Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure to hurricane-related stressors and mental illness after Hurricane Katrina.

CONTEXT Uncertainty exists about the prevalence, severity, and correlates of mental disorders among people exposed to Hurricane Katrina. OBJECTIVE To estimate the prevalence and associations between DSM-IV anxiety-mood disorders and hurricane-related stressors separately among prehurricane residents of the New Orleans metropolitan area and the remainder of the areas in Alabama, Louisiana, and Mississippi affected by Katrina. DESIGN Community survey. SETTING AND PARTICIPANTS A probability sample of 1043 English-speaking prehurricane residents of the areas affected by Hurricane Katrina was administered via telephone survey between January 19 and March 31, 2006. The survey assessed hurricane-related stressors and screened for 30-day DSM-IV anxiety-mood disorders. MAIN OUTCOME MEASURES The K6 screening scale of anxiety-mood disorders and the Trauma Screening Questionnaire scale for posttraumatic stress disorder (PTSD), both calibrated against blinded structured clinical reappraisal interviews to approximate the 30-day prevalence of DSM-IV disorders. RESULTS Prehurricane residents of the New Orleans metropolitan area were estimated to have a 49.1% 30-day prevalence of any DSM-IV anxiety-mood disorder (30.3% estimated prevalence of PTSD) compared with 26.4% (12.5% PTSD) in the remainder of the sample. The vast majority of respondents reported exposure to hurricane-related stressors. Extent of stressor exposure was more strongly related to the outcomes in the New Orleans metropolitan area subsample than the remainder of the sample. The stressors most strongly related to these outcomes were physical illness/injury and physical adversity in the New Orleans metropolitan area subsample and property loss in the remainder of the sample. Sociodemographic correlates were not explained either by differential exposure or reactivity to hurricane-related stressors. CONCLUSIONS The high prevalence of DSM-IV anxiety-mood disorders, the strong associations of hurricane-related stressors with these outcomes, and the independence of sociodemographics from stressors argue that the practical problems associated with ongoing stressors are widespread and must be addressed to reduce the prevalence of mental disorders in this population.
IL-1 beta is required for IL-12 to induce production of IFN-gamma by NK cells. A role for IL-1 beta in the T cell-independent mechanism of resistance against intracellular pathogens.

Mice with the severe combined immunodeficiency (SCID) possess an IFN-gamma-dependent mechanism of resistance to the intracellular pathogens Toxoplasma gondii and Listeria monocytogenes that is dependent on IL-12-induced production of IFN-gamma by NK cells. In this report we demonstrate that IL-1 beta is required for IL-12 to stimulate production of IFN-gamma by NK cells, and that IL-1 is important in IL-12-mediated resistance to T. gondii in vivo. Stimulation of SCID mouse splenocytes with tachyzoites of T. gondii resulted in production of IFN-gamma. Addition of neutralizing Ab specific for IL-1 beta to these cultures inhibited completely the production of IFN-gamma. Similar results were obtained when LPS or L. monocytogenes were used to stimulate production of IFN-gamma by SCID mouse splenocytes. Addition of a neutralizing Ab to IL-1 alpha did not affect production of IFN-gamma by SCID mouse splenocytes stimulated with T. gondii, L. monocytogenes, or LPS. Stimulation of SCID mouse splenocytes with IL-1 beta or IL-1 alpha did not result in production of IFN-gamma but enhanced remarkably the ability of T. gondii or IL-12 to stimulate production of IFN-gamma. Furthermore, production of IFN-gamma by SCID mouse splenocytes stimulated with IL-12 plus TNF-alpha was completely ablated by anti-IL-1 beta, but not by anti-IL-1 alpha. Analysis of the culture supernatants of spleen cells from SCID mice stimulated with T. gondii or IL-12 plus TNF-alpha detected low levels of IL-1 beta; addition of a neutralizing Ab to IFN-gamma resulted in a 5- to 10-fold increase in levels of IL-1 beta. Furthermore, stimulation of SCID mouse splenocytes with IL-12, in the presence of anti-IFN-gamma, resulted in an increase in detectable levels of IL-1 beta. To determine the in vivo relevance of our in vitro data, SCID mice were infected with T. gondii and treated with IL-12 alone or IL-12 in combination with an Ab specific for the type I IL-1 receptor. This Ab reduced production of IFN-gamma by SCID mouse splenocytes stimulated with either T. gondii, LPS, L. monocytogenes, or IL-12 plus IL-1 beta. In vivo administration of this Ab antagonized significantly the ability of exogenous IL-12 to delay the time to death of SCID mice infected with T. gondii. (ABSTRACT TRUNCATED AT 400 WORDS)
Spurious Free Wide Stopband Waveguide Filter for Q-band Payload

This paper presents a high-performance, low-loss Q-band waveguide filter with a wide spurious free stopband. A 6-pole Chebyshev filter is designed with a stepped impedance waveguide resonator at 38GHz, which provides more than 50 dB rejection at 1.5 times center frequency. The filter far stopband performance is superior compared to the equivalent waveguide resonator filter with a minimal increase of insertion loss. The filter performance is stable over the temperature range from −20 deg. C to 80 deg. C with a frequency drift of 16.26 ppm.
Facial Emotion Recognition by Ensemble-DenseNet Networks

Emotions build an intrinsic and important sight of human behaviour. Facial expressions can greatly display human emotions and indicate an individual's behavioural intentions within a social situation. The detection of the state of emotion from facial expressions is becoming more diffusive with the emergence of advanced computational intelligence, In recent years, numerous types of research have been conducted on facial detection and emotion recognition using the various methods of convolutional neural networks (CNN). However, this research has been conducted to find how the DenseNet networks (DenseNet121, DenseNet169, DenseNet201) from transfer learning behave in detecting emotion from the FER2013 dataset. Our approach is to train these models separately and later build an ensemble model. Therefore, an accuracy of 71.59%, 72.01%, 72.32%, and 74.16% gain from DenseNet121, DenseNet169, DenseNet201, and ensemble model respectively. The outcome of our study is that the ensemble model (DenseNet121 + DenseNet169 + DenseNet201) can carry out better than the standalone model. This research may come in handy to solve the problem of facial expression recognition in fields 1 ike human-computer i nteraction systems and data analytics.
The DC-Link Voltage Double Line Frequency Ripple Reduction for AC-DC Converters

This paper proposes a control method of reducing the dc-link voltage double line frequency ripple in AC-DC converters. The proposed method utilizes variable duty cycle control, allowing for a reduction in the dc-link capacitance, thereby decreasing the size and cost of the converter. Additionally, the proposed control method eliminates the need for a current sensor, which is typically required in conventional harmonic input current injection techniques. This paper provides detailed mathematical analyses and design procedures for a boost PFC converter with the proposed control method. Furthermore, computer simulation and experimental results are included to validate the effectiveness and performance of the proposed control method.
Parallax-Aware Network for Light Field Salient Object Detection

Multi-view images capture scene details from different views, making them advantageous for light field salient object detection (LF SOD). However, most existing LF SOD methods neglect effective modeling and utilization of parallax information inherent in multi-view images. To address this limitation, we propose to explicitly model parallax information and conduct SOD in a parallax-aware manner, resulting in a novel network called PANet. Our model initiates by generating horizontal and vertical visual parallax maps from four border views using optical flow estimation. We then introduce a parallax-aware network, incorporating a parallax processing module (PPM) that handles both parallax quality assessment and parallax correction. In the parallax correction phase, we design a channel-based correction unit (CCU) and a graph-based correction unit (GCU) to rectify deviations of parallax features in a direction-specific manner. Additionally, a parallax supplement module (PSM) seamlessly fuses the parallax information from different directions and embeds it into the center view, thereby improving SOD accuracy. Experiments on three benchmark datasets demonstrate the superiority of our PANet model over 15 state-of-the-art models. Our code for the model will be publicly available soon.
Predicting Crop Yield in Quezon Province, Philippines Using Gaussian Process Regression: A Data-Driven Approach for Agriculture Sustainability

This study presents a predictive application for rice and corn crop yields in Quezon Province, Philippines, using advanced machine learning techniques, with a focus on the Gaussian Process Regression model. The desktop application utilizes weather parameters as inputs to forecast crop volumes, offering farmers valuable insights for optimized planting and harvesting decisions. Through rigorous evaluation, the Gaussian Process Regression model consistently outperforms other models, demonstrating its accuracy and potential for practical use in the agricultural sector. With an overall Mean Absolute Percentage Error (MAPE) of 3.39%, this tool holds promise for enhancing food security and sustainable agriculture while serving as a model for similar initiatives in other regions. Furthermore, this research highlights the crucial role of data-driven approaches in addressing climate change impacts on agriculture, offering a tangible solution for a more food-secure future. The study underscores the wider potential of data science and machine learning in addressing pressing global concerns, promoting more resilient and sustainable agricultural practices and ensuring a food-secure future for all.
Multi-Modal Domain Distribution Dilation For Text-Based Person Retrieval

The goal of text-based person retrieval is to recognize a corresponding target person from a mass of person image dataset according to a provided text query. Many previous methods are faced with a limited domain distribution (LD2) dilemma. To solve this tough problem, we propose a novel Multi-modal Domain Distribution Dilation (MD3) framework for text-based person retrieval. MD3 consists of two streams, namely an original distribution stream (ODS) and a dilated distribution stream (DDS). A Visual Distribution Dilating (VDD) module is proposed to perturb the key attributes (such as brightness, contrast, and saturation) of an input raw image. A Textual Distribution Dilating (TDD) module is also adopted to make a variation on the textual domain distribution. In order to achieve the purpose of adapting to various domain distribution in a reasonable and effective way, we adopt a mutual learning mechanism that facilitates communication and learning between two streams with diverse distribution information. We carried out a large number of experiments on the widely-used CUHK-PEDES, RSTPReid and ICFG-PEDES datasets to verify the effectiveness of MD3. Compared with the existing methods, MD3 is superior and has achieved the state-of-the-art performance.
Midlithospheric discontinuities and complex anisotropic layering in the mantle lithosphere beneath the Wyoming and Superior Provinces

The observation of widespread seismic discontinuities within Archean and Proterozoic lithosphere is intriguing, as their presence may shed light on the formation and early evolution of cratons. A clear explanation for the discontinuities, which generally manifest as a sharp decrease in seismic velocity with depth, remains elusive. Recent work has suggested that midlithospheric discontinuities (MLDs) may correspond to a sharp gradient in seismic anisotropy, produced via deformation associated with craton formation. Here we test this hypothesis beneath the Archean Superior and Wyoming Provinces using anisotropic Ps receiver function (RF) analysis to characterize the relationship between MLDs and seismic anisotropy. We computed radial and transverse component RFs for 13 long‐running seismic stations. Of these, six stations with particularly clear signals were analyzed using a harmonic regression technique. In agreement with previous studies, we find evidence for multiple MLDs within the cratonic lithosphere of the Wyoming and Superior Provinces. Our harmonic regression results reveal that (1) MLDs can be primarily explained by an isotropic negative velocity gradient, (2) multiple anisotropic boundaries exist within the lithospheric mantle, (3) the isotropic MLD and the anisotropic boundaries do not necessarily occur at the same depths, and (4) the depth and geometry of the anisotropic boundaries vary among stations. We infer that the MLD does not directly correspond to a change in anisotropy within the mantle lithosphere. Furthermore, our results reveal a surprising level of complexity within the cratonic lithospheric mantle, suggesting that the processes responsible for shaping surface geology produce similar structural complexity at depth.
Understanding replication in databases and distributed systems

Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and "neutral" framework to compare replication techniques from both communities. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.
Design of CPW Fed 4 × 4 MIMO Antenna for UWB Application

In this paper, a $4 \times 4$ MIMO antenna fed by CPW for UWB applications is demonstrated. A substrate composed of FR-4 is utilized in the design of this antenna. The UVB functionality of the antenna is made feasible by truncating the upper and bottom edges of the radiating patch. The modified step-like pattern has been carved into the lower part of the rectangular patch. The size of the antenna is $80 \times 80 \times 1.6 \mathrm{~mm}^{3}$ having large bandwidth (S11 <−10dB), extending from 2.29GHz to 10.9GHz. The antenna elements have been arranged in an orthogonal manner to increase the isolation without any additional decoupling structure. All four patches have mutual coupling which is below −15dB. The suggested antenna is capable of achieving the highest gain of 5.9dBi. MIMO antennas have a variety of performance parameters to consider. It has an Envelope Correlation Coefficient (ECC) and a Diversity gain (DG) that is lower than 0.01 and greater than 9.95dB, respectively. The design that has been planned is a suitable aspirant for UWB wireless communication systems.
Modulation of the Auditory Cortex during Speech: An MEG Study

Several behavioral and brain imaging studies have demonstrated a significant interaction between speech perception and speech production. In this study, auditory cortical responses to speech were examined during self-production and feedback alteration. Magnetic field recordings were obtained from both hemispheres in subjects who spoke while hearing controlled acoustic versions of their speech feedback via earphones. These responses were compared to recordings made while subjects listened to a tape playback of their production. The amplitude of tape playback was adjusted to match the amplitude of self-produced speech. Recordings of evoked responses to both self-produced and tape-recorded speech were obtained free of movement-related artifacts. Responses to self-produced speech were weaker than were responses to tape-recorded speech. Responses to tones were also weaker during speech production, when compared with responses to tones recorded in the presence of speech from tape playback. However, responses evoked by gated noise stimuli did not differ for recordings made during self-produced speech versus recordings made during tape-recorded speech playback. These data suggest that during speech production, the auditory cortex (1) attenuates its sensitivity and (2) modulates its activity as a function of the expected acoustic feedback.
Exploiting path diversity in datacenters using MPTCP-aware SDN

Recently, Multipath TCP (MPTCP) has been proposed as an alternative transport approach for datacenter networks. MPTCP provides the ability to split a flow into multiple paths thus providing better performance and resilience to failures. Usually, MPTCP is combined with flow-based Equal-Cost Multi-Path Routing (ECMP), which uses random hashing to split the MPTCP subflows over different paths. However, random hashing can be suboptimal as distinct subflows may end up using the same paths, while other available paths remain unutilized. In this paper, we explore an MPTCP-aware SDN controller that facilitates an alternative routing mechanism for the MPTCP subflows. The controller uses packet inspection to provide deterministic subflow assignment to paths. Using the controller, we show that MPTCP can deliver significantly improved performance when connections are not limited by the access links of hosts. To lessen the effect of throughput limitation due to access links, we also investigate the usage of multiple interfaces at the hosts. We demonstrate, using our modification of the MPTCP Linux Kernel, that using multiple subflows per pair of IP addresses can yield improved performance in multi-interface settings.
Sentimental Analysis for Social Media Platform Based on Trend Analysis

Social media platform is one of the most preferable domains of the people of the society where people are primarily using it for the communication, digital marketing, exploring things, gaining knowledge and in some occasions, it stands as the means of taking decisions based on the information gained from it. The abundant information or data on the platform may strive towards right in certain scenarios but it stands as a faulty either in one or in many situations. Sentimental analysis is a difficult task due to the rise of informal and noisy data. There has been a wide and powerful word embedding techniques with large dataset of various categorized word embedding. In this paper we are trying to implement sentiment analysis on the data gained from any social media platform for example consider twitter whereas tweets are raised based on the specific event or topic we are trying to perform sentimental analysis on that data and we are giving the conclusion either as positive or negative based on the data which exists its updates as long as the specific topic is being discussed among the people . The trend favorability is the new feature we are implementing in this paper.
Sebacinales - one thousand and one interactions with land plants.

20 I 21 II 21 III 23 IV 29 V 33 VI 35 36 36 References 36 SUMMARY: Root endophytism and mycorrhizal associations are complex derived traits in fungi that shape plant physiology. Sebacinales (Agaricomycetes, Basidiomycota) display highly diverse interactions with plants. Although early-diverging Sebacinales lineages are root endophytes and/or have saprotrophic abilities, several more derived clades harbour obligate biotrophs forming mycorrhizal associations. Sebacinales thus display transitions from saprotrophy to endophytism and to mycorrhizal nutrition within one fungal order. This review discusses the genomic traits possibly associated with these transitions. We also show how molecular ecology revealed the hyperdiversity of Sebacinales and their evolutionary diversification into two sister families: Sebacinaceae encompasses mainly ectomycorrhizal and early-diverging saprotrophic species; the second family includes endophytes and lineages that repeatedly evolved ericoid, orchid and ectomycorrhizal abilities. We propose the name Serendipitaceae for this family and, within it, we transfer to the genus Serendipita the endophytic cultivable species Piriformospora indica and P. williamsii. Such cultivable Serendipitaceae species provide excellent models for root endophytism, especially because of available genomes, genetic tractability, and broad host plant range including important crop plants and the model plant Arabidopsis thaliana. We review insights gained with endophytic Serendipitaceae species into the molecular mechanisms of endophytism and of beneficial effects on host plants, including enhanced resistance to abiotic and pathogen stress.
Mitigation of Over-Confidence in Scale-Adjusted Training for Early-Exit Networks

Dynamic networks such as a network with multiple early exits (EEs) is a promising approach for computation reduction by effectively choosing an execution path for a given image during inference. However, an EE network may suffer from significant accuracy degradation by an overconfidence problem in which an EE classifier outputs an excessively high confidence score even with wrong prediction. In this study, we show an overconfidence case when applying scaled-adjusted training (SAT), a well-known low-bit quantization method for an EE network. More precisely, SAT originally scales down a classifier layer's weight during training only, which may result in exaggerated classification logits and overconfidence during inference. To address the problem, we propose a simple yet effective method by applying weight scaling during inference with just a one-line code modification. Experiments with Resnet-18 on the ImageNet dataset demonstrate the effectiveness of our method for EE networks in both a confidence calibration metric and a Top-1 accuracy performance.
Multi-Scale Convolutional Neural Network with Sinc-Layer and Adaptive Feature Weight Distribution for Motor Imagery EEG Recognition

Electroencephalography (EEG) signal decoding is instrumental in the brain-computer interface (BCI) system. Complex characteristics of EEG pose challenges to the decoding work. In this paper, a multi-scale convolutional neural network with sinc-layer and adaptive feature weight distribution is proposed to decode motor imagery (MI) EEG signals. Sinc-layer can transform kernels of one-dimensional convolution into band-pass filters and it designs the cut-off frequency as learning parameters which can effectively decode the temporal features in the EEG signal. Then a multi-scale convolution module is established, which exploits two one-dimensional convolution and a two-dimensional convolution to extract features from EEG signals. An adaptive feature weight distribution layer is designed to assign weights to features after multi-scale convolution module, aiming to reduce the negative impact of redundant features on recognition accuracy. The results reveal that the method proposed in this paper obtains better results compared with the baseline model in the BCI IV dataset 2b and the dataset collected in our laboratory. Finally, the experimental results show that the proposed method is feasible performs well in the BCI system. Therefore, the proposed method can stimulate the development of human-computer interaction.