Miracle-dz/UniFAR-FTUs huggingface dataset:
LOCI: Privacy-aware, Device-free, Low-power Localization of Multiple Persons using IR Sensors

High accuracy and device-free indoor localization is still a holy grail to enable smart environments. With the growing privacy concerns and regulations, it is necessary to develop methods and systems that can be low-power, device-free as well as privacy-aware. While IR-based solutions fit the bill, they require many modules to be installed in the area of interest for higher accuracy, or proper planning during installation, or they may not work if the background has multiple heat-emitting objects, etc. In this paper, we propose a custom-built miniature device called LOCI that uses IR sensing. One unit of LOCI can provide three-dimensional localization at best. LOCI uses only a thermopile and a PIR sensor built within a 5x5x2 cm3 module. Since IR-based sensing is used, LOCI consumes around 80 mW. LOCI uses analog waveform from the PIR sensor with the gain of the PIR sensor dynamically controlled through software in real-time to simulate spatial diversity. LOCI proposes low-complexity techniques with sensor fusion to eliminate the noise in the background, which has not been handled in previous works even with sophisticated signal processing techniques. Since LOCI uses raw data from the thermopile, the computations are power-efficient. We present the complete design of LOCI and the proposed methodology to estimate height and location. LOCI achieves accuracies of sub-22 cm with a confidence of 0.5 and sub-35 cm with a confidence of 0.8. The best-case location accuracy is 12.5 cm. The accuracy of height estimation is within 8 cm in majority cases. LOCI can easily be extended to recognize activities.
PQJE178995 1..18

This study examined the effect of the variability of representation of durations in reference memory on temporal discrimination performance in children aged 5 and 8 years as well as in adults using a bisection (Experiment 1) and a generalization task (Experiment 2). In each task, the participants were familiarized before the blocks of tested trials with either the same referent duration values (fixed condition) or a distribution of referent duration values, with a mean equal to the referent durations used in the fixed condition and a . 20 coefficient of variation (variable condition). The results showed that the sensitivity to duration was lower in the variable than in the fixed condition in the children and, to a lesser extent, in the adults. The modelling of the data indicated that this effect was due to the increase in the variability of the representation of durations in reference memory, but also to changes in the decisional processes.
A DQ Synchronous Reference Frame Current Control for Grid Connected Photovoltaic Systems usingSingle Phase Cascaded H Bridge Multilevel Inverter

This paper projects a high performance decoupled current control using a dq synchronous reference frame for single-phase inverter. For the three-phase inverter the conversion from AC to DC with Proportional Integral controller grants to obtain steady state error for AC Voltages and currents but has a few challenges with the single-phase systems. Hence, an orthogonal pair (β) is created by shifting the phase by one quarter cycle with respect to the real component (α) which is needed for the transformation from stationary to rotating frame. The synchronous reference frame control theory helps in controlling the AC voltage by using DC signal as the reference with the proportional integrator controllers. The implementation of the control is done with two-stage converter with LCL filter for a single-phase photovoltaic system. A modified MPPT Incremental conductance algorithm along with decoupled current control helps in regulating the active and reactive power infused into the grid where the power factor is improved, the efficiency of the system is increased above 95% and total harmonic distortion for current is also reduced to3%. The results have been validated using MATLAB. KeywordsPhotovoltaic(PV), boost converter, Maximum Power Point Tracking (MPPT), Maximum Power Point (MPP), Incremental conductance (IC),dq Synchronous Reference Frame (SRF), Phase Shifted Pulse Width Modulation (PSPWM), Phase locked loop (PLL), Total Harmonic Distortion (THD).
DAPred: Dynamic Attention Location Prediction with Long-Short Term Movement Regularity

Predicting users’ future locations has become an important task in various aspects, such as ride-sharing, tourism recommendation and urban planning. However, existing methods disregard that users’ interest over next location is dynamic. The dynamic preference over next location involves two aspects: First, preference over distance is dynamic when users move; Second, preference over related terms vary on different target times. Hence, directly predicting next location with static network would result in unsatisfactory accuracies. Dynamic attention location prediction problem is still open now. We propose a multilayer recurrent attention model DAPred to solve the problem. The effectiveness of DAPred is under-pinned by the following reasons: (1) An embedding recurrent module to map history movements into latent place, which helps build the attention module for the following layers; (2) A historical attention module that detects multiple distance preference from dynamic movement history; (3) A prediction module for learning different weights on different time gaps. Compared to the state-of-art baselines, DAPred reaches 49.8% improvement in hitting ratio accuracy, and 18.5% improvement in average distance predictor error on three real-life datasets.
The A* trafﬁc process in a queue with feedback

This paper establishes the A* trafﬁc process in queueing networks. The A* trafﬁc process links open and closed networks, and explains why a queue with instantaneous Bernoulli feedback has identical limiting queue length distribution as an ordinary M/M/1 queue (with no feed-back). We show that the A* embedded queue length process is a Markov chain. The similarities and differences between the feedback queue and the ordinary queue are encapsulated in the A* process. This trafﬁc process can be used to understand open/closed product form and non-product form networks, and this could lead to better performance evaluation tools for non-product form networks.
Detection of chlorocatechol 1,2‐dioxygenase genes in proteobacteria by PCR and gene probes

In various bacterial strains belonging to the β-subdivision of proteobacteria which are capable of degrading chlorinated monoaromatic compounds, chlorocatechol 1,2-dioxygenase genes were detected by PCR and Southern hybridization. Using PCR primers derived from the conserved sequence motifs of chlorocatechol 1,2-dioxygenase genes tfdC, clcA and tcbC, PCR products of the expected size were obtained with the test strains, but not with negative control strains. The specificity of the PCR products was verified by hybridization using an oligonucleotide probe for an internal sequence motif which is evolutionarily conserved among chlorocatechol 1,2-dioxygenases and some other dioxygenases that catalyze the intradiol aromatic-ring-cleavage. Hybridization with the tfdC PCR product from the 2,4-D degradative plasmid pJP4 under stringent conditions revealed different extents of homology of the chlorocatechol 1,2-dioxygenase genes to the canonical tfdC sequence in the various strains. These findings were confirmed by the nucleotide sequence analysis of the tfdC-specific PCR products. From our results, we conclude that the PCR primer set is more suitable than the hybridization with pJP4-derived gene probes for the detection of diverse chlorocatechol 1,2-dioxygenase genes in proteobacteria.
Texture reconstruction based on underlying pattern modification that reflects user's intention

This paper presents a method for reconstructing texture patterns using deep learning. The proposed method is based on a deep neural network called pix2pix generative adversarial network (GAN) that is able to learn the conversion process between input and output images. It extends the pix2pix by adding constraints to the network to change the underline image pattern while retaining the input fine texture. Using texture images with underlying patterns and fine textures as test data, we verified the effectiveness of our modification through several computational experiments. Although the generated images can keep the input color and edge information, these images are blurred, and the input texture information cannot be reproduced in some cases. The latter problems need to be improved in the future research direction.
Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency

In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as “Low” or “High” feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.
Relationship Between Personality Disorders Scales, Pathological Personality Traits, and Six Domains of Functioning in Sample With Alcohol Use Disorder

Background Studies reveal a functional impairment in patients with personality disorders (PDs), but there is not enough information to form conclusions about this relation in patients with alcohol use disorder (AUD). The aim of this study was to investigate to what extent a personality disorders scales including pathological personality traits (PPTs) predict six domains of functioning in patients with AUD. Methods In total, 48 patients with AUD diagnosis, who were treated in the psychiatric clinics, aged 20 to 65 years [M = 37.5; SD = 12.08; 12 (25%) females and 36 (75%) males], filled out the demographic questionnaire, WHO Disability Assessment Schedule 2.0 (WHODAS 2.0, Latvian version) and Latvian Clinical Personality Inventory (LCPI v2.1.). All respondents signed the informed consent form. Results Stepwise regression analysis showed that PD Avoidant scale positively predicts impairment in Cognition and Getting along domains of functioning in AUD patients, but, on the PPTs level, it was found that Social withdrawal along with Irresponsibility and Guilt/Shame positively predict impairment in Cognition domain of functioning, and Social withdrawal along with Depressivity and Irresponsibility positively predict impairment in Getting along domain of functioning. The results of the study showed that PPT Orderliness negatively predicts impairment in Live activities domain of functioning. The PD Dependent scale and PPT Separation insecurity positively predict impairment in Participation domain of functioning. Conclusions Obtained results add deeper insight into understanding of the relationship between personality disorders scales including pathological personality traits and six domains of functioning in patients with AUD.
Commensal Obligate Anaerobic Bacteria and Health: Production, Storage, and Delivery Strategies

In the last years several human commensals have emerged from the gut microbiota studies as potential probiotics or therapeutic agents. Strains of human gut inhabitants such as Akkermansia, Bacteroides, or Faecalibacterium have shown several interesting bioactivities and are thus currently being considered as food supplements or as live biotherapeutics, as is already the case with other human commensals such as bifidobacteria. The large-scale use of these bacteria will pose many challenges and drawbacks mainly because they are quite sensitive to oxygen and/or very difficult to cultivate. This review highlights the properties of some of the most promising human commensals bacteria and summarizes the most up-to-date knowledge on their potential health effects. A comprehensive outlook on the potential strategies currently employed and/or available to produce, stabilize, and deliver these microorganisms is also presented.
Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images

We propose an automatic segmentation method of meniscus using cascaded segmentation network consisting of 2D and 3D convolutional neural networks and 2D conditional random fields in knee MR images. First, 2D segmentation network and 2D conditional random fields are performed to narrow the field of view of the medial and lateral meniscus. Second, 3D segmentation network considering local and spatial information is performed to segment the medial and lateral meniscus. The 2D segmentation network showed under-segmentation inside the meniscus. The under-segmentation was prevented after 2D CRF, but over-segmentation occurred in nearby ligaments with similar intensity. The 3D segmentation network prevented under- and over-segmentation due to considering local and spatial information, and showed the best performance. The average dice similarity coefficients of proposed method were 92.27% and 90.27% at medial and lateral meniscus, showed better results of 4.78% and 9.96% at medial meniscus, 3.94% and 9.58% at lateral meniscus compared to the segmentation method using 2D U-Net results and combined 2D U-Net and 2D CRF, respectively. The medial meniscus shows higher accuracy than the lateral meniscus due to less leakage into the collateral ligament.
Looking Outside the Box: Gaze-Pointing for Augmentative and Alternative Communication

Augmentative and Alternative Communication (AAC) provides different methods for people with disabilities to communicate. By employing eye tracking, these methods allowed the use of aided AAC autonomously, without the need for a caregiver to assist the user in choosing what they want to convey. However, these methods focus on verbal communication, which typically covers only a small portion of our daily communication. We present a system that can be integrated in modern AAC devices to allow users with impaired communication and mobility to take a picture of what is in front of them, zoom-in at a specific portion of the picture and share it with others. Such a simple solution could provide an alternative to pointing gestures, allowing users to express preferences to real objects in their environment. Other use cases of this system are discussed.
Photo spot recommendation for theme park visitors using collage images

Nowadays, social networking sites (SNS) are used for posting and seeing beautiful photographs. Although many visitors click photographs in theme parks and post them on SNS, finding appropriate photo spots using SNS is not easy because of the enormous number of posted images. This study proposes recommendation algorithms that will help visitors find appropriate photo spots. As test case, we chose Tokyo Disneyland (TDL), which posts its photographs on Twitter. Twitter characteristically merges several photographs into a single image called a collage, which shows the excursion history of visitors. Based on these histories, we apply a collaborative filtering algorithm, which recommends photo spots to visitors. Before designing a photo spot recommendation system for theme parks, we must know the intentions and preferences of theme park visitors. To acquire this knowledge, we conducted a questionnaire survey. The results suggested that male subjects prefer scenic and pose-friendly photo spots, whereas female subjects tend to choose spots that render them “instagrammable,” “beautiful,” and “cute”. Based on these findings, we categorized photo spots inside TDL and created a prototype recommendation system.
Automated detection of fundic gland polyps and hyperplastic polyps from endoscopic images using SSD

In recent years, for reducing diagnostic burdens in stomach screening, a computer aided diagnostic system (CAD system) for endoscopic stomach images is required. In our previous study, an automated polyp detection method from endoscopic images using the SSD (Single Shot MultiBox Detector) has been developed with 93.7% of detection rate. However, the detection target of this method has been limited only to fundic gland polyp. In this paper, we propose a method for automated detection and classification of two different types of polyp; fundic gland polyp (FGP) and hyperplastic polyp (HP) from endoscopic images using the SSD. In the experiment, 71 and 96 practical endoscopic images of FGP and HP were used. For training of SSD, 11210 and 5053 training images of FGP and HP were generated by data augmentation, respectively, and 20% of training images were automatically selected and used as verification images. As a result for test samples including 132 polyps (69 FGPs and 63 HPs), the detection rate for entire polyps was 96.2% (127/132), and the classification rate for two types of polyp was 88.6% (117/132). The number of false positive was only one all through the experiment.
THE INFLUENCE OF NITROGEN, PHOSPHORUS AND POTASSIUM ON THE COLD ACCLIMATION OF WINTER WHEAT (Triticum aestivum L.)

Mineral nutrition influenced the rate of cold acclimation and the level of hardiness attained by winter wheat plants grown in modified Hoagland’s solution. Plants grown in modified Hoagland’s solution at (a) one-half strength (control), (b) low potassium and (c) low phosphorus acclimated the fastest and were the hardiest. Plants grown in high and very high levels of nitrogen and a high level of phosphorus were the least hardy. For the acclimation period, significant correlation coefficients existed between the LT50 and osmotic potential, crown moisture content and crown dry weight for all nutrient regimes. However, when differences due to nutrient regimes were considered, there were no significant correlations with LT50.
Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.
An investigation of machine learning methods for prediction bus travel time of Mongolian public transportation

Many public bus services have their timetable which provide time information of arrival and/or departure at waypoints along the route. On the other hand, there are some bus services that do not have fixed time schedule, such as in Ulaanbaatar, the city of Mongolia. In this case, a lot of confusion occurs for the passengers. Prediction of bus travel time can help to provide services such as efficient scheduling for the passengers of their trips by avoiding to wait a long time. For this purpose, we investigate some machine learning methods to predict bus travel time. Concretely, for bus travel data, we employ three regression methods: linear regression (LR), support vector regression (SVR) and artificial neural network (ANN) to predict travel time. The performances of these machine learning methods are estimated and compared using conventional measures such as mean absolute error and root mean squared error. In a quantitative study, the artificial neural network is the best model having errors less than 1 minute in most cases. We also performed a qualitative study to investigate the details of our prediction results by using heatmap visualizations. Our visualization results offer easily grasping the tendency of travel time and error values.