Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Cost Parameterizations of Deep Convolution Neural Networks

Convolutional Neural Networks (CNNs) filter the input data using a series of spatial convolution operators with compactly supported stencils and point-wise nonlinearities. Commonly, the convolution operators couple features from all channels. For wide networks, this leads to immense computational cost in the training of and prediction with CNNs. In this paper, we present novel ways to parameterize the convolution more efficiently, aiming to decrease the number of parameters in CNNs and their computational complexity. We propose new architectures that use a sparser coupling between the channels and thereby reduce both the number of trainable weights and the computational cost of the CNN. Our architectures arise as new types of residual neural network (ResNet) that can be seen as discretizations of a Partial Differential Equations (PDEs) and thus have predictable theoretical properties. Our first architecture involves a convolution operator with a special sparsity structure, and is applicable to a large class of CNNs. Next, we present an architecture that can be seen as a discretization of a diffusion reaction PDE, and use it with three different convolution operators. We outline in our experiments that the proposed architectures, although considerably reducing the number of trainable weights, yield comparable accuracy to existing CNNs that are fully coupled in the channel dimension.
How Many Samples are Needed to Learn a Convolutional Neural Network?

A widespread folklore for explaining the success of convolutional neural network (CNN) is that CNN is a more compact representation than the fully connected neural network (FNN) and thus requires fewer samples for learning. We initiate the study of rigorously characterizing the sample complexity of learning convolutional neural networks. We show that for learning an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O} (m/\epsilon^2)$, whereas its FNN counterpart needs at least $\Omega(d/\epsilon^2)$ samples. Since $m \ll d$, this result demonstrates the advantage of using CNN. We further consider the sample complexity of learning a one-hidden-layer CNN with linear activation where both the $m$-dimensional convolutional filter and the $r$-dimensional output weights are unknown. For this model, we show the sample complexity is $\widetilde{O}\left((m+r)/\epsilon^2\right)$ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are localized empirical process and a new lemma characterizing the convolutional structure. We believe these tools may inspire further developments in understanding CNN.
Relation between coping and posttrauma cognitions on PTSD in a combat-trauma population

ABSTRACT Individual differences in cognitive processes and coping behaviors play a role in the development and maintenance of posttraumatic stress disorder (PTSD). Given the large numbers of combat-exposed service members returning from the Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND) conflicts, exploring individual differences in cognitive-affective processes is important for informing our understanding of PTSD etiology and early intervention in military samples. The present study examined the unique main and interactive effects of negative posttrauma cognitions (i.e., negative beliefs about self [NS], the world [NW], and self-blame [SB]) and coping strategies (i.e., positive behavioral, positive cognitive, avoidant coping, and social and emotional coping) on PTSD diagnosis within 155 (Mage = 30.7, SD = 4.48) OEF/OIF/OND combat trauma-exposed Veterans recruited from an ongoing study examining the effects of combat trauma and stress reactivity. In the final, stepwise logistic regression analysis, avoidant coping, but no other coping strategy, was significantly positively related to PTSD diagnosis in the initial step. Higher levels of NS, but not NW, were significantly associated with having a PTSD diagnosis, whereas SB was associated with decreased likelihood of PTSD, above and beyond coping strategies. A significant interaction effect was found between NS and positive cognitive coping, such that greater positive cognitive coping weakened the relationship between NS and PTSD. Examining and addressing coping behaviors and negative thoughts of self jointly may benefit assessment and intervention approaches in a combat-trauma population.
An evaluation of nivolumab for the treatment of metastatic renal cell carcinoma

ABSTRACT Introduction: The treatment paradigm for metastatic renal cell carcinoma (mRCC) has undergone a revolution with the rapid market approval of multiple agents over a three-year period. The immunogenicity of renal cell carcinoma (RCC) provided the biological rationale to assess the clinical efficacy of nivolumab, an immune checkpoint inhibitor. Nivolumab is approved for second-line treatment after failure of angiogenesis-targeted therapy and in combination therapy with ipilimumab for previously untreated intermediate- or poor-risk advanced RCC. Areas covered: The authors review the preclinical and clinical data supporting nivolumab employment in mRCC. Discussion of the underlying mechanisms of immunotherapy, data on objective responses, safety and tolerability, regulatory affairs, and future directions of nivolumab therapy are highlighted. Expert opinion: Nivolumab serves as a proof-of-principle of the efficacy of immunotherapy with checkpoint inhibition in mRCC. Nivolumab may be considered the leading monotherapy in the second-line setting for patients with low tumor volume considering its risks and benefits. Nivolumab was recently approved in the first-line setting as part of combination therapy with another immune modulator. Moreover, nivolumab use may offer clinicians the option for treatment beyond progression as emerging data has indicated possible overall survival benefits in this setting. Ongoing clinical studies may result in nivolumab use in the first-line setting, as monotherapy or in combination therapy with antiangiogenesis agents.
国产抗CD20单抗治疗CD20+B细胞非霍奇金淋巴瘤的Ⅰ期临床耐受性和药效学研究

目的 通过剂量递增的Ⅰ期临床试验，评价重组人-鼠嵌合抗CD20单克隆抗体注射液IBI301单次给药在经治疗达客观缓解的CD20+ B细胞非霍奇金淋巴瘤患者中的耐受性、安全性及药效学特征。 方法 纳入9例受试者，按剂量递增原则设125、375和500 mg/m2三个剂量组，每组3例逐组完成试验。 3组受试者均接受单次IBI301静脉输注，125、375、500 mg/m2组IBI301的中位暴露量分别为243、690、980 mg。 观察IBI301给药后不良事件（AE）发生情况。 采用流式细胞术检测外周血CD19+、CD20+ B细胞比例，采用速率散射比浊法检测血清IgG、IgM水平，进行药效学评价。 结果 9例受试者发生52例次AE，包括：125 mg/m2组18例次，375 mg/m2组14例次，500 mg/m2组20例次。 其中不良反应共26例次，22例次与试验药物可能有关，4例次可能无关；对症处理后均消失或恢复至基线水平。 常见的AE包括WBC降低、上呼吸感染、ANC降低、消化不良、高尿酸血症、感觉异常、口腔黏膜炎和头晕。 研究中未出现AE导致的用药暂停、受试者退出或试验提前中止，未发生严重AE（SAE）及剂量限制性毒性（DLT）事件。 所有受试者CD20+、CD19+ B细胞比例均有不同程度下降，而IgG和IgM水平无明显变化。 结论 IBI301耐受性良好，受试者出现的不良反应均恢复，未发生SAE及DLT事件。 IBI301对CD20+ B细胞有较好的清除作用。
Influence of different drilling preparation on cortical bone: A biomechanical, histological, and micro‐CT study on sheep

OBJECTIVE The aim of this study was to investigate the extent of cortical bone remodeling between two different drilling protocols by means of histomorphometric, µ-CT, and biomechanical analyses. MATERIAL AND METHODS A total of 48 implants were inserted into the mandible of six sheep following two drilling protocols: Group A (Test, n = 24), undersized preparation; Group B (Control, n = 24), non-undersized preparation. The animals were euthanatized to obtain 5 and 10 weeks of implantation time. Removal torque (RTQ) was measured on 12 implants of each group and the peri-implant bone was µ-CT scanned. Bone volume density (BV/TV) was calculated in pre-determined cylindrical volumes, up to 1.5 mm from implant surface. Non-decalcified histology was prepared on the remaining 12 implants from each group, where total bone-to-implant contact (totBIC) and newly-formed BIC (newBIC) was measured. Bone Area Fraction Occupancy (BAFO) was determined in pre-determined areas up to 1.5 mm from implant surface. Paired sample t test or Wilcoxon signed-rank test was used to investigate differences between the groups. RESULTS Group A presented significantly increased RTQ value at 5 weeks, while no difference was observed at 10 weeks. Group B presented increased BV/TV value at 5 weeks. Both groups showed comparable values for totBIC at both time-points. However, Group A presented significantly lower newBIC at 5 weeks. Higher BAFO was observed in Group B at 5 weeks. CONCLUSIONS Implants inserted into undersized sites has an increased biomechanical performance, but provoked major remodeling of the cortical bone during the early healing period compared to non-undersized preparations. After 10 weeks, no difference was observed.
Determining the Radiation Enhancement Effects of Gold Nanoparticles in Cells in a Combined Treatment with Cisplatin and Radiation at Therapeutic Megavoltage Energies

Combined use of chemotherapy and radiation therapy is commonly used in cancer treatment, but the toxic effects on normal tissue are a major limitation. This study assesses the potential to improve radiation therapy when combining gold nanoparticle (GNP) mediated radiation sensitization with chemoradiation compared to chemoradiation alone. Incorporation of GNPs with 2 Gy, 6 MV (megavoltage) radiation resulted in a 19 ± 6% decrease in survival of MDA-MB-231 cells. Monte-Carlo simulations were performed to assess dosimetric differences in the presence of GNPs in radiation. The results show that physics dosimetry represents a small fraction of the observed effect. The survival fraction of the cells exposed to GNPs, cisplatin, and radiation was 0.16 ± 0.007, while cells treated with cisplatin and radiation only was 0.23 ± 0.011. The presence of GNPs resulted in a 30 ± 6% decrease in the survival, having an additive effect. The concentration of the GNPs and free drug used for this study was 0.3 and 435 nM, respectively. These concentrations are relatively lower and achievable in an in vivo setting. Hence, the results of our study would accelerate the incorporation of GNP-mediated chemoradiation into current cancer therapeutic protocols in the near future.
Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.
Modeling the Effect of Primary and Secondary Twinning on Texture Evolution during Severe Plastic Deformation of a Twinning-Induced Plasticity Steel

Modeling the effect of deformation twinning and the ensuing twin-twin- and slip-twin-induced hardening is a long-standing problem in computational mechanical metallurgy of materials that deform by both slip and twinning. In this work, we address this effect using the twin volume transfer method, which obviates the need of any cumbersome criterion for twin variant selection. Additionally, this method is capable of capturing, at the same time, secondary or double twinning, which is particularly important for modeling in large strain regimes. We validate our modeling methodology by simulating the behavior of an Fe-23Mn-1.5Al-0.3C twinning-induced plasticity (TWIP) steel under large strain conditions, experimentally achieved in this work through equal-channel angular pressing (ECAP) for up to two passes in a 90° die following route BC at 300 °C. Each possible twin variant, whether nucleating inside the parent grain or inside a potential primary twin variant was predefined in the initial list of orientations as possible grain of the polycrystal with zero initial volume fraction. A novelty of our approach is to take into account the loss of coherency of the twins with their parent matrix under large strains, obstructing progressively their further growth. This effect has been captured by attenuating growth rates of twins as a function of their rotation away from their perfect twin orientation, dubbed here as “disorientation” with respect to the mother grain’s lattice. The simulated textures and the hardening under tensile strain showed very good agreement with experimental characterization and mechanical testing results. Furthermore, upper-bound Taylor deformation was found to be operational for the TWIP steel deformation when all the above ingredients of twinning are captured, indicating that self-consistent schemes can be bypassed.
Information discovery from complementary literatures: Categorizing viruses as potential weapons

Using novel informatics techniques to process the output of Medline searches, we have generated a list of viruses that may have the potential for development as weapons. Our findings are intended as a guide to the virus literature to support further studies that might then lead to appropriate defense and public health measures. This article stresses methods that are more generally relevant to information science. Initial Medline searches identified two kinds of virus literatures---the first concerning the genetic aspects of virulence, and the second concerning the transmission of viral diseases. Both literatures taken together are of central importance in identifying research relevant to the development of biological weapons. Yet, the two literatures had very few articles in common. We downloaded the Medline records for each of the two literatures and used a computer to extract all virus terms common to both. The fact that the resulting virus list includes most of an earlier independently published list of viruses considered by military experts to have the highest threat as potential biological weapons served as a test of the method; the test outcome showed a high degree of statistical significance, thus supporting an inference that the new viruses on the list share certain important characteristics with viruses of known biological warfare interest.
A Diversified Generative Latent Variable Model for WiFi-SLAM

WiFi-SLAM aims to map WiFi signals within an unknown environment while simultaneously determining the location of a mobile device. This localization method has been extensively used in indoor, space, undersea, and underground environments. For the sake of accuracy, most methods label the signal readings against ground truth locations. However, this is impractical in large environments, where it is hard to collect and maintain the data. Some methods use latent variable models to generate latent-space locations of signal strength data, an advantage being that no prior labeling of signal strength readings and their physical locations is required. However, the generated latent variables cannot cover all wireless signal locations and WiFi-SLAM performance is significantly degraded. Here we propose the diversified generative latent variable model (DGLVM) to overcome these limitations. By building a positive-definite kernel function, a diversity-encouraging prior is introduced to render the generated latent variables non-overlapping, thus capturing more wireless signal measurements characteristics. The defined objective function is then solved by variational inference. Our experiments illustrate that the method performs WiFi localization more accurately than other label-free methods.
Nonsteroidal anti‐inflammatory drugs reduce the time to recovery of gut function after elective colorectal surgery: a systematic review and meta‐analysis

Postoperative ileus causes significant patient morbidity after abdominal surgery. Some evidence suggests nonsteroidal anti‐inflammatory drugs (NSAIDs) may reduce time to gut recovery, but there has not been a meta‐analysis to assess their efficacy. This systematic review and meta‐analysis aimed to determine the benefit of NSAIDs for recovery of postoperative gut function in patients undergoing elective colorectal surgery.
Supplementation of Vitamin D Deficiency in Patients with Neuroendocrine Tumors Using Over-the-Counter Vitamin D3 Preparations

ABSTRACT Vitamin D (vit-D) deficiency is highly prevalent in patients with gastro-entero-pancreatic neuroendocrine tumors (GEP-NET) and has been linked to reduced overall survival. We here assessed the vit-D status in 183 patients with GEP-NET at the time of their first presentation in the ARDEN NET Centre. We further examined the effect of simple advice to increase vit-D intake using over-the-counter vit-D preparations [colecalciferol (Vit-D3), 1,000–2,000 units/day], over a prospective observation period of 24 mo. At baseline, only 33.3% of patients showed vit-D sufficiency (25-OH-vit-D; >50 nmol/L), the remainder was insufficient (31.3%; 25-OH-vit-D; 25–50 nmol/L) or deficient (35.5%; 25-OH-vit-D; <25 nmol/L). Repeated advice to increase vit-D intake at routine 6-monthly follow-up appointments was associated with increased 25-OH-vit-D from 37.8 ± 3.5 nmol/L at baseline to 60.4 ± 5.6 nmol/L (P < 0.0001) and 56.8 ± 7.0 nmol/L (P = 0.039) after 12 and 24 mo. Percentage of vit-D insufficiency decreased from 66.6% at baseline to 44.9% and 46.2% after 12 and 24 months, respectively. Previous abdominal surgery, but not treatment with somatostatin analogues predicted 25-OH-vit-D levels in bootstrapped linear regression analyses (P = 0.037). In summary, simple advice to increase vit-D intake using over-the-counter preparations was associated with significant improvement of vit-D deficiency/insufficiency, although, 15% of GEP-NET patients remained deficient and may benefit from additional measures of vit-D replacement.
Sampling-Free Variational Inference of Bayesian Neural Nets

We propose a new Bayesian Neural Net (BNN) formulation that affords variational inference for which the evidence lower bound (ELBO) is analytically tractable subject to a tight approximation. We achieve this tractability by decomposing ReLU nonlinearities into an identity function and a Kronecker delta function. We demonstrate formally that assigning the outputs of these functions to separate latent variables allows representing the neural network likelihood as the composition of a chain of linear operations. Performing variational inference on this construction enables closed-form computation of the evidence lower bound. It can thus be maximized without requiring Monte Carlo sampling to approximate the problematic expected log-likelihood term. The resultant formulation boils down to stochastic gradient descent, where the gradients are not distorted by any factor besides minibatch selection. This amends a long-standing disadvantage of BNNs relative to deterministic nets. Experiments on four benchmark data sets show that the cleaner gradients provided by our construction yield a steeper learning curve, achieving higher prediction accuracies for a fixed epoch budget.
Sustained Impact of a Pediatric Resident-Led Patient Safety Council

Objectives The aim of the study was to evaluate the impact of a resident-led patient safety council. This study measured change in resident perceptions and knowledge of safety issues for 3 years, as well as behavioral choices to participate in patient safety activities during and after residency. Methods Pediatric residents formed a resident-led safety council to engage their peers in patient safety activities. Surveys were distributed annually from 2013 to 2015 to measure residents’ perception and knowledge surrounding patient safety. The number of patient safety reports submitted by residents was tracked for the same period. In addition, recent graduates were surveyed to assess the influence of the council on postresidency involvement in patient safety. Results Resident perception of the institutional culture of safety improved and knowledge of basic patient safety concepts increased. The number of resident-submitted safety reports increased from 6.2 to 15.2 reports per month in the 2013 and 2015 academic years, respectively. Surveys of recent graduates suggest that involvement with the safety council during residency fostered future engagement in patient safety. Conclusions This resident-led council models successful involvement of trainees in system-based patient safety. Such involvement can help shape the safety culture within a training program and encourages continued participation in patient safety after residency completion.
Sustainable Leadership, Organizational Trust on Job Satisfaction: Empirical Evidence from Higher Education Institutions in Syria

This research develops a theoretical model of sustainable leadership, organizational trust and satisfaction at work in higher education environment in Syria. The model assesses staff perception of outstanding leadership behaviors and examines its relationship with perceived organizational trust in the field of higher education institutions in Syria. This research examines a conceptual framework identifying outstanding leadership styles and behaviors which are associated with sustainable leadership, organisational trust identified by members’ trust in their coworkers, and job satisfaction at an institutional level. The research methodology applied in this research develops a quantitative approach through application of questionnaire survey. To measure the dimensionality of scale factors an exploratory factor analysis is conducted. Reliability analysis is performed, Cronbach alpha test indicates that the research scales are internally consistent. The sample of the study employed a convenience sample from higher education institutions. The managerial implication of the research study recommends application and adoption of sustainable leadership behaviors among functional, mid and senior levels of managers and academics in management positions in higher education institutions. The limitation of research is mainly indicated in the sample size and measurement scales of sustainable leadership, organizational trust and job satisfaction.
Wasserstein Coresets for Lipschitz Costs

Sparsification is becoming more and more relevant with the proliferation of huge data sets. Coresets are a principled way to construct representative weighted subsets of a data set that have matching performance with the full data set for specific problems. However, coreset language neglects the nature of the underlying data distribution, which is often continuous. In this paper, we address this oversight by introducing a notion of measure coresets that generalizes coreset language to arbitrary probability measures. Our definition reveals a surprising connection to optimal transport theory which we leverage to design a coreset for problems with Lipschitz costs. We validate our construction on support vector machine (SVM) training, k-means clustering, k-median clustering, and linear regression and show that we are competitive with previous coreset constructions.