Miracle-dz/UniFAR-FTUs huggingface dataset:
GLNet for Target Detection in Millimeter Wave Images

Millimeter wave imaging can be used to detect objects hidden under clothes and it does no harm to human body, thus it is useful for human body inspection. However, due to limitation of technology, the image is usually noisy, which leads to a great challenge for target detection without human supervision. Based on CNN (convolutional neural network), this paper proposes a Global and Local Network, namely GLNet. The GLNet consists of two convolution neural networks: a global faster and coarse region proposal network (GNet) aiming to extract potential regions containing targets and a local fine network (LNet) for further classification and position calibration. The final results combining the outputs of GNet and LNet shows a better performance than other method. The proposed model achieves 73.5% AP on target detection in our millimeter wave images dataset while keeps 35 frames per seconds for real time detection.
Tübingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction

This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural networks. Our SVM classifier obtained the top rank in both subtasks with macro-averaged F1-measures of 35.99% for English and 22.36% for Spanish data sets. Similar to a few earlier attempts, the results with neural networks were not on par with linear SVMs.
M-ary beam angle shift keying modulation for MIMO channels

This paper introduces Beam Angle Shift Keying (BASK) modulation that uses an antenna array to encode the information symbols into the angle of wave propagation (and arrival) in a Multiple-Input Multiple-Output (MIMO) wireless channel. Specifically, at the transmitter, the block of information bits is mapped into a "spatial" symbol selecting the beam or equivalently the direction of departure for the unmodulated carrier signal. Through the specular reflection paths, the carrier signal is assumed to arrive at the receiver from one direction over the duration of the spatial symbol indexing the transmitter/receiver beam. Using the receiver antenna array signals, we derive a maximum likelihood (ML) detection to differentiate between the direction-of-arrival from different beams. Mathematical analysis and simulation results are presented to demonstrate the performance of BASK in AWGN and Rayleigh fading channels. The selection of angles of arrivals is optimized to obtain the lowest symbol error rates for different modulation levels.
First direct measurements of formaldehyde flux via eddy covariance: implications for missing in-canopy formaldehyde sources

We report the first observations of formalde- hyde (HCHO) flux measured via eddy covariance, as well as HCHO concentrations and gradients, as observed by the Madison Fiber Laser-Induced Fluorescence Instrument dur- ing the BEACHON-ROCS 2010 campaign in a rural, Pon- derosa Pine forest northwest of Colorado Springs, CO. A me- dian noon upward flux of 80 µg m 2 h 1 ( 24 pptv m s 1 ) was observed with a noon range of 37 to 131 µg m 2 h 1 . Enclosure experiments were performed to determine the HCHO branch (3.5 µg m 2 h 1 ) and soil (7.3 µg m 2 h 1 ) direct emission rates in the canopy. A zero-dimensional canopy box model, used to determine the apportionment of HCHO source and sink contributions to the flux, underpre- dicted the observed HCHO flux by a factor of 6. Simulated increases in concentrations of species similar to monoter- penes resulted in poor agreement with measurements, while simulated increases in direct HCHO emissions and/or con- centrations of species similar to 2-methyl-3-buten-2-ol best improved model/measurement agreement. Given the typi- cal diurnal variability of these BVOC emissions and direct HCHO emissions, this suggests that the source of the missing flux is a process with both a strong temperature and radiation dependence.
Neuroevolutionary Planning for Robotic Control

Traditional industrial robotics is primarily focused on optimal path control. In contrast, modern robotic tasks increasingly require a greater degree of adaptability and flexibility to deal with variations in the robots environment. Moreover, new robotic tasks can be specified without precise models or target behaviors. Neuroevolution of controllers is a promising approach to such tasks: Neural networks represent nonlinear control naturally and evolutionary computation creates robust and flexible solutions. This proposal discusses completed work on evolving controllers for an object insertion task. Experiments show that, with proper input and output setup, evolution is able to discover controllers with precise behavior. However, when extending the task to require strategy and planning, finding solutions becomes hard. This proposal discusses a new evolutionary method to both discover and complete subtasks that leads to the completion of the main task. The proposed method evolves a set of action sequence networks and a planning network using an adaptation of the Enforced Subpopulations (ESP) [13] method. As a stretch goal, the proposal discusses extending this setup toward sensory-driven task specifications with the goal of eliminating the need for hand-designed objective functions.
Detection of Atrial Flutter using PRSA

Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for cardiac diseases. A study on the feasibility of automatic detection of Atrial Flutter (AFL) based on time and frequency domain features has been presented in this paper to prevent the serious heart failure by detecting it at early stage. The proposed algorithm is developed based on feature subsets of a set of statistical time-frequency-domain parameters by using phase rectified signal average (PRSA) method. Classification of the abnormality using the derived features has been performed with the help of two class clustering method by Support Vector Machine (SVM). This classifier is tested on 382 and 587 numbers of AFL and normal cardiac cycles respectively taken from MIT-BIH Arrhythmia database. Satisfactory result is obtained as the 96% sensitivity and 98% specificity is observed.
When Relationships Generate Realities : Therapeutic Communication Reconsidered

Effective therapy often seems magical. A life shattering problem is described in the quiet recesses of a chamber far removed from the site of turmoil. Questions and answers, stories good and bad, emotional outbursts, a little silence and perhaps some tears all may be present. And then, almost by miraculous intervention, there is change. The problem is transformed, seems less severe, or is possibly dissolved. Yet, we ponder, how was the result achieved? What is it about this particular configuration of events that brought about change? At least one central candidate for answering this particular form of "miracle question" is therapeutic communication. There is something about the nature of communicative interchange that engenders change. Yet, to answer in this way is scarcely sufficient. What precisely is it about such communication that precipitates transformation? What forms of communication are invited, which are proscribed; how might we be more effective?
Metformin Use Is Not Associated With B12 Deficiency or Neuropathy in Patients With Type 2 Diabetes Mellitus in Qatar

Background Metformin may lead to B12 deficiency and neuropathy. There are no published data on the prevalence of Metformin-related B12 deficiency and neuropathy in the Arabian Gulf. Aims Determine whether Metformin intake is associated with B12 deficiency and whether B12 deficiency is associated with diabetic peripheral neuropathy (DPN) and painful diabetic neuropathy. Methods Patients with type 2 diabetes mellitus (T2DM) (n = 362) attending outpatient clinics at HMC underwent assessment of B12 levels, the DN4 questionnaire, and vibration perception threshold (VPT). Results Comparing Metformin to non-Metformin users there were no differences in B12 levels, VPT, or DN4. The prevalence of B12 deficiency (B12 <133 pmol/l) was lower (P < 0.01) in Metformin (8%) compared to non-Metformin (19%) users. Patients with B12 deficiency had a comparable prevalence and severity of sensory neuropathy and painful neuropathy to patients without B12 deficiency. Conclusion Serum B12 levels were comparable between Metformin and non-Metformin users with T2DM in Qatar. T2DM patients on Metformin had a lower prevalence of B12 deficiency. Furthermore, the prevalence and severity of neuropathy and painful diabetic neuropathy were comparable between patients with and without B12 deficiency.
Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions

Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.
Routing Protocol for Life Time Enhancement in MANET

A Mobile Ad Hoc Network (MANET) consists of a set of self organizing wireless mobile nodes deployed without centralized infrastructure. Nodes communicate with each other with a limited battery supply. Several routing protocols are presented at network layer considering transmission power, residual battery power. This work aims at increasing the network life time while reducing the energy consumption, minimizing the end to end delay. The Qual Net simulator is used for simulation and the results are compared with existing Ad Hoc on Demand Distance vector (AODV) protocol. The proposed model performs par excellence in terms of energy consumption with AODV under various CBR traffic.
Effect of vitamin D3 and strontium on performance, nutrient retention, and bone mineral composition in broiler chickens

The therapeutic use of vitamin D3 and strontium has been successful for the treatment of osteoporosis in humans; however, the value of similar strategies in poultry is not clear. Male Ross broiler chicks (n = 216) were used in a 28-day broiler trial to assess effects of vitamin D3 and strontium supplementation on performance, nutrient retention, bone composition, and bone mass. Treatments included an industry-standard control diet and five additional diets where vitamin D3 was added at 5000, 20 000, or 35 000 IU/kg and strontium was added at 0 or 1200 mg/kg in a 3 × 2 factorial design. Broiler chickens supplemented with strontium and additional vitamin D3 did not increase bodyweight; however, there was a significant improvement in feed efficiency at medium levels of vitamin D3, and although not statistically significant, there was also an improvement in feed conversion ratio with strontium supplementation at normal vitamin D3 concentrations. Strontium supplementation at normal levels of vitamin D3 significantly (P < 0.01) increased calcium, phosphorus, sodium, potassium, and magnesium retention. A high concentration of vitamin D3 produced a significant improvement in apparent metabolisable energy, protein, and dry matter utilisation in the broiler chicken. Paradoxically, however, there was a detrimental effect of high vitamin D3 on bodyweight and feed efficiency, which was partially ameliorated by strontium supplementation. Strontium addition did not change bone mass but did change bone composition. High levels of vitamin D3 significantly (P < 0.01) reduced bone ash content and increased strontium content of bone. In conclusion, both vitamin D3 and strontium have the potential to positively influence the performance, mineral retention, and bone characteristics of broiler chicks. However, as with calcium and phosphorus, vitamin D3 and strontium interact and so more research is required with strontium on the optimum dose rate and its relationship with vitamin D3, calcium, phosphorus, and IGF-1 in broiler nutrition.
Multifaceted Event Analysis on Cross-Media Network Data

People are discussing about events such as shootings, protests, flight crashes and new public policies across news media and social media. Different media sources reflect different facets of the events. In this positioning paper, we point out the importance of integrating various data from the multiple media sources for understanding/analyzing the events. Here the big data’s “Variety” issue can be resolved by extending the information network representation to a "cross-media information network" but how to generate comprehensive event analysis from the network’s components? We propose a multifaceted analysis framework that models four critical facets to fully understand the events across social media network and news media network. The facets include media type for differentiating event information sources; content for discovering events from unstructured text; sentiment on quantifying user reflecting on events; and, time for turning events into dynamic evolving objects. We performed our framework on a real data set from Twitter and Google News, which creates interesting and useful event description and visualization for human inspection.
Batrachochytrium dendrobatidis in amphibians from the Po River Delta, Northern Italy

Batrachochytrium dendrobatidis is a pathogen infecting amphibians at the global scale and causing their decline, but knowledge of the distribution of this patho- gen is far from complete. We sampled amphibians from three species (Hyla interme- dia, Rana dalmatina and Pelophylax synklepton esculentus) to evaluate whether B. dendrobatidis infects amphibians in the Po River Delta Natural Park, Northern Italy. We detected the pathogen in one population of P. sk. esculentus (prevalence: 0.33). These findings expand the known distribution of B. dendrobatidis in Italy and add fur - ther concern to the conservation of amphibians in this area.
Ultra-short-term heart rate variability during resistance exercise in the elderly

Despite the appeal of ultra-short-term heart rate variability (HRV) methods of analysis applied in the clinical and research settings, the number of studies that have investigated HRV by analyzing R-R interval (RRi) recordings shorter than 5 min is still limited. Moreover, ultra-short-term HRV analysis has not been extensively validated during exercise and, currently, no indications exist for its applicability during resistance exercise. The aim of the present study was to compare ultra-short-term HRV analysis with standard short-term HRV analysis during low-intensity, dynamic, lower limb resistance exercise in healthy elderly subjects. Heart rate (HR) and RRi signals were collected from 9 healthy elderly men during discontinuous incremental resistance exercise consisting of 4-min intervals at low intensities (10, 20, 30, and 35% of 1-repetition maximum). The original RRi signals were segmented into 1-, 2-, and 3-min sections. HRV was analyzed in the time domain (root mean square of the of differences between adjacent RRi, divided by the number of RRi, minus one [RMSSD]), RRi mean value and standard deviation [SDNN] (percentage of differences between adjacent NN intervals that are greater than 50 ms [pNN50]), and by non-linear analysis (short-term RRi standard deviation [SD1] and long-term RRi standard deviation [SD2]). No significant difference was found at any exercise intensity between the results of ultra-short-term HRV analysis and the results of standard short-term HRV analysis. Furthermore, we observed excellent (0.70 to 0.89) to near-perfect (0.90 to 1.00) concordance between linear and non-linear parameters calculated over 1- and 2-min signal sections and parameters calculated over 3-min signal sections. Ultra-short-term HRV analysis appears to be a reliable surrogate of standard short-term HRV analysis during resistance exercise in healthy elderly subjects.
A state-of-the-art multi-criteria model for drug benefit-risk analysis

Drug benefit-risk analysis is based on firm clinical evidence related to various safety and efficacy outcomes, such as tolerability, treatment response, and adverse events. In this paper, we propose a new approach for constructing a supporting multi-criteria model that fully takes into account this evidence. Our approach is based on the Stochastic Multicriteria Acceptability Analysis (SMAA) methodology, which allows us to compute the typical value judgments that support a decision, to quantify uncertainty, and to compute a comprehensive benefit-risk profile. As an example, we constructed a multi-criteria model for the therapeutic group of second-generation antidepressants. We analyzed Fluoxetine, Paroxetine, Sertraline, and Venlafaxine according to relative efficacy and absolute rates of several common adverse drug reactions using meta-analytical data from the literature. Our model showed that there are clear trade-offs among the four drugs. Based on our experiences from this study, SMAA appears to be a suitable approach for quantifying trade-offs and decision uncertainty in drug benefit-risk analysis.
A Methodology to Model and Simulate Customized Realistic Anthropomorphic Robotic Hands

When building robotic hands, researchers are always face with two main issues of how to make robotic hands look human-like and how to make robotic hands function like real hands. Most existing solutions solve these issues by manually modelling the robotic hand [10-18]. However, the design processes are long, and it is difficult to duplicate the geometry shape of a human hand. To solve these two issues, this paper presents a simple and effective method that combines 3D printing and digitization techniques to create a 3D printable cable-driven robotic hand from scanning a physical hand. The method involves segmenting the 3D scanned hand model, adding joints, and converting it into a 3D printable model. Comparing to other robotic solutions, our solution retains more than 90% geometry information of a human hand1, which is attained from 3D scanning. Our modelling progress takes around 15 minutes that include 10 minutes of 3D scanning and five minutes for changing the scanned model to an articulated model by running our algorithm. Compared to other articulated modelling solutions [19, 20], our solution is compatible with an actuation system which provides our robotic hand with the ability to mimic different gestures. We have also developed a way of representing hand skeletons based on the hand anthropometric. As a proof of concept, we demonstrate our robotic hand's performance in the grasping experiments.
Feature Extraction for On-Road Vehicle Detection Based on Support Vector Machine

Inspired by alarming statistics of deaths and injuries in car accidents, this work presents the development of vehicles detection method, which is part of an Advanced Driving Assistance System. A computer vision software capable to interpret real-time events on roads, that can identify vehicles based on Support Vector Machine, was presented and evaluated by adopting two distinct techniques for features extraction. Comparisons between two feature extraction techniques (Invariant Features Transform and Histogram of Oriented Gradients) were presented, and promising results in terms of vehicles identification accuracy were obtained when a frame scan technique was integrated to the system.
Massively Parallel A* Search on a GPU: Appendix

Algorithm A1 Sequential A* graph search algorithm 1: procedure A*(s, t) . find the shortest path from s to t 2: Let Q be a priority queue . Q is the open list 3: Push(Q, s) 4: Let H be a linked hash table . H is the close list 5: H ← ∅ 6: while Q is not empty do 7: q ←Extract(Q) 8: if q = t then 9: return the path found 10: end if 11: R← Expand(q) 12: S ← R−H . deduplication (needless for A* tree search) 13: for s ∈ S do 14: Push-Back-Or-Update(Q, s, f(s)) . f is the heuristic function 15: end for 16: H ← H ∪ {q} 17: end while 18: end procedure
Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens

Automated detection of prostate cancer in digitized H and E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H and E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity.