Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Bioelectrical Impedance Thresholds for Early Detection of Breast Cancer-related Lymphedema

Background: Bioelectrical impedance technology is a common technique used for the early detection of breast cancer-related lymphedema (BCRL). However, studies on the threshold value established by Inbody 720 device (Biospace, Korea) have been extremely limited. We aimed to determine its reference range and cutoff values. Methods: All patients were recruited from October 2017 to October 2019 at the Peking University People's Hospital Breast Center. In total, 82 patients with unilateral BCRL and 1305 healthy subjects were recruited in this study. We measured the extracellular fluid (ECF) ratio, extracellular water (ECW) ratio, as well as the single-frequency bioimpedance analysis (SFBIA) ratios at 1 and 5 kHz with the Inbody 720 device. The Youden index-based cutoff points, mean + 2SD and mean + 3SD values of these four indicators for both dominant and nondominant arms were also calculated. Results: Data were collected from 1387 women, including healthy subjects and patients with lymphedema. All statistical analyses were performed with SPSS. Significant differences were found between the two groups in the ECW, ECF, and SFBIA ratios. For the dominant affected arms, the Youden index-based cutoff points for the ECF, ECW, as well as SFBIA ratios at 1 and 5 kHz were 1.009, 1.008, 1.068, and 1.068, respectively. For the nondominant affected arms, the Youden index-based cutoff points were 1.014, 1.013, 1.047, and 1.048, respectively. The mean + 2 standard deviations (SD) and mean + 3SD values were also calculated. Conclusions: We determined the Youden index-based cutoff points, mean + 2SD and mean + 3SD values of the ECF, ECW, as well as SFBIA ratios at 1 and 5 kHz for both dominant and nondominant arms with data from 1305 healthy subjects. Next, the Youden index-based cutoff points, the mean + 2SD and mean + 3SD values were used to recognize patients with lymphedema. We found that the Youden index-based cutoff points and the mean + 2SD showed similar identification capacity on lymphedema, and they seemed to distinguish more patients with lymphedema than mean + 3SD values.
Knowledge, and practices regarding COVID-19 among public healthcare workers in Galle Regional Directors of Health Services, Southern province, Sri Lanka

Background: Coronavirus disease-2019 (COVID-19) is a public health problem where the world's 3.3 billion global workforces are at risk of losing their livelihoods. The present study sought to determine the knowledge, and practices of community health care workers (HCWs) toward COVID-19 in the Regional Director of Health Services in Galle District, Sri Lanka. Methods: A descriptive cross-sectional study was conducted with the objective of knowledge, and practices among healthcare workers at the Regional Director of Health Services in Galle district, Sri Lanka, during August 2020. A selfadministered questionnaire was used as the study instrument. The sample consisted of 20 -Medical Officers of Health,18 Additional Medical Officers of Health, 14-Senior Public Health Inspectors, and 101Public Health Inspectors, who are directly involved in the management of the COVID-19 pandemic. Knowledge and practices towards COVID-19 were assessed by using a pre-tested questionnaire. A cut-off of 60% was used to determine sufficient knowledge (≥60%), and good practice (≥60%). All analyses were performed using SPSS version 21, chi-square and binary logistic regression were performed for the significance of the data. Results: Of the 153 healthcare workers, approached, 118(77%) responded. A majority of the participant were male (76.7%, n=90). Overall, 83.1%(n=98) had a sufficient level of knowledge, and 87.3%(n=103) had high to moderate level preventive practices toward COVID-19. Univariate and multivariate analysis showed: Female health care workers had high knowledge level than male health care workers (OR=6.86, 95% CI:1.07-235.6; p=0.05) and junior health care workers had good preventive practices than senior workers (OR: 3.63; 95%CI: 1.01-120.3; p=0.05). Conclusion: Continued professional education on Covid-19 is advised among healthcare workers in Sri Lanka to improve knowledge, hence averting negative attitudes and promoting positive preventive and therapeutic practices.
The prognosis of giant cell tumor of bone and the vital risk factors that affect its postoperative recurrence: a meta-analysis

Background The purpose of this study is to analyze the overall prognosis of giant cell tumor of bone (GCTB) and the risk factors that affect its postoperative recurrence. Methods The databases of PubMed, Cochrane, Web of Science, Embase, China national knowledge infrastructure, China Biology Medicine disc, and Wanfang were searched until 20 June 2020. Following patients, intervention, comparator, outcomes, study design (PICOS) guidelines, eligible articles were defined as studies evaluating the overall prognosis of GCTB and the risk factors that affect its postoperative recurrence. The association between five risk factors (surgical methods, whether there is soft tissue invasion, tumor size, p53 expression, vascular endothelial growth factor (VEGF) expression) and the recurrence of GCTB were calculated using fixed-effects or random-effects models. The heterogeneity of the odd ration (OR) and effect size (ES) of each study was quantified using Cochran’s Q test and Higgins-I2 statistic. The publication bias was analyzed through the drawing of the funnel diagram. Results A total of 10 studies were included in the study. We found that the probability of recurrence of patients who choose simple curettage is 5.75 times that of patients who choose amputation or total resection. Patients with soft tissue invasion are 3.76 times more likely to relapse than non-invasive GCTB. The probability of recurrence of patients with tumors larger than 5 cm is 2.8 times that of patients with tumors smaller than. Patients with positive expression of p53 are 3.82 times more likely to relapse than patients with negative expression. And patients with positive expression of VEGF are 3.82 times more likely to relapse than patients with negative expression. Conclusions In conclusion, our analysis of five risk factors can be used to measure the recurrence of GCTB and provide important preoperative recommendations for patients with GCTB.
Fitness barriers to spread of colistin-resistant Klebsiella pneumoniae overcome by establishing niche in patient population with elevated colistin use

To combat antibiotic resistance, it is critical to improve our understanding of how new resistant strains emerge and spread. An antibiotic resistance threat of critical priority is the epidemic ST258 strain of carbapenem-resistant Klebsiella pneumoniae (CRKP). Here, we studied the spread of resistance among ST258 to an antibiotic of last resort, colistin, by tracking its evolution across 21 U.S. long-term acute care hospitals over the course of a year. Phylogenetic analysis suggested that a significant cost was associated with colistin resistance in most cases, as resistance emergence was common but resistance variants were rarely transmitted. The high cost of resistance was further supported by the observation that several of the resistance variants that were transmitted had acquired secondary variants that reverted the strain to colistin susceptibility. The exceptions to the general pattern of instability associated with colistin resistance were two large clusters of resistant strains in one ST258 clade II sublineage (clade IIB) present across 11 of the 12 sampled Southern California hospitals. Quantification of transmission fitness in the healthcare environment using time-scaled haplotypic density indicated that while resistant isolates from other clades were less fit than their susceptible counterparts, clade IIB resistant isolates were more fit. Overlaying patient clinical data suggested that the increased fitness of colistin-resistant clade IIB isolates is in part driven by a lineage defining variant that increased clade IIB's association with patient subpopulations who were more likely to be treated with colistin. These results show that a favorable genetic background and sustained selective pressure led to the emergence and spread of a colistin-resistant ST258 sublineage across a regional healthcare network. More broadly, these findings highlight the utility of integrating pathogen genomic and corresponding clinical data from regional healthcare networks to detect and understand the origin and dissemination of antibiotic resistance threats.
Water Quality in the Tietê River Reservoirs (Billings‚ Barra Bonita‚ Bariri and Promissão‚ SP-Brazil) and Nutrient Fluxes across the Sediment-Water Interface (Barra Bonita)

: Water Quality in the Tietê River Reservoirs (Billings, Barra Bonita, Bariri and Promissão, SP-Brazil) and Nutrient Fluxes across the Sediment-Water Interface (Barra Bonita). The quality of the water in the Tietê River reservoirs (Billings, Barra Bonita, Bariri and Promissão) was assessed at different times during 2001 and 2002 in a series of analyses of dissolved chemical constituents (nitrate, nitrite, ammonium, soluble reactive phosphorus, dissolved organic carbon, chloride, fluoride, sulfate and reactive silica), and of the particulate phase (total organic nitrogen and total phosphorus). Physico-chemical variables such as pH, redox potential, electrical conductivity and dissolved oxygen were measured in situ. A study of seasonal variation in the water column was carried out to evaluate the behavior of these variables throughout a complete hydrological year. The majority of the nutrient concentrations determined in the water column increased in the dry season (autumn-winter-spring: April-September), compared to the rainy season (summer: November-March), suggesting that their seasonal cycles were associated with the water residence time and rainfall, as previous studies had already demonstrated. An evaluation of the present water quality status in these reservoirs was made and compared to results from previous studies. It is documented that water quality has deteriorated in the last 2 decades (1979/80 to 2000/2002). Using Fick’s first law, diffusive fluxes across the sediment-water column interface were calculated for the studied reservoirs. Specifically for Barra Bonita, results show that sediment acts, for the majority of the elements, as a source to the water column, reaching values such as 284 and 5 mg cm -2 year -1 for ammonium and soluble reactive phosphorus, respectively. These are rather critical findings because, even if current nutrient sources were controlled at the Barra Bonita reservoir, the water quality would not necessarily improve as sediment would continue to be an important internal source of nutrient to the water column for a long time.
Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted signiﬁcant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a speciﬁc type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and deﬁne the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we ﬁnd that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically diﬀerent from that in natural distribution. Thus, DNNs that ﬁt the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the diﬀerence between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the eﬃcacy of the proposed method. Our method can be seen as the ﬁrst attempt to leverage causality for mitigating adversarial vulnerability.
An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Cardiovascular diseases monitoring, usually involving electrocardiogram (ECG) traces analysis, is one of the most promising and high-impact applications. Nevertheless, to fully exploit the potential of IoMT in this domain, some steps forward are needed. First, the edge-computing paradigm must be added to the picture. A certain level of near-sensor processing has to be enabled, to improve the scalability, portability, reliability and responsiveness of the IoMT nodes. Second, novel, increasingly accurate data analysis algorithms, such as those based on artificial intelligence and Deep Learning, must be exploited. To reach these objectives, designers, and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets. In this work, we explore the implementation of a cognitive data analysis algorithm, based on a convolutional neural network trained to classify ECG waveforms, on a resource-constrained microcontroller-based computing platform. To minimize power consumption, we add an adaptivity layer that dynamically manages the hardware and software configuration of the device to adapt it at runtime to the required operating mode. Our experimental results show that adapting the node setup to the workload at runtime can save up to 50% power consumption. Our optimized and quantized neural network reaches an accuracy value higher than 97% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
Evidence of 100 TeV ${\gamma}$-ray emission from HESS J1702-420: A new PeVatron candidate

The identification of active PeVatrons, hadronic particle accelerators reaching the knee of the cosmic-ray spectrum (at the energy of few PeV), is crucial to understand the origin of cosmic rays in the Galaxy. In this context, we report on new H.E. S.S. observations of the PeVatron candidate HESS J1702-420, which reveal the presence of gamma-rays up to 100 TeV. This is the first time in the history of H.E. S.S. that photons with such high energy are clearly detected. Remarkably, the new deep observations allowed the discovery of a new gamma-ray source component, called HESS J1702-420A, that was previously hidden under the bulk emission traditionally associated with HESSJ1702-420. This new object has a power-law spectral slope<2 and a gamma-ray spectrum that, extending with no sign of curvature up to 100 TeV, makes it an excellent candidate site for the presence of PeV-energy cosmic rays. This discovery brings new information to the ongoing debate on the nature of the unidentified source HESSJ1702-420, one of the most compelling PeVatron candidates in the gamma-ray sky, and on the origin of Galactic cosmic rays.
Shallow Optical Flow Three-Stream CNN For Macro- And Micro-Expression Spotting From Long Videos

Facial expressions vary from the visible to the subtle. In recent years, the analysis of micro-expressions— a natural occurrence resulting from the suppression of one’s true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting micro-expressions in long videos becomes increasingly challenging when intertwined with normal or macro-expressions. In this paper, we propose a shallow optical flow three-stream CNN (SOFTNet) model to predict a score that captures the likelihood of a frame being in an expression interval. By fashioning the spotting task as a regression problem, we introduce pseudo-labeling to facilitate the learning process. We demonstrate the efficacy and efficiency of the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art performance is achieved on CAS(ME)2 with equally promising results on SAMM Long Videos.
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges to generate a graph. However, the likelihood of a graph under the autoregressive model is intractable, as there are numerous sequences leading to the given graph; this makes maximum likelihood estimation challenging. Instead, in this work we derive the exact joint probability over the graph and the node ordering of the sequential process. From the joint, we approximately marginalize out the node orderings and compute a lower bound on the log-likelihood using variational inference. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods. Our experiments show that the log-likelihood bound is significantly tighter than the bound of previous schemes. Moreover, the models fitted with the proposed algorithm can generate high-quality graphs that match the structures of target graphs not seen during training. We have made our code publicly available at \hyperref[https://github.com/tufts-ml/graph-generation-vi]{https://github.com/tufts-ml/graph-generation-vi}.
Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution

Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated, presenting serious security threats to real-world applications. Since existing textual backdoor attacks pay little attention to the invisibility of backdoors, they can be easily detected and blocked. In this work, we present invisible backdoors that are activated by a learnable combination of word substitution. We show that NLP models can be injected with backdoors that lead to a nearly 100% attack success rate, whereas being highly invisible to existing defense strategies and even human inspections. The results raise a serious alarm to the security of NLP models, which requires further research to be resolved. All the data and code of this paper are released at https://github.com/thunlp/BkdAtk-LWS.
Recollision of excited electron in below-threshold nonsequential double ionization

Recollision is the most important post-tunneling process in strong-field physics, but so far has been restricted to interaction between the first ionized electron and the residual ion in nonsequential double ionization. Here we identify the role of recollision of the second ionized electron in the below-threshold nonsequential double ionization process by introducing a Coulomb-corrected quantum-trajectories method. We will reproduce the experimentally observed cross-shaped and anti-correlated patterns in correlated two-electron momentum distributions, and the transition between them. Both the cross-shaped and anti-correlated patterns are attributed to recolliding trajectories of the second electron. The effect of recollision of the second electron is significantly enhanced by the stronger Coulomb potential of the higher valence residual ion, and is further strengthened by the recapture process of the second electron. Our work paves a potential way to image ultrafast dynamics of atoms and molecules in intense laser field. The recollision of photoexcited electrons with ions is a fundamental process in light-matter interaction, but so far only the rescattering of a single electron has been considered. Here by considering the recollision of a second electron theoretically, experimentally-observed momentum distributions can be understood.
Nonparametric Learning of Two-Layer ReLU Residual Units

We describe an algorithm that learns two-layer residual units using rectified linear unit (ReLU) activation: suppose the input $\mathbf{x}$ is from a distribution with support space $\mathbb{R}^d$ and the ground-truth generative model is a residual unit of this type, given by $\mathbf{y} = \boldsymbol{B}^\ast\left[\left(\boldsymbol{A}^\ast\mathbf{x}\right)^+ + \mathbf{x}\right]$, where ground-truth network parameters $\boldsymbol{A}^\ast \in \mathbb{R}^{d\times d}$ represent a full-rank matrix with nonnegative entries and $\boldsymbol{B}^\ast \in \mathbb{R}^{m\times d}$ is full-rank with $m \geq d$ and for $\boldsymbol{c} \in \mathbb{R}^d$, $[\boldsymbol{c}^{+}]_i = \max\{0, c_i\}$. We design layer-wise objectives as functionals whose analytic minimizers express the exact ground-truth network in terms of its parameters and nonlinearities. Following this objective landscape, learning residual units from finite samples can be formulated using convex optimization of a nonparametric function: for each layer, we first formulate the corresponding empirical risk minimization (ERM) as a positive semi-definite quadratic program (QP), then we show the solution space of the QP can be equivalently determined by a set of linear inequalities, which can then be efficiently solved by linear programming (LP). We further prove the strong statistical consistency of our algorithm, and demonstrate its robustness and sample efficiency through experimental results on synthetic data and a set of benchmark regression datasets.
Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation

Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e.g., heart rate) using facial videos. Recent deep learning architectures have improved the accuracy of such physiological measurement significantly, yet they are restricted by the diversity of the annotated videos. The existing datasets MMSE-HR, AFRL, and UBFC-RPPG contain roughly 10%, 0%, and 5% of dark-skinned subjects respectively. The unbalanced training sets result in a poor generalization capability to unseen subjects and lead to unwanted bias toward different demographic groups. In Western academia, it is regrettably difficult in a university setting to collect data on these dark-skinned subjects. Here we show a first attempt to overcome the lack of dark-skinned subjects by synthetic augmentation. A joint optimization framework is utilized to translate real videos from light-skinned subjects to dark skin tones while retaining their pulsatile signals. In the experiment, our method exhibits around 31% reduction in mean absolute error for the dark-skinned group and 46% improvement on bias mitigation for all the groups, as compared with the previous work trained with just real samples.
Quantile Bandits for Best Arms Identification with Concentration Inequalities

We consider a variant of the best arm identification task in stochastic multi-armed bandits. Motivated by risk-averse decision-making problems in fields like medicine, biology and finance, our goal is to identify a set of $m$ arms with the highest $\tau$-quantile values under a fixed budget. We propose Quantile Successive Accepts and Rejects algorithm (Q-SAR), the first quantile based algorithm for fixed budget multiple arms identification. We prove two-sided asymmetric concentration inequalities for order statistics and quantiles of random variables that have non-decreasing hazard rate, which may be of independent interest. With the proposed concentration inequalities, we upper bound the probability of arm misidentification for the bandit task. We show illustrative experiments for best arm identification.