Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.
Parametric Modeling of a Magnetorheological Engine Mount Based on a Modified Polynomial Bingham Model

This paper mainly deals with the establishment of the phenomenological mechanical model for a magnetorheological(MR) engine mounts under frequency variation and magnetic variation effects. Firstly, the mounts’ reaction force is divided into three parts in this paper: a Coulomb damping force, an elastic reaction force and a viscous damping force. Then, by using correlation analysis on these forces with frequency and magnetic field, a modified polynomial Bingham parameterized model is proposed in this paper. This model takes external current and external loading frequency as the variables. And by analyzing the relationship between energy dissipation and storage caused by the external displacement excitation, then an identifying method is proposed to identify the nine parameters in the model. Based on this model, an experimental scheme is designed, and the force-displacement relationship of a typical MR mount under different working conditions is tested through the experiment in this paper. According to the proposed method in this paper, the relationship of the reaction force of a MR mount with current and external loading frequency was obtained. The experimental results show that the proposed model can correctly reflect the wide-frequency dynamic characteristics of the mounts in dynamic stiffness, lagging angle and hysteretic curve.
Effect of Respiratory Growth on the Metabolite Production and Stress Robustness of Lactobacillus casei N87 Cultivated in Cheese Whey Permeate Medium

Cheese whey permeate (WP) is a low-cost feedstock used for the production of biomass and metabolites from several lactic acid bacteria (LAB) strains. In this study, Lactobacillus casei N87 was cultivated in an optimized WP medium (WPM) to evaluate the effect of anaerobic and respiratory conditions on the growth performances (kinetics, biomass yield), consumption of sugars (lactose, galactose, glucose) and citrate, metabolite production [organic acids, volatile organic compounds (VOCs)] and stress survival (oxidative, heat, freezing, freeze-drying). The transcription of genes involved in the main pathways for pyruvate conversion was quantified through Real Time-PCR to elucidate the metabolic shifts due to respiratory state. Cultivation in WPM induced a diauxic growth in both anaerobic and respiratory conditions, and L. casei N87 effectively consumed the lactose and galactose present in WPM. Genomic information suggested that membrane PTS system and tagatose-6-P pathway mediated the metabolism of lactose and galactose in L. casei N87. Respiration did not affect specific growth rate and biomass production, but significantly altered the pyruvate conversion pathways, reducing lactate accumulation and promoting the formation of acetate, acetoin and diacetyl to ensure the redox balance. Ethanol was not produced under either cultivation. Pyruvate oxidase (pox), acetate kinase (ack), α-acetolactate decarboxylase (ald), acetolactate synthase (als) and oxaloacetate decarboxylase (oad) genes were up-regulated under respiration, while L-lactate dehydrogenase (ldh), pyruvate formate lyase (pfl), pyruvate carboxylase (pyc), and phosphate acetyltransferase (pta) were down regulated by oxygen. Transcription analysis was consistent with metabolite production, confirming that POX-ACK and ALS-ALD were the alternative pathways activated under aerobic cultivation. Respiratory growth affected the production of volatile compounds useful for the development of aroma profile in several fermented foods, and promoted the survival of L. casei N87 to oxidative stresses and long-term storage. This study confirmed that the respiration-based technology coupled with cultivation on low-cost medium may be effectively exploited to produce competitive and functional starter and/or adjunct cultures. Our results, additionally, provided further information on the activation and regulation of metabolic pathways in homofermentative LAB grown under respiratory promoting conditions.
Anomaly Detection for Container Cluster based on JointCloud Platform

In order to accurately discover container exception data of large-scale container clusters to guide the maintenance of container clusters, a new anomaly detection model for container clusters is proposed in this article. The model combines the advantages of supervised learning and unsupervised learning to accurately and efficiently label container anomaly data in a large-scale data environment. Experiments show that the labeling rate of the raw data is as high as 95.6%, and the accuracy of anomaly detection is as high as 87.0%. Simultaneously, the common five classification algorithms are used to compare the anomaly detection effect between the labeled data and the raw data, and the validity of the model is further verified.
Interpretable and Generalizable Deep Image Matching with Adaptive Convolutions

For image matching tasks, like face recognition and person re-identification, existing deep networks often focus on representation learning. However, without domain adaptation or transfer learning, the learned model is fixed as is, which is not adaptable to handle various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and result is interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of such an image matching architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. The proposed method is preliminarily validated on the person re-identification task. Through direct cross-dataset evaluation without further transfer learning, it achieves better results than many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method is proposed, which improves the performance to a further extent, resulting in state-of-the-art results in cross-dataset evaluation.
EECP-EI: energy-efficient clustering protocol based on energy intervals for wireless sensor networks

Wireless Sensor Network is comprised of sensor nodes with a limited energy supply in the form of built-in batteries. Efficient utilization of limited energy supply of sensor nodes is one of the key design issues in wireless sensor networks. Hence, energy efficiency needs to be enhanced to prolong the stability and lifetime of WSN. In this paper, we propose Energy-Efficient Clustering Protocol based on Energy Intervals (EECP-EI) for multi-level WSN. EECP-EI associates sensor nodes to clusters based on energy intervals. The cluster heads are selected based on average energy of certain energy interval and the total estimated energy of network at a particular round. The role of cluster head is rotated within the cluster to evenly distribute the energy among sensor nodes inside the cluster. The sensed data is aggregated at the cluster head to reduce the amount of data that needs to be communicated to the base station. The sensed data is communicated to the base station using hierarchy of cluster heads that are in ten-meter distance to the sending cluster head. When the cluster formation no longer remains optimal, every sensor node in the network transmits data directly to the base station. Simulations show that EECP-EI shows the improved stability period of 37.08%, 36.37%, and 19.39%, and the improved network lifetime of 6.58%, 13.13%, and 32.15% as compared to LEACH, DEEC, and DDEEC respectively.
A Refined Scaling Law for Spatially Coupled LDPC Codes Over the Binary Erasure Channel

We propose a refined scaling law to predict the finite-length performance in the waterfall region of spatially coupled low-density parity-check codes over the binary erasure channel. In particular, we introduce some improvements to the scaling law proposed by Olmos and Urbanke that result in a better agreement between the predicted and simulated frame error rate. We also show how the scaling law can be extended to predict the bit error rate performance.
UDFNET: Unsupervised Disparity Fusion with Adversarial Networks

Fusing disparity maps from different methods is an useful technique to get a refined disparity map by leveraging the complimentary advantage. We present a model for disparity fusion that uses an adversarial network, which can be trained without using ground truth disparity data. We input two initial disparity maps (from the left view) along with auxiliary information (gradient, left & right intensity image) into the generator and train the generator to output a refined disparity map registered on the left view. The refined left disparity map and left intensity image are used to reconstruct a fake right intensity image. Finally, the fake and real right intensity images (from the right stereo vision camera) are fed into a discriminator. The trained network’s architecture is effective for the fusion task (90 fps on Kitti2015). The accuracy is on par or even better than the state-of-art supervised methods. A demo video is available https://youtu.be/XTHOF3kZGsU.
Harnessing the Four Elements for Mental Health

Humans are intimately connected to nature, and our physical and mental health is influenced strongly by our environment. The “elements,” classically described in humoral theory as Fire, Water, Earth, and Air, all may impact our mental health. In a contemporary sense, these elements reflect a range of modifiable factors: UV light or heat therapy (Fire); sauna, hydrotherapy, and balneotherapy (Water); nature-based exposure therapy and horticulture (Earth); oxygen-rich/clean air exposure; and breathing techniques (Air). This theoretical scoping review paper details the emerging evidence for a range of these elements, covering epidemiological and interventional data, and provides information on how we can engage in “biophilic” activities to harness their potential benefits. Interventional examples with emerging evidentiary support include “forest-bathing,” heat therapy, sauna, light therapy, “greenspace” and “bluespace” exercise, horticulture, clay art therapy activities, and pranayamic yoga breathing exercises. Further robust research is however required to firmly validate many of these interventions, and to establish their therapeutic applications for the benefit of specific mental health disorders.
Cognitive Reserve and Its Effect in Older Adults on Retrieval of Proper Names, Logo Names and Common Nouns

Previous studies showed that high Cognitive Reserve (CR, years of education and experience and knowledge acquired in life) is correlated with language proficiency as measured with vocabulary size, verbal analogy and semantic processing. The aim of the present study is to investigate the relationship between CR and the ability in retrieving different categories of words: Proper Names, Logo Names and Common Nouns. The hypothesis is that CR contributes more in retrieving Common Nouns and Logo Names which are highly semantically interconnected, than Proper Names which are pure referring expressions. Forty-six Italian healthy older adults underwent the Montreal Cognitive Assessment (MoCA) and their performances spanned from low to high global cognitive profile. They were also administered a picture naming task for Proper Names, Logo Names and Common Nouns. Latency and Accuracy were recorded. CR was measured with the Cognitive Reserve Index (CRI) questionnaire which includes education, working time activities, and leisure time activities. Participants were significantly faster and more accurate in name retrieval when CR was high. CRI and MoCA as interaction terms predicted naming Latency with a stronger effect of CRI when the global cognitive profile was in the low range. The effect of CRI on Accuracy was lower for Proper Names than for Common Nouns and Logo Names, which did not differ from each other. Our results show that name retrieval Accuracy can be predicted by CR, significantly more in the case of Logo Names and Common Nouns than in the case of Proper Names. As Proper Names have scarce semantic interconnections and are arbitrarily assigned to unique individuals, they are not much influenced by CR. Although Logo Names are also arbitrarily assigned to their bearers, they can be conceptually categorized and thus influenced by CR. The weak relationship between Proper Names and CR might suggest a proper name task as a useful tool to detect early signs of dementia, in particular for persons with high CR.
Free-Form Video Inpainting With 3D Gated Convolution and Temporal PatchGAN

Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal. Existing patch-based methods could not handle non-repetitive structures such as faces, while directly applying image-based inpainting models to videos will result in temporal inconsistency (see this https://www.youtube.com/watch? v=BuTYfo4bO2I&list=PLnEeMdoBDCISRm0EZYFcQuaJ5ITUaaEIb&index=1). In this paper, we introduce a deep learning based free-form video inpainting model, with proposed 3D gated convolutions to tackle the uncertainty of free-form masks and a novel Temporal PatchGAN loss to enhance temporal consistency. In addition, we collect videos and design a free-form mask generation algorithm to build the free-form video inpainting (FVI) dataset for training and evaluation of video inpainting models. We demonstrate the benefits of these components and experiments on both the FaceForensics and our FVI dataset suggest that our method is superior to existing ones. Related source code, full-resolution result videos and the FVI dataset could be found on Github: https://github.com/amjltc295/Free-Form-Video-Inpainting
Surface2Volume

Users frequently seek to fabricate objects whose outer surfaces consist of regions with different surface attributes, such as color or material. Manufacturing such objects in a single piece is often challenging or even impossible. The alternative is to partition them into single-attribute volumetric parts that can be fabricated separately and then assembled to form the target object. Facilitating this approach requires partitioning the input model into parts that conform to the surface segmentation and that can be moved apart with no collisions. We propose Surface2Volume, a partition algorithm capable of producing such assemblable parts, each of which is affiliated with a single attribute, the outer surface of whose assembly conforms to the input surface geometry and segmentation. In computing the partition we strictly enforce conformity with surface segmentation and assemblability, and optimize for ease of fabrication by minimizing part count, promoting part simplicity, and simplifying assembly sequencing. We note that computing the desired partition requires solving for three types of variables: per-part assembly trajectories, partition topology, i.e. the connectivity of the interface surfaces separating the different parts, and the geometry, or location, of these interfaces. We efficiently produce the desired partitions by addressing one type of variables at a time: first computing the assembly trajectories, then determining interface topology, and finally computing interface locations that allow parts assemblability. We algorithmically identify inputs that necessitate sequential assembly, and partition these inputs gradually by computing and disassembling a subset of assemblable parts at a time. We demonstrate our method's robustness and versatility by employing it to partition a range of models with complex surface segmentations into assemblable parts. We further validate our framework via output fabrication and comparisons to alternative partition techniques.
Polarization, sign sequences and isotropic vector systems

We determine the order of magnitude of the $n$th $\ell_p$-polarization constant of the unit sphere $S^{d-1}$ for every $n,d \geq 1$ and $p>0$. For $p=2$, we prove that extremizers are isotropic vector sets, whereas for $p=1$, we show that the polarization problem is equivalent to that of maximizing the norm of signed vector sums. Finally, for $d=2$, we discuss the optimality of equally spaced configurations on the unit circle.
Adaptation of the Human Gut Microbiota Metabolic Network During the First Year After Birth

Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.
Stage of Gestation at Porcine Epidemic Diarrhea Virus Infection of Pregnant Swine Impacts Maternal Immunity and Lactogenic Immune Protection of Neonatal Suckling Piglets

During pregnancy, the maternal immune response changes dramatically over the course of gestation. This has implications for generation of lactogenic immunity and subsequent protection in suckling neonates against enteric viral infections. For example, porcine epidemic diarrhea virus (PEDV) is an alphacoronavirus that causes acute diarrhea in neonatal piglets. Due to the high virulence of PEDV and the naïve, immature immune system of neonatal suckling piglets, passive lactogenic immunity to PEDV induced during pregnancy, via the gut-mammary gland (MG)-secretory IgA (sIgA) axis, is critical for piglet protection. However, the anti-PEDV immune response during pregnancy and stage of gestation required to optimally stimulate the gut-MG-sIgA axis is undefined. We hypothesize that there is a gestational window in which non-lethal PEDV infection of pregnant gilts influences maximum lymphocyte mucosal trafficking to the MG, resulting in optimal passive lactogenic protection in suckling piglets. To understand how the stages of gestation affect maternal immune responses to PEDV, three groups of gilts were orally infected with PEDV in the first, second or third trimester. Control (mock) gilts were inoculated with medium in the third trimester. To determine if lactogenic immunity correlated with protection, all piglets were PEDV-challenged at 3–5 days postpartum. PEDV infection of gilts at different stages of gestation significantly affected multiple maternal systemic immune parameters prepartum, including cytokines, B cells, PEDV antibodies (Abs), and PEDV antibody secreting cells (ASCs). Pregnant second trimester gilts had significantly higher levels of circulating PEDV IgA and IgG Abs and ASCs and PEDV virus neutralizing (VN) Abs post PEDV infection. Coinciding with the significantly higher PEDV Ab responses in second trimester gilts, the survival rate of their PEDV-challenged piglets was 100%, compared with 87.2, 55.9, and 5.7% for first, third, and mock litters, respectively. Additionally, piglet survival positively correlated with PEDV IgA Abs and ASCs and VN Abs in milk and PEDV IgA and IgG Abs in piglet serum. Our findings have implications for gestational timing of oral attenuated PEDV maternal vaccines, whereby PEDV intestinal infection in the second trimester optimally stimulated the gut-MG-sIgA axis resulting in 100% lactogenic immune protection in suckling piglets.
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.
Solving a perturbed amplitude-based model for phase retrieval

In this paper, we propose a new algorithm for solving phase retrieval problem, i.e., the reconstruction of a signal $ \vx\in\H^n $ ($\H=\R$ or $\C$) from phaseless samples $ b_j=\abs{\langle \va_j, \vx\rangle } $, $ j=1,\ldots,m $. The proposed algorithm solves a perturbed amplitude-based model for phase retrieval and is correspondingly named as {\em Perturbed Amplitude Flow } (PAF). We prove that PAF can recover $c\vx$ ($\abs{c} = 1$) under $O(n)$ Gaussian random measurements (optimal order of measurements). Unlike several existing $O(n)$ algorithms that can be theoretically proven to recover only real signals, our algorithm works for both real and complex signals. Starting with a designed initial point, our PAF algorithm iteratively converges to the true solution at a linear rate. Besides, PAF algorithm enjoys both low computational complexity and the simplicity for implementation. The effectiveness and benefit of the proposed method can be validated by both the simulation studies and the experiment of recovering natural image.