Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an original procedure for toxic compounds multi-detectionusing an acetylcholinesterase-based biosensors

Investigations reported in this manuscript are focused on the development of an original approach for the detection of several toxic compounds, mainly aflatoxins and organophosphorus pesticides, using acetylcholinesterase (AChE)-based inhibitory electrochemical biosensors. In a first step, a new potentiometric biosensor using pH Sensitive Field-Effect Transistors (pH-FETs) as transducers was investigated for aflatoxin B1 (AFB1) determination and different elaboration and working parameters were optimized. The proposed biosensor was characterized by high operational stability and reproducibility of the signal during the work as well as during the storage. The biosensor was further evaluated for real samples analysis (wheat, sesame, walnuts and peas) and a mathematical simulation of the potentiometric biosensor response to aflatoxin B1 was proposed for the first time and validated. In a second step, a conductometric biosensor using interdigitated gold microelectrodes was developed. The sensitivity of the biosensor to aflatoxins and other classes of toxic substances, such as organophosphorus pesticides, heavy metals ions, glycoalkaloids, and surfactants, was determined. A new and original procedure, enabling the selective determination of multiclass toxins by applying successive reactivation solutions targeting either irreversible or reversible inhibitors, was finally proposed. Using this method, the electrochemical enzyme inhibitory biosensors could be applied to the analysis of aflatoxins and organophosphorus pesticides, as well as for the determination of total toxicity of the samples
Sintered Glass Monoliths as Supports for Affinity Columns

A novel stationary phase for affinity separations is presented. This material is based on sintered borosilicate glass readily available as semi-finished filter plates with defined porosity and surface area. The material shows fast binding kinetics and excellent long-term stability under real application conditions due to lacking macropores and high mechanical rigidity. The glass surface can be easily modified with standard organosilane chemistry to immobilize selective binders or other molecules used for biointeraction. In this paper, the manufacturing of the columns and their respective column holders by 3D printing is shown in detail. The model system protein A/IgG was chosen as an example to examine the properties of such monolithic columns under realistic application conditions. Several specifications, such as (dynamic) IgG capacity, pressure stability, long-term performance, productivity, non-specific binding, and peak shape, are presented. It could be shown that due to the very high separation speed, 250 mg antibody per hour and column can be collected, which surpasses the productivity of most standard columns of the same size. The total IgG capacity of the shown columns is around 4 mg (5.5 mg/mL), which is sufficient for most tasks in research laboratories. The cycle time of an IgG separation can be less than 1 min. Due to the glass material’s excellent pressure resistance, these columns are compatible with standard HPLC systems. This is usually not the case with standard affinity columns, limited to manual use or application in low-pressure systems. The use of a standard HPLC system also improves the ability for automation, which enables the purification of hundreds of cell supernatants in one day. The sharp peak shape of the elution leads to an enrichment effect, which might increase the concentration of IgG by a factor of 3. The final concentration of IgG can be around 7.5 mg/mL without the need for an additional nanofiltration step. The purity of the IgG was > 95% in one step and nearly 99% with a second polishing run.
International Journal of Management Sciences and Business Research, June-2021 ISSN (2226-8235) Vol-10, Issue 6

Customer satisfaction is one of the key objectives of service organizations because satisfaction leads the customers to be loyal and loyalty can be the source of competitive edge for the organization. Therefore, this study is designed to investigate the mediating role of customer satisfaction between service quality, CRM, perceived value, and brand loyalty in the overall service sectors of Pakistan. For this study, the targeted population consists of the different consumers who were involved in consuming services of both public and private sectors. We chose the “convenient sampling technique”. Among 302 respondents result from only 215 (71%) results were used through quantitative research methodology. The results indicate that service quality, customer relationship management, and perceived value are the key drivers in enhancing brand loyalty when customer satisfaction is mediated in their relationship. For analysis, the collection was generated through SPSS. The findings confirm highly positive correspondence among variables considers for this study.
Effect of re-drying seeds after wet treatment at 10°c on the germination and growth of eustoma grandiflorum (Raf.) shinn

This study was initiated to investigate the effects of wet treatment at 10°C (WT10°C) and subsequent re-drying treatment (RDT) on the germination and growth of Eustoma grandiflorum seeds. Both treatments, WT10°C and RDT, were applied at 10°C under dark conditions. In all the experiments, ‘King of Snow’, which is one of the major Eustoma cultivars in Japan, was used. When the total number of days exposed to WT10°C and RDT was set to 35 days, the prevention of rosettes and the promotion of growth were dependent on the number of days of WT10°C, and these effects were maintained after RDT. When RDT was applied to the seeds after WT10°C for 35 days, the growth promotion due to WT10°C was maintained regardless of the length of re-drying time from 7 to 28 days; however, the germination rate decreased with RDT. On the other hand, applying RDT after WT10°C for 14 days had no effect on the germination rate; however, the growth promotion was insufficient compared with WT10°C for 28 days. This problem was solved by carrying out a further 14 days of WT10°C after RDT. When the seeds were exposed to WT10°C for 14 days, then re-dried for 7 days, and exposed to WT10°C for a further 14 days, the same growth promotion was achieved as for seeds exposed to WT10°C for 28 days continuously. The above results suggest that the promotion of growth depends on the total number of days of WT10°C regardless of whether these days are continuous or not. Moreover, the growth promotion was maintained after RDT. The results also suggest the possibility that when both the processes of WT10°C and the RDT are carried out by seed companies, growers who have no cooling equipment can produce cut flowers using seedlings grown in the high-temperature season.
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization

Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However, recent deep learning models have moved forward from independent and identically distributed data to graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to the emergence of Graph Neural Networks (GNNs) that go beyond the models existing data selection methods are designed for. Therefore, we present GRAIN, an efficient framework that opens up a new perspective through connecting data selection in GNNs with social influence maximization. By exploiting the common patterns of GNNs, GRAIN introduces a novel feature propagation concept, a diversified influence maximization objective with novel influence and diversity functions, and a greedy algorithm with an approximation guarantee into a unified framework. Empirical studies on public datasets demonstrate that GRAIN significantly improves both the performance and efficiency of data selection (including active learning and core-set selection) for GNNs. To the best of our knowledge, this is the first attempt to bridge two largely parallel threads of research, data selection, and social influence maximization, in the setting of GNNs, paving new ways for improving data efficiency.
♫ MuSiQue: Multihop Questions via Single-hop Question Composition

Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by construction, requires proper multihop reasoning? To this end, we introduce a bottom–up approach that systematically selects composable pairs of single-hop questions that are connected, that is, where one reasoning step critically relies on information from another. This bottom–up methodology lets us explore a vast space of questions and add stringent filters as well as other mechanisms targeting connected reasoning. It provides fine-grained control over the construction process and the properties of the resulting k-hop questions. We use this methodology to create MuSiQue-Ans, a new multihop QA dataset with 25K 2–4 hop questions. Relative to existing datasets, MuSiQue-Ans is more difficult overall (3× increase in human–machine gap), and harder to cheat via disconnected reasoning (e.g., a single-hop model has a 30-point drop in F1). We further add unanswerable contrast questions to produce a more stringent dataset, MuSiQue-Full. We hope our datasets will help the NLP community develop models that perform genuine multihop reasoning.1
Point-wise posteriori phase estimation in high-precision fringe projection profilometry

In fringe projection profilometry, the high-order harmonics information of non-sinusoidal fringes will lead to errors in the phase estimation. In order to solve this problem, a point-wise posterior phase estimation (PWPPE) method based on deep learning technique is proposed in this paper. The complex nonlinear mapping relationship between the multiple gray values and the sine / cosine value of the phase is constructed by using the feedforward neural network model. After the model training, it can estimate the phase values of each pixel location, and the accuracy is higher than the point-wise least-square (PWLS) method. To further verify the effectiveness of this method, a face mask is measured, the traditional PWLS method and the proposed PWPPE method are employed, respectively. The comparison results show that the traditional method is with periodic phase errors, while the proposed PWPPE method can effectively eliminate such phase errors caused by non-sinusoidal fringes.
Macaque's Cortical Functional Connectivity Dynamics at the Onset of Propofol-Induced Anesthesia

Propofol, when administered for general anesthesia, induces oscillatory dynamic brain states that are thought to underlie the drug's pharmacological effects. Despite the elucidation of propofol's mechanisms of action at the molecular level, its effects on neural circuits and overall cortical functioning, which eventually lead to unconsciousness, are still unclear. To identify possible mechanisms, the spatial-temporal patterns of functional connectivity established among specialized cortical areas in anesthetized subjects need to be described. Within this context, the present research involved the analysis of dense sub-dural ECoG electrode array recordings from macaques under propofol anesthetic induction. Granger causality methodology was used to infer functional connectivity interactions in five physiological frequency bands serially over time, every five seconds throughout the experiment. The time-resolved networks obtained permitted us to observe the unfolding of the anesthetic induction and compare the networks obtained under different experimental conditions. About one minute after administering propofol, functional connectivity started to increase for 4-5 minutes, then decreased until the LOC was achieved. A predominant Granger causality flow from the occipital and temporal areas towards the frontal and parietal regions was also evidenced during the transition. During general anesthesia, the local connectivity of the occipital lobe increased, as did the interactions between the occipital and temporal lobes. Conversely, the functional connectivity from the frontal and parietal lobes toward the temporal and occipital regions was mainly impaired. The research is one of the first studies to describe the dynamics of the functional connectivity during the transitional state that precedes the LOC.
Estimating the Node Degree of Public Peers and Detecting Sybil Peers Based on Address Messages in the Bitcoin P2P Network

Some peers in the Bitcoin P2P network distributed a huge amount of spam IP addresses during July 2021. These spam IP addresses did not belong to actual Bitcoin peers. We found that the behavior of the spamming peers can be used to determine the number of neighbors of public peers and to ﬁnd Sybil peers (peers that have multiple addresses). We evaluate the method by running an analysis based on data collected by our monitor nodes and compare the data to a ground-truth based on few peers that we run ourselves. The node degree of public peers is found with high precision and Sybil peers are correctly classiﬁed with very high precision and high recall if the spamming peers and the monitor are connected to all Sybil addresses.
On Game Theory Using Stochastic Tail Orders

We consider a family of distributions on which natural tail orders can be constructed upon a representation of a distribution by a (single) hyperreal number. Past research revealed that the ordering can herein strongly depend on the particular model of the hyperreals, speciﬁcally the under-lying ultraﬁlter. Hence, our distribution family is constructed to order invariantly of an ultraﬁlter. Moreover, we prove that it lies dense in the set of all distributions with the (same) compact support, w.r. t. the supre-mum norm. Overall, this work presents a correction to [10, 12], in response to recent ﬁndings of [2].
Pro-UIGAN: Progressive Face Hallucination From Occluded Thumbnails

In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (<inline-formula> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula>) the occluded and tiny faces (<inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> pixels). Pro-UIGAN iteratively <xref rid="deqn1" ref-type="disp-formula">(1)</xref> estimates facial geometry priors for low-resolution (LR) faces and <xref rid="deqn2" ref-type="disp-formula">(2)</xref> acquires non-occluded HR face images under the guidance of the estimated priors. Our multi-stage hallucination network upsamples and inpaints occluded LR faces via a coarse-to-fine fashion, significantly reducing undesirable artifacts and blurriness. Specifically, we design a novel cross-modal attention module for facial priors estimation, in which an input face and its landmark features are formulated as queries and keys, respectively. Such a design encourages joint feature learning across the input facial and landmark features, and deep feature correspondences will be discovered by attention. Thus, facial appearance features and facial geometry priors are learned in a mutually beneficial manner. Extensive experiments show that our Pro-UIGAN attains visually pleasing completed HR faces, thus facilitating downstream tasks, <italic>i.e.,</italic> face alignment, face parsing, face recognition as well as expression classification.
LASOR: Learning Accurate 3D Human Pose and Shape via Synthetic Occlusion-Aware Data and Neural Mesh Rendering

A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a novel framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose estimation accuracy. The proposed method evidently outperforms Mesh Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code will be available at https://igame-lab.github. io/LASOR/.
Neuromechanical model-based control of bi-lateral ankle exoskeletons: biological joint torque and electromyogram reduction across walking conditions

—To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can effectively support large repertoires of movements. We propose a new human-machine interface to drive bilateral ankle exoskeletons during a range of “unseen” walking conditions that were not used for establishing the control interface. The proposed approach uses person-speciﬁc neuromechanical models of the human body to estimate biological ankle torques in real-time from electromyograms (EMGS) and joint angles. A low-level controller based on a disturbance observer translates biological torque estimates into exoskeleton commands. We call this ”neuromechanical model-based control” (NMBC). NMBC enabled ﬁve individuals to voluntarily control exoskeletons across two walking speeds performed at three ground elevations with no need for predeﬁned torque proﬁles, nor a priori chosen neuro- muscular reﬂex rules, or state machines as common in literature. Furthermore, a single subject case study was carried out on a dexterous moonwalk task, showing reduction in muscular effort. NMBC enabled reducing biological ankle torques as well as eight ankle muscle EMGs both within (22% for the torque; 13% for the EMG) and between walking conditions (22% for the torque; 13% for the EMG) when compared to non-assisted conditions. Torque and EMG reduction in novel walking conditions indicated the exoskeleton operated symbiotically as an exomuscle controlled by the operator’s neuromuscular system. This will open new avenues for systematic adoption of wearable robots in out-of-the-lab medical and occupational settings.
Vanadium Oxidation and Retention by Iron and Manganese Oxides

Author(s): Salvador, Amy | Advisor(s): Ying, Samantha C | Abstract: Vanadium (V) is a ubiquitous redox active transition metal found in soil systems, which can have adverse effects on the neurological development of children when ingested. Human exposure to vanadium can occur via consumption of contaminated groundwater containing V that is mobilized from soils and sediments under redox-fluctuating conditions. V is predominantly present in the more mobile V(V) form under oxidizing conditions; however, specific biological and geochemical processes contributing to vanadium redox cycling and subsequent change in its mobility into groundwater systems is not well understood. Past studies have demonstrated that permanganate is a strong oxidant of V, rapidly oxidizing V(IV) to V(V) upon contact. Therefore, we hypothesize that naturally-occurring manganese (Mn) oxides, such as birnessite, could be a potential oxidant of V in soils and sediment systems. Furthermore, iron (Fe) and manganese oxides are important components of soil systems as possible sorbents of vanadium. To understand the fate and transport of V, a Donnan reactor that simulates diffusion-limited environments within soils was used to examine V(IV) oxidation and subsequent sorption of V(V) on Mn and Fe oxides. V sorption and oxidation was monitored over time by measuring the concentration of aqueous V using ICP-OES in conjunction with Fe speciation determined by ferrozine assay. X-ray absorption spectroscopy (EXAFS and XANES analysis) and SEM-EDX imaging was used to determine changes in Mn oxide structure and speciation of adsorbed V over time. The oxidation of V resulting from contact with birnessite, with subsequent reductive dissolution of the oxidant, will affirm the potential role of V transformation from V(IV) to V(V) by Mn oxides. The results of this study demonstrate that the presence of naturally-occurring Fe and Mn oxides in soils and sediments can impact the fate and transport of V in natural systems and their role in controlling V mobilization into water resources.
A Deep Learning Approach to Predict Blood Pressure from PPG Signals

Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body’s vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals. Different from previous work, the proposed model analyzes the PPG signal in time-domain and automatically extracts the most critical features for this specific application, then uses a variation of recurrent neural networks (RNN) called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP value associated with that time window. Experimental results on two separate standard hospital datasets, yielded absolute errors mean and absolute error standard deviation for systolic and diastolic BP values outperforming prior works.
The Homeomorphism Problem For Hyperbolic Manifolds I

We give a bounded runtime solution to the homeomorphism problem for closed hyperbolic 3-manifolds. This is an algorithm which, given two triangulations of hyperbolic 3-manifolds by at most $t$ tetrahedra, decides if they represent the same hyperbolic 3-manifold with runtime bounded by \[ 2^{2^{t^{O(t)}}}. \] We do this by first finding a hyperbolic structure on each manifold given as a geometric triangulation and then comparing the two as geometric manifolds.
Learning to Control Direct Current Motor for Steering in Real Time via Reinforcement Learning

: Model free techniques have been successful at optimal control of complex systems at an expense of copious amounts of data and computation. However, it is often desired to obtain a control policy in a short period of time with minimal data use and computational burden. To this end, we make use of the NF Q algorithm for steering position control of a golf cart in both a real hardware and a simulated environment that was built from real-world interaction. The controller learns to apply a sequence of voltage signals in the presence of environmental uncertainties and inherent non-linearities that challenge the the control task. We were able to increase the rate of successful control under four minutes in simulation and under 11 minutes in real hardware.
Bipartite Graft III: General Case

This paper is a sequel of our previous paper (N. Kita: Bipartite graft II: Cathedral decomposition for combs. arXiv preprint arXiv:2101.06678, 2021). In our previous paper, a graft analogue of the Dulmage-Mendelsohn decomposition has been introduced for comb bipartite grafts. In this paper, we prove how this result can be extended for general bipartite grafts.