Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust network traffic identification with unknown applications

Traffic classification is a fundamental component in advanced network management and security. Recent research has achieved certain success in the application of machine learning techniques into flow statistical feature based approach. However, most of flow statistical feature based methods classify traffic based on the assumption that all traffic flows are generated by the known applications. Considering the pervasive unknown applications in the real world environment, this assumption does not hold. In this paper, we cast unknown applications as a specific classification problem with insufficient negative training data and address it by proposing a binary classifier based framework. An iterative method is proposed to extract unknown information from a set of unlabelled traffic flows, which combines asymmetric bagging and flow correlation to guarantee the purity of extracted negatives. A binary classifier is used as an application signature which can operate on a bag of correlated flows instead of individual flows to further improve its effectiveness. We carry out a series of experiments in a real-world network traffic dataset to evaluate the proposed methods. The results show that the proposed method significantly outperforms the-state-of-art traffic classification methods under the situation of unknown applications present.
A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

Abstract Background Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications. Findings This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied. Conclusions Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.
An Energy-Efficient Antenna Impedance Detection Using Electrical Balance for Single-Step On-Chip Tunable Matching in Wearable/Implantable Applications

A low-power impedance detection method using a hybrid transformer and its analysis are presented. A balun is reused for the hybrid transformer to achieve area-efficiency. The complex impedance detection enables faster optimization on a tunable matching network and its calibration technique increases the detection accuracy. The proposed impedance detection method fabricated in a 40-nm CMOS process consumes only 0.83 mW and demonstrates the accuracy of less than 18° and 0.1 on the Γ detection up to <inline-formula><tex-math notation="LaTeX">$\vert \Gamma _{{\rm{Ant}}}\vert $</tex-math></inline-formula> of 0.5 in ISM2.4 GHz band. After single-step matching network tuning, PA power and RX noise figure are improved by up to 1.2 dB and 1.3 dB, respectively.
Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges

Robot-Assisted Therapy (RAT) has successfully been used in HRI research by including social robots in health-care interventions by virtue of their ability to engage human users both social and emotional dimensions. Research projects on this topic exist all over the globe in the USA, Europe, and Asia. All of these projects have the overall ambitious goal to increase the well-being of a vulnerable population. Typical work in RAT is performed using remote controlled robots; a technique called Wizard-of-Oz (WoZ). The robot is usually controlled, unbeknownst to the patient, by a human operator. However, WoZ has been demonstrated to not be a sustainable technique in the long-term. Providing the robots with autonomy (while remaining under the supervision of the therapist) has the potential to lighten the therapists burden, not only in the therapeutic session itself but also in longer-term diagnostic tasks. Therefore, there is a need for exploring several degrees of autonomy in social robots used in therapy. Increasing the autonomy of robots might also bring about a new set of challenges. In particular, there will be a need to answer new ethical questions regarding the use of robots with a vulnerable population, as well as a need to ensure ethically-compliant robot behaviours. Therefore, in this workshop we want to gather findings and explore which degree of autonomy might help to improve health-care interventions and how we can overcome the ethical challenges inherent to it.
3408 Fabrication of Antireflection Film using Roll Shape Glassy Carbon Mold with Moth-Eye Structures

: Antireflection (AR) structure is the most promising nano-scale application for AR film such as flat panel display, mobile phone display and so on. Ultraviolet nanoimprint lithography (UV-NIL) is a potentially powerful tool for such a nanofabrication, thus, we have established the fabrication method of AR structure film using photo-curable polymer and UV-NIL. The AR structure mold was fabricated by oxygen ion beam irradiation to roll shape glassy carbon (GC) surface. Using this roll shape mold, AR structured film was fabricated by roll to roll UV-NIL.
Contrast Time‐Density Time on Digital Subtraction Angiography Correlates With Cerebral Arteriovenous Malformation Flow Measured by Quantitative Magnetic Resonance Angiography, Angioarchitecture, and Hemorrhage

BACKGROUND Digital subtraction angiography (DSA) currently provides angioarchitectural features of cerebral arteriovenous malformations (AVMs) but its role in the hemodynamic evaluation of AVMs is poorly understood. OBJECTIVE To assess contrast time‐density time (TT) on DSA relative to AVM flow measured using quantitative magnetic resonance angiography (QMRA). METHODS Patients seen at our institution between 2007 and 2014 with a supratentorial AVM and DSA and QMRA obtained prior to any treatment were retrospectively reviewed. Regions of interest were selected on the draining veins at the point closest to the nidus. TT on DSA was defined as time needed for contrast to change image intensity from 10% to 100%, 100% to 10%, and 25% to 25%. TT was correlated to AVM total flow, angioarchitectural features, and hemorrhage. RESULTS Twenty‐eight patients (mean age 35.6 yr) were included. Six patients presented with hemorrhage. Mean AVM volume was 11.42 mL (range 0.3‐57.7 mL). Higher total AVM flow significantly correlated with shorter TT100%‐10% and TT25%‐25% (P = .02, .02, respectively). Presence of venous stenosis correlated significantly with shorter TT100%‐10% (P = .04) and TT25%‐25% (P = .04). AVMs with a single draining vein exhibited longer TT25%‐25% compared to those with multiple draining veins (P = .04). Ruptured AVMs had significantly shorter TT10%‐100% compared to unruptured AVMs (P = .05). CONCLUSION TT on DSA correlates with cerebral AVM flow measured using QMRA and with AVM angioarchitecture and hemorrhagic presentation. Thus, TT may be used to indirectly estimate AVM flow during angiography in real‐time and may also be an indicator of important AVM characteristics associated with outflow resistance and increased rupture risk, such as venous stenosis.
Individual Prognosis of Symptom Burden and Functioning in Chronic Diseases: A Generic Method Based on Patient-Reported Outcome (PRO) Measures

Background Information to the patient about the long-term prognosis of symptom burden and functioning is an integrated part of clinical practice, but relies mostly on the clinician’s personal experience. Relevant prognostic models based on patient-reported outcome (PRO) data with repeated measurements are rarely available. Objective The aim was to describe a generic method for individual long-term prognosis of symptom burden and functioning that implied few statistical presumptions, to evaluate an implementation for prognosis of depressive symptoms in stroke patients and to provide open access to a Web-based prototype of this implementation for individual use. Methods The method used to describe individual prognosis of a PRO outcome was based on the selection of a specific subcohort of patients who have the same score as the patient in question at the same time (eg, after diagnosis or treatment start), plus or minus one unit of minimal clinically important difference. This subcohort’s experienced courses were then used to provide quantitative measures of prognosis over time. A cohort of 1404 stroke patients provided data for a simulation study and a prototype for individual use. Members of the cohort answered questionnaires every 6 months for 3.5 years. Depressive symptoms were assessed by the Hospital Anxiety and Depression Scale (HADS) and a single item from the SF-12 (MH4) health survey. Four approaches were compared in a simulation study in which the prognosis for each member of the cohort was individually assessed. Results The mean standard deviations were 40% to 70% higher in simulated scores. Mean errors were close to zero, and mean absolute errors were between 0.46 and 0.66 SD in the four approaches. An approach in which missing HADS scores were estimated from the single-item SF-12 MH4 performed marginally better than methods restricted to questionnaires with a genuine HADS score, which indicates that data collected with shorter questionnaires (eg, in clinical practice) may be used together with longer versions with the full scale, given that the design includes at least two simultaneous measurements of the full scale and the surrogate measure. Conclusions This is the first description and implementation of a nonparametric method for individual PRO-based prognosis. Given that relevant PRO data have been collected longitudinally, the method may be applied to other patient groups and to any outcome related to symptom burden and functioning. This initial implementation has been deliberately made simple, and further elaborations as well as the usability and clinical validity of the method will be scrutinized in clinical practice. An implementation of the prototype is available online at www.prognosis. dk.
Comments on: Microvascular and macrovascular complications in diabetes mellitus: Distinct or continuum?

1. De Sanctis V, Elsedfy H, Soliman AT, Elhakim IZ, Kattamis C, Soliman NA, et al. Clinical and biochemical data of adult thalassemia major patients (TM) with multiple endocrine complications (MEC) versus TM patients with normal endocrine functions: A long-term retrospective study (40 years) in a tertiary care center in Italy. Mediterr J Hematol Infect Dis 2016;8:e2016022. 2. Wang C, Tso SC, Todd D. Hypogonadotropic hypogonadism in severe beta-thalassemia: Effect of chelation and pulsatile gonadotropin-releasing hormone therapy. J Clin Endocrinol Metab 1989;68:511-6. 3. De Sanctis V, Elsedfy H, Soliman AT, Elhakim IZ, Soliman NA, Elalaily R, et al. Endocrine profile of β-thalassemia major patients followed from childhood to advanced adulthood in a tertiary care center. Indian J Endocr Metab 2016;20:451-9. 4. De Sanctis V, Soliman AT. ICET-A an opportunity for improving thalassemia management. J Blood Disord 2014;1:1-2. 5. Aydin B, Yaprak I, Akarsu D, Okten N, Ulgen M. Psychosocial aspects and psychiatric disorders in children with thalassemia major. Acta Paediatr Jpn 1997;39:354-7.
The Effects of Calcium, Vitamins D and K co-Supplementation on Markers of Insulin Metabolism and Lipid Profiles in Vitamin D-Deficient Women with Polycystic Ovary Syndrome.

Data on the effects of calcium, vitamins D and K co-supplementation on markers of insulin metabolism and lipid profiles among vitamin D-deficient women with polycystic ovary syndrome (PCOS) are scarce. This study was done to determine the effects of calcium, vitamins D and K co-supplementation on markers of insulin metabolism and lipid profiles in vitamin D-deficient women with PCOS. This randomized double-blind, placebo-controlled trial was conducted among 55 vitamin D-deficient women diagnosed with PCOS aged 18-40 years old. Subjects were randomly assigned into 2 groups to intake either 500 mg calcium, 200 IU vitamin D and 90 µg vitamin K supplements (n=28) or placebo (n=27) twice a day for 8 weeks. After the 8-week intervention, compared with the placebo, joint calcium, vitamins D and K supplementation resulted in significant decreases in serum insulin concentrations (-1.9±3.5 vs. +1.8±6.6 µIU/mL, P=0.01), homeostasis model of assessment-estimated insulin resistance (-0.4±0.7 vs. +0.4±1.4, P=0.01), homeostasis model of assessment-estimated b cell function (-7.9±14.7 vs. +7.0±30.3, P=0.02) and a significant increase in quantitative insulin sensitivity check index (+0.01±0.01 vs. -0.008±0.03, P=0.01). In addition, significant decreases in serum triglycerides (-23.4±71.3 vs. +9.9±39.5 mg/dL, P=0.03) and VLDL-cholesterol levels (-4.7±14.3 vs. +2.0±7.9 mg/dL, P=0.03) was observed following supplementation with combined calcium, vitamins D and K compared with the placebo. Overall, calcium, vitamins D and K co-supplementation for 8 weeks among vitamin D-deficient women with PCOS had beneficial effects on markers of insulin metabolism, serum triglycerides and VLDL-cholesterol levels.
Large Margin Deep Neural Networks: Theory and Algorithms

Deep neural networks (DNN) have achieved huge practical success in recent years. However, its theoretical properties (in particular generalization ability) are not yet very clear, since existing error bounds for neural networks cannot be directly used to explain the statistical behaviors of practically adopted DNN models (which are multi-class in their nature and may contain convolutional layers). To tackle the challenge, we derive a new margin bound for DNN in this paper, in which the expected 0-1 error of a DNN model is upper bounded by its empirical margin error plus a Rademacher Average based capacity term. This new bound is very general and is consistent with the empirical behaviors of DNN models observed in our experiments. According to the new bound, minimizing the empirical margin error can effectively improve the test performance of DNN. We therefore propose large margin DNN algorithms, which impose margin penalty terms to the cross entropy loss of DNN, so as to reduce the margin error during the training process. Experimental results show that the proposed algorithms can achieve significantly smaller empirical margin errors, as well as better test performances than the standard DNN algorithm.
Quantification of Lactate Dehydrogenase for Cell Viability Testing Using Cell Lines and Primary Cultured Astrocytes

Drug discovery heavily relies on cell viability studies to assess the potential toxicity of drug candidates. L‐Lactate dehydrogenase (LDH) is a cytoplasmic enzyme that catalyzes the concomitant interconversions of pyruvate to L‐lactate and NADH to NAD+ during glycolysis, and the reverse reactions during the Cori cycle. In response to cellular damage, induced by endogenous cellular mechanisms or as a result of exogenously applied insults, LDH is released from the cytoplasm into the extracellular environment. Its stability in cell culture medium makes it a well‐suited correlate for the presence of damage and toxicity in tissues and cells. We herein present protocols for a reproducible and validated LDH assay optimized for several cell types. In contrast to commercially available LDH assays, often associated with proprietary formulations and high cost, our protocols provide ample opportunities for experiment‐specific optimization with low variability and cost. © 2017 by John Wiley & Sons, Inc.
Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill

Background With a large-scale electronic health record repository, it is feasible to build a customized patient outcome prediction model specifically for a given patient. This approach involves identifying past patients who are similar to the present patient and using their data to train a personalized predictive model. Our previous work investigated a cosine-similarity patient similarity metric (PSM) for such patient-specific predictive modeling. Objective The objective of the study is to investigate the random forest (RF) proximity measure as a PSM in the context of personalized mortality prediction for intensive care unit (ICU) patients. Methods A total of 17,152 ICU admissions were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A number of predictor variables were extracted from the first 24 hours in the ICU. Outcome to be predicted was 30-day mortality. A patient-specific predictive model was trained for each ICU admission using an RF PSM inspired by the RF proximity measure. Death counting, logistic regression, decision tree, and RF models were studied with a hard threshold applied to RF PSM values to only include the M most similar patients in model training, where M was varied. In addition, case-specific random forests (CSRFs), which uses RF proximity for weighted bootstrapping, were trained. Results Compared to our previous study that investigated a cosine similarity PSM, the RF PSM resulted in superior or comparable predictive performance. RF and CSRF exhibited the best performances (in terms of mean area under the receiver operating characteristic curve [95% confidence interval], RF: 0.839 [0.835-0.844]; CSRF: 0.832 [0.821-0.843]). RF and CSRF did not benefit from personalization via the use of the RF PSM, while the other models did. Conclusions The RF PSM led to good mortality prediction performance for several predictive models, although it failed to induce improved performance in RF and CSRF. The distinction between predictor and similarity variables is an important issue arising from the present study. RFs present a promising method for patient-specific outcome prediction.
Nanoscale virus biosensors: state of the art

: Since the beginning of the new millennium, viruses have shown huge epidemiological and pandemic potential: severe acute respiratory syndrome (SARS) in 2002, pandemic swine flu in 2009, and last but not least the West African Ebola outbreak in 2014. The occurrence and spread of the new virus in pandemic dimension poses a threat to the health and lives of 7 billion people worldwide. There is a growing urgency for highly sensitive and selective detection techniques, usable for a wide number of applications, including disease diagnosis, pharmaceutical research, agriculture, as well as preventive measures. Nanobiosensors represent a new promising tool for virus detection. This review gives a brief survey of the issue of viral detection, comprising diagnostics of target structure of viruses such as nucleic acids or proteins. This review covers different detection principles, methods of fabrication, and applications of virus biosensors.
Silicate Based Glass Formulations for Immobilization of U.S. Defense Wastes Using Cold Crucible Induction Melters

iii Acknowledgments v Acronyms and Abbreviations vii 1.0 Introduction and Background 1 2.0 Quality Assurance 2 3.0 Previous Silicate Glass Formulation Efforts for CCIM Processing of U.S. Tank Wastes 3 3.1 Hanford TWRS-P HLW 3 3.2 Hanford C-106/AY-102 Formulation for Crystal Tolerant Melters 4 3.3 Idaho Sodium Bearing Waste 6 3.4 Savannah River Sludge Batch 2 Glasses 8 3.5 Savannah River Sludge Batch 3 Glasses 9 3.6 Savannah River Sludge Batch 4 Glasses 11 3.6. 1 Frit 503-R4 (SIA Radon) 12 3.6. 2 Frit 503-R3 (KHNP) 13 3.6. 3 Frit 503-R6 (CEA) 13 3.7 Hanford AZ-101 High Level Waste Glass 14 3.8 Hanford AN-105 Low-Activity Waste Glass Formulation 16 3.9 Hanford HLW Waste Cluster Groups 18 3.10 Summary of Key Processing Related Properties for CCIM Test Glasses 19 4.0 Glass Formulation and Testing for Hanford High-Alumina and High-Iron HLWs 23 4.1 Selection of Wastes 23 4.2 Experimental Methods 25 4.2. 1 Glass Fabrication and Canister Centerline Cooling Treatment 25 4.2. 2 Composition Analyses 26 4.2. 3 Product Consistency Test 26 4.2. 4 Viscosity 26 4.2. 5 Electrical Conductivity 27 4.2. 6 Equilibrium Crystal Fraction and Crystal Identification After Canister Centerline Cooling 27 4.3 Glass Formulation Approaches 28 4.4 High-Al2O3 Glass Formulation and Testing for C-102 29 4.5 High-Fe2O3 Glass Formulation and Testing for 244-TX 39 4.6 Summary of Glass Formulation Results for C-102 and 244-TX 45 5.0 Evaluation of Glass Formulations for Hanford Tank Waste Using CCIM 46 5.1 High-Level Waste Melter Study Report 47 5.2 Evaluation of Hanford HLW Vitrification Process Alternatives 47 5.3 River Protection Project System Plan (Current Baseline) 48
Mining the Dispatch under Supervision : Using Casualty Counts to Guide Topics from the Richmond Daily Dispatch Corpus

Large digitized text collections are of immense potential value to historians but are notoriously difﬁcult to digest, given the near-impossibility of reading the entirety of their content within a reasonable amount of time. Making sense of such collections on the basis of searching with keywords is usually inadequate because it is often hard to know beforehand what the appropriate keywords ought to be. However, while large corpora present these challenges for close reading, digital techniques promise new avenues for engaging with text through “distant reading” (Moretti, 2005). On recent interest has been topic modeling, as exempliﬁed by Latent Dirichlet Allocation (LDA) Blei et al. (2003), which has recently been applied to civil war texts Nelson (2010). Topic modeling dis-covers coherent topic threads that wind through large corpora, enabling readers to discover connections and patterns would otherwise be lost to the data’s volume. While LDA allows researchers to explore corpora in an undirected fashion, it does not enable directed research to focus on speciﬁc themes or issues. In this work, we employ supervised Latent Dirichlet Allocation (SLDA), which allows us to discover cross-cutting topics, like LDA, but with respect to a particular subject of interest. In this work, we explore using casualty ﬁgures allowing us to explore texts viewed through the lens of military sucess (or failure).