & NOTEWORTHY No study to date has investigated whether exercise training can alter resting cerebral blood flow (CBF) regulation in patients with chronic obstructive pulmonary disease (COPD). This study is the first to assess CBF regulation at rest, before, and after aerobic exercise training in patients with COPD and healthy control subjects. This study demonstrated that while exercise training improved aerobic fitness, it had little effect on CBF regulation in patients with COPD or control subjects.
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain

We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent — Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.
SISMA: A Numerical Simulation Software for SIS Mixer Design

SISMA is a numerical simulation software programmed for the design of SIS mixers with single junction or junction arrays. It is based on quantum mixing theory and a proximation of the nonlinearity up to 2nd harmonic. A new fixed-point iteration algorithm is designed to solve the large-signal nonlinear equation, which enables simulations of SIS mixers with distributed series-connected junction arrays, removing the limitation that only parallel-connected junction arrays can be simulated. The temperature and frequency-dependent complex surface impedances of various superconducting materials can be calculated in the simulation of thin-film superconducting passive components. The graphic interface of this software is a beneficial to fast optimization of the mixer circuit design.