Miracle-dz/UniFAR-FTUs huggingface dataset:
A gamified simulator and physical platform for self-driving algorithm training and validation

We identify the need for a gamified self-driving simulator where game mechanics encourage high-quality data capture, and design and apply such a simulator to collecting lane-following training data. The resulting synthetic data enables a Convolutional Neural Network (CNN) to drive an in-game vehicle. We simultaneously develop a physical test platform based on a radio-controlled vehicle and the Robotic Operating System (ROS) and successfully transfer the simulation-trained model to the physical domain without modification. The cross-platform simulator facilitates unsupervised crowdsourcing, helping to collect diverse data emulating complex, dynamic environment data, infrequent events, and edge cases. The physical platform provides a low-cost solution for validating simulation-trained models or enabling rapid transfer learning, thereby improving the safety and resilience of self-driving algorithms.
Convex Formulation of Overparameterized Deep Neural Networks

The analysis of over-parameterized neural networks has drawn significant attention in recent years. It was shown that such systems behave like convex systems under various restricted settings, such as for two-layer neural networks, and when learning is only restricted locally in the so-called neural tangent kernel space around specialized initializations. However, there is a lack of powerful theoretical techniques that can analyze fully trained deep neural networks under general conditions. This paper considers this fundamental problem by investigating such overparameterized deep neural networks when fully trained. Specifically, we characterize a deep neural network by its features’ distributions and propose a metric to intuitively measure the usefulness of feature representations. Under certain regularizers that bounds the metric, we show deep neural networks can be reformulated as a convex optimization and the system can guarantee effective feature representations in terms of the metric. Our new analysis is more consistent with empirical observations that deep neural networks are capable of learning efficient feature representations. Empirical studies confirm that predictions of our theory are consistent with results observed in practice.
Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortion characteristics are analytically derived and the spectral efficiency (SE) achieved by several receivers are explored for third-order non-linearities. Next, two deep feedforward neural networks are designed and trained to estimate the effective channels and the distortion variance at each BS antenna, which are used in signal detection. We compare the performance of the proposed methods with state-of-the-art distortion-aware and -unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise. Using the data generated by the derived effective channels for general order of non-linearities at both the BS and UEs, it is shown that the deep learning-based estimator provides better estimates of the effective channels also for non-linearities more than order three.
Skin Lesion Classification Using Deep Neural Network

This paper reports the methods and techniques we have developed for classify dermoscopic images (task 1) of the ISIC 2019 challenge dataset for skin lesion classification, our approach aims to use ensemble deep neural network with some powerful techniques to deal with unbalance data sets as its the main problem for this challenge in a move to increase the performance of CNNs model.
Detecting F-formations & Roles in Crowded Social Scenes with Wearables: Combining Proxemics & Dynamics using LSTMs

In this paper, we investigate the use of proxemics and dynamics for automatically identifying conversing groups, or so-called F-formations. More formally we aim to automatically identify whether wearable sensor data coming from 2 people is indicative of F-formation membership. We also explore the problem of jointly detecting membership and more descriptive information about the pair relating to the role they take in the conversation (i.e. speaker or listener). We jointly model the concepts of proxemics and dynamics using binary proximity and acceleration obtained through a single wearable sensor per person. We test our approaches on the publicly available MatchNMingle dataset which was collected during real-life mingling events. We find out that fusion of these two modalities performs significantly better than them independently, providing an AUC of 0.975 when data from 30-second windows are used. Furthermore, our investigation into roles detection shows that each role pair requires a different time resolution for accurate detection.
Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search

Establishing associations between the structure and the generalisation ability of deep neural networks (DNNs) is a challenging task in modern machine learning. Producing solutions to this challenge will bring progress both in the theoretical understanding of DNNs and in building new architectures efficiently. In this work, we address this challenge by developing a new complexity measure based on the concept of {Periodic Spectral Ergodicity} (PSE) originating from quantum statistical mechanics. Based on this measure a technique is devised to quantify the complexity of deep neural networks from the learned weights and traversing the network connectivity in a sequential manner, hence the term cascading PSE (cPSE), as an empirical complexity measure. This measure will capture both topological and internal neural processing complexity simultaneously. Because of this cascading approach, i.e., a symmetric divergence of PSE on the consecutive layers, it is possible to use this measure for Neural Architecture Search (NAS). We demonstrate the usefulness of this measure in practice on two sets of vision models, ResNet and VGG, and sketch the computation of cPSE for more complex network structures.
A phase I trial of AEG35156 (XIAP antisense) administered as 2-hour intravenous infusions in patients with advanced tumours

Background: The X-linked inhibitor of apoptosis (XIAP) is a potent anti-apoptotic protein. AEG35156 is a 2nd generation antisense oligonucleotide to human XIAP that enhances cancer cell apoptosis preclinically as a single agent and in combination with chemotherapeutics. Methods: The primary objective was to establish the maximum tolerated dose of AEG35156 given as a 2-hour infusion once weekly following 3 daily loading doses in week 1. Other objectives were to evaluate AEG35156 pharmacokinetics, XIAP inhibition, circulating apoptosis biomarkers and document anti-tumour activity. Results: Twenty-nine adult patients (16 M; 13 F) have been registered. Twenty-three have completed at least one 3-week treatment cycle at doses ranging from 60 to 500mg AEG35156. Treatment has been generally well tolerated, the most common toxicities have been grade I chills/pyrexia, rapidly reversible increases in aPTT at the end of infusion and mild transaminitis. One patient at 240mg had a silent myocardial infarction probably related to prophylactic aspirin withdrawal prior to starting protocol therapy. Three DLTs were observed at AEG35156 350mg: Grade 3 chills/lethargy (mesothelioma), grade 3 transaminitis (colon), transient grade 3 chills/tumour lysis syndrome/transaminitis (small lymphocytic lymphoma). A less intensive dosing protocol was deemed appropriate for future NHL patients and a protocol amendment excluding NHL patients from this study and permitting resumption in patients with solid tumors and including premedication with dexamethasone / acetaminophen was implemented. Dosing at AEG35156 500mg has been completed with no DLTs and the study is ongoing at 650mg. Pharmacokinetics were dose-proportional and linear from 60 to 500mg AEG35156. Mean plasma Cmax at 500mg was 55�g/mL. Decreases in XIAP mRNA have been observed in PBMCs at dose levels >240mg and increased levels of circulating biomarkers of apoptosis have been observed in some patients. One patient with NHL who received only a single infusion had rapid clearance of circulating tumour cells and reduction in nodal disease. Conclusions: AEG35156 can be safely delivered by 2-hour infusions and preliminary evidence of XIAP mRNA knockdown and antitumour activity in lymphoma has been observed.
NATURAL BOND ORBITAL ANALYSIS OF [FE(H2O)6]

Nature of delocalization of the electrons from the ligands to metals in the first coordination sphere of the highspin complexes [Fe(H2O)6] 2+/3+ and [Zn(H2O)6] 2+ are computationally studied using density functional theory. Among the studied complexes, natural charge transfer from H2O ligands to metal ion is found to be maximum of 1.556e in [Fe(H2O)6] 3+ and minimum of 0.621e in [Zn(H2O)6] 2+ . On the other hand, the interaction between the lone pairs of oxygen with metal ion was found to be stronger in [Zn(H2O)6] 2+ than in the complexes with second coordination sphere. Number of such strong interactions in the first coordination sphere was found to be decreased with the addition of H2O ligands in the second coordination sphere.
Editorial: Psychophysiological Contributions to Traffic Safety

1 Leiden Institute for Brain and Cognition, Leiden University Institute of Psychology, Leiden, Netherlands, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy, Neuroelectrical Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy, 4 Faculty of Behavioural and Social Sciences, Groningen University, Groningen, Netherlands, Massachusetts Institute of Technology, Center for Transportation and Logistics, Cambridge, MA, United States
Synthesis of Nano/Microsized MIL-101Cr Through Combination of Microwave Heating and Emulsion Technology for Mixed-Matrix Membranes

Nano/microsized MIL-101Cr was synthesized by microwave heating of emulsions for the use as a composite with Matrimid mixed-matrix membranes (MMM) to enhance the performance of a mixed-gas-separation. As an example, we chose CO2/CH4 separation. Although the incorporation of MIL-101Cr in MMMs is well-known, the impact of nanosized MIL-101Cr in MMMs is new and shows an improvement compared to microsized MIL-101Cr under the same conditions and mixed-gas permeation. In order to reproducibly obtain nanoMIL-101Cr microwave heating was supplemented by carrying out the reaction of chromium nitrate and 1,4-benzenedicarboxylic acid in heptane-in-water emulsions with the anionic surfactant sodium oleate as emulsifier. The use of this emulsion with the phase inversion temperature (PIT) method offered controlled nucleation and growth of nanoMIL-101 particles to an average size of <100 nm within 70 min offering high apparent BET surface areas (2,900 m2 g−1) and yields of 45%. Concerning the CO2/CH4 separation, the best result was obtained with 24 wt. % of nanoMIL-101Cr@Matrimid, leading to 32 Barrer in CO2 permeability compared to six Barrer for the neat Matrimid polymer membrane and 21 Barrer for the maximum possible 20 wt. % of microMIL-101Cr@Matrimid. The nanosized filler allowed reaching a higher loading where the permeability significantly increased above the predictions from Maxwell and free-fractional-volume modeling. These improvements for MMMs based on nanosized MIL-101Cr are promising for other gas separations.
CHANGES IN PERIPHERAL BLOOD LYMPHOCYTE POPULATIONS IN PATIENTS WITH ACUTE PANCREATITIS

We tested peripheral blood total lymphocyte and lymphocyte subsets in 32 consecutive acute pancreatitis patients (18 women and 14 man; age 33-80 years, median 63 years)) studied at admission, as well as, at 48h and on day 5 of hospitalization, using a flow cytometric analysis. Seventeen healthy subjects comparable for sex and age were studied as controls. On admission the percentage of total lymphocyte, CD3+ T cells and CD4+ T cells was significantly decreased in patients with acute pancreatitis as compared to controls but was significantly increased at 48h and on day 5 with respect to those on admission. Conversely, the percentage of CD8+ T cells in patients was significantly decreased compared to controls at all points of investigation. The percentage of CD56+ NK cells was increased in patients on admission, but significantly decreased on day 5 compared to those on admission and to controls, as well. The percentage of activated CD3+ CD25+T cells, CD4+CD25+ T cells and CD8+CD25+ T cells was significantly increased compared to controls at all points of investigation. We conclude that cell-mediated immunity was compromised in the early stage of acute pancreatitis, along with significant activation of T lymphocytes, especially T helpers cells.
Latency of 30 ms Benefits First Person Targeting Tasks More Than Refresh Rate Above 60 Hz

In competitive sports, human performance makes the difference between who wins and loses. In some competitive video games (esports), response time is an essential factor of human performance. When the athlete’s equipment (computer, input and output device) responds with lower latency, it provides a measurable advantage. In this study, we isolate latency and refresh rate by artificially increasing latency when operating at high refresh rates. Eight skilled esports athletes then perform gaming-inspired first person targeting tasks under varying conditions of refresh rate and latency, completing the tasks as quickly as possible. We show that reduced latency has a clear benefit in task completion time while increased refresh rate has relatively minor effects on performance when the inherent latency reduction present at high refresh rates is removed. Additionally, for certain tracking tasks, there is a small, but marginally significant effect from high refresh rates alone.
Why statistical power matters for probabilistic real-time: work-in-progress

The probabilistic approaches for real-time systems are based on the estimation of the probabilistic-WCET distribution. Such estimation is naturally subject to errors, caused by both systematic and estimation uncertainties. To solve this problem, statistical tests are applied on the resulting distribution to check whether such errors affect or not the output validity. In this paper, we show that the reliability of these tests depends on the statistical power that must be estimated in order to select the proper sample size. This a priori analysis is required to obtain a reliable result of the probabilistic-WCET.
The Graft-Versus-Leukemia Effect in AML

Allogeneic hematopoietic stem cell transplantation (allo-SCT) is the most established and commonly used cellular immunotherapy in cancer care. It is the most potent anti-leukemic therapy in patients with acute myeloid leukemia (AML) and is routinely used with curative intent in patients with intermediate and poor risk disease. Donor T cells, and possibly other immune cells, eliminate residual leukemia cells after prior (radio)chemotherapy. This immune-mediated response is known as graft-versus-leukemia (GvL). Donor alloimmune responses can also be directed against healthy tissues, which is known as graft-versus-host disease (GvHD). GvHD and GvL often co-occur and, therefore, a major barrier to exploiting the full immunotherapeutic benefit of donor immune cells against patient leukemia is the immunosuppression required to treat GvHD. However, curative responses to allo-SCT and GvHD do not always occur together, suggesting that these two immune responses could be de-coupled in some patients. To make further progress in successfully promoting GvL without GvHD, we must transform our limited understanding of the cellular and molecular basis of GvL and GvHD. Specifically, in most patients we do not understand the antigenic basis of immune responses in GvL and GvHD. Identification of antigens important for GvL but not GvHD, and vice versa, could impact on donor selection, allow us to track GvL immune responses and begin to specifically harness and strengthen anti-leukemic immune responses against patient AML cells, whilst minimizing the toxicity of GvHD.
miR-4666-3p and miR-329 Synergistically Suppress the Stemness of Colorectal Cancer Cells via Targeting TGF-β/Smad Pathway

Quiescent caner stem cells are identified as a subpopulation of colon cancer cells in dormant state and possess strong stem-cell like characteristics. Previously, we have identified this subpopulation in colorectal cancer (quiescent colon cancer stem cells, QCCSCs), and find QCCSCs are sensitive to the apoptotic effect of IFN-γ, which is attributed to their high IFN-γR expression levels. Microarray and bioinformatic analysis indicate miR-4666-3p is low expressed in QCCSCs and target IFN-γR1/2, which is proved by luciferase assay and western-blot. Furthermore, we find miR-4666-3p could also target TGF-βR1 to block the activation of TGF-β1/Smad pathway, therefore function as a tumor suppressor gene to inhibit the stemness of colon cancer cells. Besides, compared with QCCSCs, we find the TGF-β1 expression also decreased with the weakening of stemness properties. In terms of mechanism, our result reveal TGF-β1 is the target gene of miR-329, which is also high expressed in non-QCCSCs. Thereafter, we perform gain- and loss- function experiments to confirm the synergistic effect between miR-4666-3p and miR-329 in blocking the activation of TGF-β/Smad pathway. Finally, we evaluate the expression of both miR-4666-3p and miR-329 in 73 tumor specimens and paired normal tissue, and find both two miRNAs are related to unfavorable prognosis and advanced tumor stage in colorectal cancer. Our study revealed a novel epigenetic regulation mechanism in colon cancer stem cells, which could be exploited as a novel therapeutic strategy for cancer treatment.
A comparative study on the effectiveness of rectal advancement flap and seton placement surgeries in patients with anal fistula on the rate of recurrence, incontinence and infection

Introduction: Due to the high prevalence of anal fistulas and the recurrence of the disease following surgery, different methods have been suggested for appropriate treatment of this disease. In this study, the effects of rectal advancement flap surgery and seton placement on the recurrence rate, incontinence and wound infection were compared. Methods: In this interventional, prospective and quasi-experimental study, 45 patients with anal fistulas including 28 males and 17 females were studied for 2 years (2015-2017). Patients were divided into two groups and mucosal advancement flap and seton placement techniques were used for each of them. Descriptive analysis of data was performed by SPSS software. Results: 24 patients (54%) had seton placement surgery and 21 patients (46%) underwent rectal advancement flap surgery. Recurrence was significantly higher in patients who underwent seton placement surgery than the group that underwent rectal advancement flap surgery. Twelve patients (57%) who underwent rectal advancement flap surgery showed improvement in sphincter tone while 11 patients (45%) showed improvements with seton placement. Conclusion: The overall results of this study showed that rectal advancement flap surgery reduces recurrence and wound infection more significantly than seton placement in patients with anal fistula.
Martin Hofmann's Case for Non-Strictly Positive Data Types

We describe the breadth-first traversal algorithm by Martin Hofmann that uses a non-strictly positive data type and carry out a simple verification in an extensional setting. Termination is shown by implementing the algorithm in the strongly normalising extension of system F by Mendler-style recursion. We then analyze the same algorithm by alternative verifications first in an intensional setting using a non-strictly positive inductive definition (not just a non-strictly positive data type), and subsequently by two different algebraic reductions. The verification approaches are compared in terms of notions of simulation and should elucidate the somewhat mysterious algorithm and thus make a case for other uses of non-strictly positive data types. Except for the termination proof, which cannot be formalised in Coq, all proofs were formalised in Coq and some of the algorithms were implemented in Agda and Haskell. 2012 ACM Subject Classification Theory of computation → Logic and verification; Theory of computation → Type theory; Software and its engineering → Abstract data types; Software and its engineering → Recursion; Software and its engineering → Software verification; Software and its engineering → Coroutines
IoT System Integrating Unmanned Aerial Vehicles and LoRa Technology: A Performance Evaluation Study

Nowadays, the popularity of the unmanned aerial vehicles (UAVs) is high, and it is expected that, in the next years, the implementation of UAVs in day-to-day service will be even greater. These new implementations make use of novel technologies encompassed under the term Internet of Things (IoT). One example of these technologies is Long-Range (LoRa), classified as a Low-Power Wide-Area Network (LPWAN) with low-cost, low-power consumption, large coverage area, and the possibility of a high number of connected devices. One fundamental part of a proper UAV-based IoT service deployment is performance evaluation. However, there is no standardized methodology for assessing the performance in these scenarios. This article presents a case study of an integrated UAV-LoRa system employed for air-quality monitoring. Each UAV is equipped with a set of sensors to measure several indicators of air pollution. In addition, each UAV also incorporates an embedded LoRa node for communication purposes. Given that mobility is key when evaluating the performance of these types of systems, we study eight different mobility models, focusing on the effect that the number of UAVs and their flying speed have on system performance. Through extensive simulations, performance is evaluated via multiple quality dimensions, encompassing the whole process from data acquisition to user experience. Results show that our performance evaluation methodology allows a complete understanding of the operation, and for this specific case study, the mobility model with the best performance is Pathway because the LoRa nodes are distributed and move orderly throughout the coverage area.
The Ubiquitin Code of NODs Signaling Pathways in Health and Disease

NOD1 and NOD2 belong to the family of intracellular Nod-like receptors (NLRs) that are involved in the maintenance of tissue homeostasis and host defense against bacteria and some viruses. When sensing such microbes, those NLRs act as hitherto scaffolding proteins for activating multiple downstream inflammatory signaling pathways to promote the production of cytokines and chemokines that are ultimately important for pathogen clearance. In recent years, substantial advances have been made on our understanding of a contextual series of intracellular processes that regulate such group of innate immune molecules, including phosphorylation and ubiquitination. Specifically, we will herein discuss those recently described posttranslational modifications of either NOD1 or NOD2 that fundamentally contribute to the robustness of protective responses within specific tissues through either internal domain association or external interactions with various proteins. From a public health perspective, it is then anticipated that a better understanding how genetic mutations and deregulation of these activating and repressing mechanisms might break down in diseases would open up new therapeutic avenues for humanity.