Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based on Parallel CNNs for Pedestrian Detection

— Recently, deep learning methods, mostly algorithms based on Deep Convolutional Neural Networks (DCNNs) have yielded great results on pedestrian detection. Algorithms based on DCNNs spontaneously learn features in a supervised manner and are able to learn qualified high level feature representations to detect pedestrian. In this paper, we first review a number of popular DCNN-based training approaches along with their recent extensions. We then briefly describe recent algorithms based on these approaches. Also, we accentuate recent contributions and main challenges of DCNNs in detecting pedestrian. We analyze deep pedestrian detection algorithms from training approach, categorization, and DCNN model points of view, and ultimately propose a new deep architecture and training approach for deep pedestrian detection. The experimental results show that the proposed DCNN and training approach, achieve more accurate rate detection than the previously reported architectures and training approaches.
Comparison study of SMART and AHP method for paddy fertilizer recommendation in decision support system

Decision support systems are used as evidence-based decision-making tools in agriculture to provide relevant information. The implementation is to recommend the provision of fertilizer for rice plants, to support plant growth based on location specifics. This study aims to analyze the final results of two SMART and AHP methods by comparing the final results in evaluating the best alternatives. The criteria analyzed are; location, the yield of rice productivity, Leaf Color, Planting Season, Nutrient Status of P, nutrient status of K soil, Fertilizer Cage, Organic Straw Material, Use of Compost Fertilizer. For alternative solutions in the form of rice fertilizer dosage N, P, K. The results of the analysis of the two methods show that both have similarities in determining location-specific rice fertilizer recommendations, which are measured by comparing alternative final results of the two methods with recommendations from experts. Tests were carried out in ten cases with different locations. The accuracy of the two methods was 70%.
Camera-Based Egomotion in a Self-Driving Car

of the master’s thesis Author Petri Manninen Title Camera-Based Egomotion in a Self-Driving Car Degree programme Automation and Electrical Engineering Major Control, Robotics and Autonomous Systems Code of major ELEC3025 Supervisor Prof. Ville Kyrki Advisor M.Sc. Heikki Hyyti Date 14.7. 2019 Number of pages 106+1 Language English
Longer Delays in Rehearsal-based Interfaces Increase Expert Use

Rehearsal-based interfaces are designed to encourage a transition from novice to expert, but many users fail to make this transition. Most of these interfaces activate novice mode after a short delay, between 150 and 500 ms. We investigate the impact of delay time on expert usage and learning in three crowdsourced experiments. The first experiment examines an 8-item marking menu with delay times from 200 ms to 2 s. Results show longer delays increase successful expert selections. The second and third experiments generalise this result to a different rehearsal-based menu, a desktop clone of FastTap with 8 items and 15 items. Together, our results show that expert use correlates positively with increased delay time, but can increase errors since users are less risk averse. We also find imperceptible delays of 200 ms can harm long-term retention of menu items. Designers should consider longer delays in rehearsal-based interfaces to encourage a transition to expert usage.
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

Automated melanoma recognition using image processing technique from the available dermoscopic images in deep learning is difficult task because of the contrast and variation of melanoma in skin. It is mainly a non-invasive method so that it cannot contact with skin more forcefully. To overcome these disadvantages this research work proposes a method using very deep convolutional neural networks (CNNs). For more accurate classification in this method we are using FCRN and CNN with the effective training limited data. Initially, Performance of Segmentation is done using residual networks using a image from the dataset followed by Classification by neural networks to check the abnormalities in skin. In this kind of classification technique the network has more specified features from the segmented portion alone. The proposed technique is mainly evaluated on datasets and experimental results that would show the performance in histogram and PSNR ratio.
High-intensity chemotherapy improved the prognosis of patients with high-grade B-cell lymphoma

Objective High-grade B-cell lymphoma (HGBL) is highly aggressive and has a poor prognosis. Methods The clinical data of 76 patients with High-grade B-cell lymphoma treated in our lymphoma center from July 2016 to April 2020 were analyzed retrospectively. The clinical features, treatment and prognosis of patients with two types of high-grade B-cell lymphoma were compared and analyzed. Results Among 76 patients with high-grade B-cell lymphoma, 44 cases (57.9%) were high-grade B-cell lymphoma, accompanied by MYC and Bcl-2 and/or Bcl-6 rearrangement (HGBLR) patients, and 32 cases (42.1%) were HGBL, NOS patients. The bone marrow infiltration, IPI (international prognostic index), Ann Arbor stage (III/IV), extranodal disease are more likely to occur in HGBLR group (P <0.05). Survival analysis of patients showed that overall survival (OS) and progression free survival (PFS) in HGBLR group were significantly shorter than those in HGBL, NOS group (median OS: 21 months vs not reached, P=0. 022; median PFS: 5 months vs 12 months, P = 0. 001). Further analysis demonstrated that, as compared with R-CHOP regimen, patients with HGBL who received high-intensity chemotherapy regimens (DA-EPOCH-R, R-CODOX-M/IVAC and R-Hyper-CVAD) had longer OS (median OS, 16 months vs not reached, P=0. 007) and PFS (median PFS, 5 months vs 11 months, P<0.001). Moreover, mu1tivariate ana1ysis showed that high-intensity chemotherapy regimens were independent risk factors for both PFS (P =0.001, HR: 0.306, 95% CI: 0.153–0.610) and OS (P =0.004, HR: 0.262, 95% CI: 0.105–0.656) in patients with HGBL. Conclusions HGBLR patients have worse prognosis than patients with HGBL, NOS. High-intensity chemotherapy may improve the prognosis of patients with HGBL.
The swing performance Index: Developing a single-score index of golf swing rotational biomechanics quantified with 3D kinematics

Introduction Golf swing generates power through coordinated rotations of the pelvis and upper torso, which are highly consistent among professionals. Currently, golf performance is graded on handicap, length-of-shot, and clubhead-speed-at-impact. No performance indices are grading the technique of pelvic and torso rotations. As an initial step toward developing a performance index, we collected kinematic metrics of swing rotational biomechanics and hypothesized that a set of these metrics could differentiate between amateur and pro players. The aim of this study was to develop a single-score index of rotational biomechanics based on metrics that are consistent among pros and could be derived in the future using inertial measurement units (IMU). Methods Golf swing rotational biomechanics was analyzed using 3D kinematics on eleven professional (age 31.0 ± 5.9 years) and five amateur (age 28.4 ± 6.9 years) golfers. Nine kinematic metrics known to be consistent among professionals and could be obtained using IMUs were selected as candidate variables. Oversampling was used to account for dataset imbalances. All combinations, up to three metrics, were tested for suitability for factor analysis using Kaiser-Meyer-Olkin tests. Principal component analysis was performed, and the logarithm of Euclidean distance of principal components between golf swings and the average pro vector was used to classify pro vs. amateur golf swings employing logistic regression and leave-one-out cross-validation. The area under the receiver operating characteristic curve was used to determine the optimal set of kinematic metrics. Results A single-score index calculated using peak pelvic rotational velocity pre-impact, pelvic rotational velocity at impact, and peak upper torso rotational velocity post-impact demonstrated strong predictive performance to differentiate pro (mean ± SD:100 ± 10) vs. amateur (mean ± SD:82 ± 4) golfers with an AUC of 0.97 and a standardized mean difference of 2.12. Discussion In this initial analysis, an index derived from peak pelvic rotational velocity pre-impact, pelvic rotational velocity at impact, and peak upper torso rotational velocity post-impact demonstrated strong predictive performance to differentiate pro from amateur golfers. Swing Performance Index was developed using a limited sample size; future research is needed to confirm results. The Swing Performance Index aims to provide quantified feedback on swing technique to improve performance, expedite training, and prevent injuries.
Advanced cardiac resynchronisation therapy : methods of improving outcome

Aims To determine whether new pacing lead technology can be used to overcome problems with left ventricular (LV) lead placement during cardiac resynchronisation therapy (CRT). To establish whether cardiac magnetic resonance (CMR) data can be used to guide LV lead placement in real-time at CRT implant. To establish the best method of multi-site pacing. Methods We investigated the incidence of problems with phrenic nerve stimulation (PNS) and high capture thresholds at implant and at 4 and 6 month follow-up periods in 40 patients who underwent CRT with a new quadripolar lead. In 20 patients we used a pressure wire to assess the acute haemodynamic response (AHR) to pacing within different regions of the coronary sinus (CS) to determine whether problems with poor AHR can be overcome with electronic repositioning. In 23 patients we used CMR acquisition, processing, overlay and registration tools to guide LV lead placement in real-time during CRT. In 12 patients we turned on the multi-site function of a quadripolar lead, implanted temporary endocardial and epicardial pacing leads and measured the AHR whilst pacing in multiple different ways. Results Quadripolar lead technology successfully overcame problems with PNS and high capture thresholds at implant and at follow-up but not poor AHR. A CMR dyssynchrony-guided approach to LV lead placement gave an AHR comparable to the best that can be achieved anywhere and was associated with improved reverse 15 remodelling at 6 months. Endocardial pacing gave the best overall AHR but in different patients different methods of multi-site pacing were best. Conclusions New lead technology can be used to overcome some LV lead problems but not poor AHR. Real-time CMR dyssynchrony guided CRT may be better than conventional empirical LV lead placement. Endocardial pacing gives excellent overall AHR but different methods of pacing (including multi-site) may be better in individual patients.
The effect licit and illicit drugs have on trauma processing and memory

Preliminary research suggests that depressant drugs such as alcohol may represent a protective factor against developing Post-traumatic Stress Disorder (PTSD), and stimulant drugs such as methamphetamine and methylenedioxymthamphetamine (MDMA) a risk factor for the development of PTSD. Based on this preliminary research, the current study explored the effect alcohol, methamphetamine and MDMA had on trauma processing and memory. Sixty-one healthy adults participated in a double-blind, placebo controlled crossover design containing three conditions that involved the consumption of either 0.42mgt/kg of d-methamphetamine, 100mg of MDMA, 1.3 grams/kg of alcohol or placebo in a randomised order. The procedure used to measure affective processing and memory was the immediate and delayed recall and recognition performance following completion of the Lexical Decision Task (LDT) and International Affective Picture System (IAPS) Task. The results supported some of the hypotheses formulated with regards to the effect alcohol and methamphetamine would have on emotional processing and storage of traumatic stimuli. The results did not support the hypotheses formulated with regards to the effect MDMA would have on emotional processing and storage of traumatic stimuli. Alcohol alters affective reactions to traumatic stimuli, reducing arousal levels and impairing threat estimation. Alcohol also has an inhibiting effect on the memory systems responsible for the storage and retrieval of such stimuli. Methamphetamine consumption appears to increase visual memory for emotional stimuli in general, but may impair short-term memory for verbally stored trauma-related stimuli, while enhancing long-term memory for such stimuli. MDMA consumption may temporarily improve short-term memory for traumarelated stimuli, but appears to reduce long-term visual and verbal memory. The effect
Inhibitory effect of Scrophulariae Radix extract on $TNF-{\alpha},\;IL-1{\beta}$, IL-6 and Nitric Oxide production in lipopolysaccharide - activated Raw 264.7 cells

Objectives : Scrophulariae Radix (SRE) is commonly used in combination with other herbs as a nutrient and health strengthening agent, and to remove 'heat' and replenish vital essence. The water-based extract of this herb can lower blood pressure in both anesthetized and concious animals, and exhibits an anti-inflammatory activity. But, there is lack of studies regarding the effects of SRE on the immunological activities in molecular levels. The present study was conducted to evaluate the effect of SRE on the regulatory mechanism of cytokines and nitric oxide (NO) in Raw 264.7 cells. Method : After the treatment of Scrophulariae Radix methanol extract, cell viability was measured by MTT assay, NO production was monitored by measuring the nitrite content in culture medium. COX-2 and iNOS were determined by Immunoblot analysis, and levels of cytokine were analyzed by sandwich immunoassays. Results : Results provided evidence that SRE inhibited the production of nitrite and nitrate (NO), inducible nitric oxide synthase (iNOS), and interleukin-6 (IL-6), and the activation of phospholylation of inhibitor in Raw 264.7 cells activated with lipopolysaccharide (LPS). Conclusion : These findings suggest that Scrophulariae Radix can produce anti-inflammatory effect, which may playa role in adjunctive therapy in Gram-negative bacterial infections.
(E)-1-[1-(3-Chlorophenyl)ethylidene]-2-(2,4-dinitrophenyl)hydrazine

There are two crystallographically independent molecules in the asymmetric unit of the title compound, C14H11ClN4O4, with the same E conformation about the C=N double bond. The molecules are approximately planar, with a dihedral angle between the benzene rings of 10.24 (12)° in one molecule and 4.73 (12)° in the other. In both molecules, the ortho-nitro groups of the 2,4-dinitrophenyl units are coplanar to their bound benzene rings, whereas the para-nitro groups are slightly twisted. In each molecule, intramolecular N—H⋯O hydrogen bonds generate S(6) ring motifs. In the crystal, molecules are linked by weak C—H⋯O interactions into sheets parallel to the (-102) plane. These sheets are stacked by π–π interactions, with centroid–centroid distances of 3.7008 (14) and 3.7459 (14) Å. A Cl⋯O short contact [3.111 (2) Å] is observed.
Artificial neural network based computation for out-of-time-ordered correlators

Out-of-time-ordered correlators (OTOCs) are of crucial importance for studying a wide variety of fundamental phenomena in quantum physics, ranging from information scrambling to quantum chaos and many-body localization. However, apart from a few special cases, they are notoriously difficult to compute even numerically due to the exponential complexity of generic quantum many-body systems. In this paper, we introduce a machine learning approach to OTOCs based on the restricted-Boltzmann-machine architecture, which features wide applicability and could work for arbitrary-dimensional systems with massive entanglement. We show, through a concrete example involving a two-dimensional transverse field Ising model, that our method is capable of computing early-time OTOCs with respect to random pure quantum states or infinite-temperature thermal ensembles. Our results showcase the great potential for machine learning techniques in computing OTOCs, which open up numerous directions for future studies related to similar physical quantities.
V0LTpwn: Attacking x86 Processor Integrity from Software

Fault-injection attacks have been proven in the past to be a reliable way of bypassing hardware-based security measures, such as cryptographic hashes, privilege and access permission enforcement, and trusted execution environments. However, traditional fault-injection attacks require physical presence, and hence, were often considered out of scope in many real-world adversary settings. In this paper we show this assumption may no longer be justified. We present V0LTpwn, a novel hardware-oriented but software-controlled attack that affects the integrity of computation in virtually any execution mode on modern x86 processors. To the best of our knowledge, this represents the first attack on x86 integrity from software. The key idea behind our attack is to undervolt a physical core to force non-recoverable hardware faults. Under a V0LTpwn attack, CPU instructions will continue to execute with erroneous results and without crashes, allowing for exploitation. In contrast to recently presented side-channel attacks that leverage vulnerable speculative execution, V0LTpwn is not limited to information disclosure, but allows adversaries to affect execution, and hence, effectively breaks the integrity goals of modern x86 platforms. In our detailed evaluation we successfully launch software-based attacks against Intel SGX enclaves from a privileged process to demonstrate that a V0LTpwn attack can successfully change the results of computations within enclave execution across multiple CPU revisions.
Forecasting Future Sequence of Actions to Complete an Activity

Future human action forecasting from partial observations of activities is an important problem in many practical applications such as assistive robotics, video surveillance and security. We present a method to forecast actions for the unseen future of the video using a neural machine translation technique that uses encoder-decoder architecture. The input to this model is the observed RGB video, and the target is to generate the future symbolic action sequence. Unlike most methods that predict frame or clip level predictions for some unseen percentage of video, we predict the complete action sequence that is required to accomplish the activity. To cater for two types of uncertainty in the future predictions, we propose a novel loss function. We show a combination of optimal transport and future uncertainty losses help to boost results. We evaluate our model in three challenging video datasets (Charades, MPII cooking and Breakfast). We outperform other state-of-the art techniques for frame based action forecasting task by 5.06\% on average across several action forecasting setups.
An Infeasible-Start Framework for Convex Quadratic Optimization, with Application to Constraint-Reduced Interior-Point Methods

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequality-constrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) $\ell_1$-least relaxed feasible problem when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more constraints than variables ("imbalanced"). Numerical comparison with popular codes (SDPT3, SeDuMi, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems.
The impact of merging on the origin of kinematically misaligned and counter-rotating galaxies in MaNGA

Galaxy mergers and interactions are expected to play a significant role leading to offsets between gas and stellar motions in galaxies. Herein we crossmatch galaxies in MaNGA MPL-8 with the Dark Energy Spectroscopic Instrument (DESI) Legacy Surveys and identify 311 merging galaxies that have reliable measurements of the $\Delta$PA, the difference between the stellar and gas kinematic position angles to investigate the impacts of merging on gas-stellar rotation misalignments. We find that the merging fractions of misaligned galaxies (30$^\circ$ $\leqslant$ $\Delta$PA $<$150$^\circ$) are higher than that of co-rotators ($\Delta$PA $<$ 30$^\circ$) in both quiescent and star-forming galaxies. This result suggests that merging is one process to produce kinematic misalignments. The merging fraction of counter-rotators ($\Delta$PA $\leqslant$ 150$^\circ$) is lower than that of misaligned galaxies in both quiescent and star-forming galaxies, while in the latter it is likely even lower than that of co-rotators. The orbital angular momentum transfer to the spins of stars and gas during merging and the tidal feature disappearance can lead to small merging fractions in counter-rotators. Numerous new stars that inherit angular momentum from gas after merging can further lower the merging fraction of star-forming counter-rotators.
A New Implementation of the Schwarzschild Method for Constructing Observationally Driven Dynamical Models of Galaxies of All Morphological Types

We present Forstand, a new code for constructing dynamical models of galaxies with the Schwarzschild orbit-superposition method. These models are constrained by line-of-sight kinematic observations and applicable to galaxies of all morphological types, including disks and triaxial rotating bars. Our implementation has several novel and improved features, is computationally efficient, and has been made publicly available. Using mock data sets taken from N-body simulations, we demonstrate that the pattern speed of a bar can be recovered with an accuracy of 10–20%, regardless of orientation, if the 3D shape of the galaxy is known or inferred correctly.
Reinforcement Learning with Non-Markovian Rewards.

Reinforcement Learning (RL) algorithms such as DQN owe their success to Markov Decision Processes, and the fact that maximizing the sum of rewards allows using backward induction and reduce to the Bellman optimality equation. However, many real-world problems require optimization of an objective that is non-linear in cumulative rewards for which dynamic programming cannot be applied directly. For example, in a resource allocation problem, one of the objectives is to maximize long-term fairness among the users. We notice that when the function of the sum of rewards is considered, the problem loses its Markov nature. This paper addresses and formalizes the problem of optimizing a non-linear function of the long term average of rewards. We propose model-based and model-free algorithms to learn the policy, where the model-based policy is shown to achieve a regret of $\widetilde{O}\left(\sqrt{\frac{K}{T}}\right)$ for $K$ users. Further, using the fairness in cellular base-station scheduling, and queueing system scheduling as examples, the proposed algorithm is shown to significantly outperform the conventional RL approaches.