Miracle-dz/UniFAR-FTUs huggingface dataset:
SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map

Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-world scenarios, leading to missed detections or false positive detections in loop closure. To address these issues, we first propose a semantic loop detection method based on quadric-level object map topology, which represents scenes through the topological graph of quadric-level objects and achieves accurate loop closure at a wide field of view by comparing differences in the topological graphs. Next, to solve the data association problem between frame and map in loop closure, we propose an object data association method based on multi-level verification, which can associate 2D semantic features of the current frame with 3D object landmarks of the map. Finally, we integrate these two methods into a complete object-aware SLAM system. Qualitative experiments and ablation studies demonstrate the effectiveness and robustness of the proposed object-level data association algorithm. Quantitative experiments show that our semantic loop closure method outperforms existing state-of-the-art methods in terms of precision, recall, and localization accuracy metrics.
Integration of Genome and Chromatin Structure with Gene Expression Profiles To Predict c-MYC Recognition Site Binding and Function

The MYC genes encode nuclear sequence specific–binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches—even experimentally confirmed sites—are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.
Precursor Frequency and Competition Dictate the HLA-A2–Restricted CD8+ T Cell Responses to Influenza A Infection and Vaccination in HLA-A2.1 Transgenic Mice

The human HLA-A2–restricted CD8+ T cell response to influenza A virus (IAV) is largely directed against the matrix protein-derived M158–66 epitope and represents an archetypal example of CD8+ T cell immunodominance. In this study, we examined the CD8+ T cell hierarchy to M158–66 and two subdominant IAV-specific epitopes: NS1122–130 and PA46–55 in HLA-A2+ human subjects and HLA-A2.1 transgenic (HHD) mice. Using epitope-based lipopeptides, we show that the CD8+ T cell hierarchy induced by IAV infection could also be induced by lipopeptide vaccination in a context outside of viral infection when the Ag load is equalized. In the HHD HLA-A2.1 mouse model, we show that the naive T cell precursor frequencies, and competition at the Ag presentation level, can predict the IAV-specific CD8+ T cell hierarchy. Immunization of mice with subdominant epitopes alone was unable to overcome the dominance of the M158–66–specific response in the face of IAV challenge; however, a multiepitope vaccination strategy was most effective at generating a broad and multispecific response to infection.
DeepCluE: Enhanced Deep Clustering via Multi-Layer Ensembles in Neural Networks

Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of representation, e.g., by performing the $K$-means clustering on the last fully-connected layer or by associating some clustering loss to a specific layer, which neglect the possibilities of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In view of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. In particular, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which an off-the-shelf ensemble clustering process is conducted. Specifically, a set of diversified base clusterings are generated from the multi-layer representations via a highly efficient clusterer. Then the reliability of clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the set of base clusterings are further formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final consensus clustering can be obtained. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
Enhancing Low-Density EEG-Based Brain-Computer Interfacing With Similarity-Keeping Knowledge Distillation

Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. In recent years, the emergence of lightweight and portable EEG wearable devices with low-density montages has significantly increased the convenience and usability of BCI applications. However, the use of low-density EEG montages often leads to a loss in EEG decoding performance due to the reduced number of electrodes and limited coverage of scalp regions. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that allows a low-density EEG student model to acquire the inter-sample similarity from a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy for low-density EEG data and outperforms other KD methods across various model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework facilitating the practicality of low-density EEG-based BCI in real-world applications.
Future Generation Computer Systems

energy consumption to solve the problem how many data packets each provider transfers. Thirdly, we model the file reconstruction problem of collaboration demanders as the shortest Hamiltonian path problem and illustrate the file reconstruction process. Moreover, we develop a best-effort distributed greedy algorithm framework to find the shortest file reconstruction path. Importantly, numerical results demonstrate that the proposed mechanism can greatly reduce the energy consumption of each device with little or no increase in transmission delay.
Femtosecond precision via RF Backplane in MTCA crates

Modern accelerator control systems based on MTCA.4 platform require femtosecond synchronization of RTM cards containing analog front-end electronics for high-speed digital signal processing AMC cards. The main challenge in such system was to realize distribution of few tens of high-quality RF signals to and between RTM cards with high reliability. Usage of coaxial cables with RF connectors in relatively small area of RTM front panels lead to difficulties with fitting all cables and to ground loop problems. Cable based distribution significantly affected system reliability and was prone to errors during installation. A concept of RF Backplane was developed for MTCA.4 system to distribute high-precision clock, and RF phase reference signals together with managed high analog quality power supply to RTM cards. It also allows for world's first solution with hot-swap on RF signals. In this paper RF Backplane conception is described together with study on performance limits.
Programmable Host-Network Traffic Management

Data-center administrators perform traffic-management tasks (e.g., performance monitoring, server load balancing, and traffic engineering) to optimize network performance for diverse applications. Increasingly, traffic-management functionality is moving from the switches to the end hosts, which have more computational resources and better visibility into application behavior. However, traffic management is complicated by the heterogeneous interfaces for monitoring and controlling hosts and switches, and the scalability challenges of collecting and analyzing measurement data across the data center. We present a scalable and programmable platform for joint HOst-NEtwork (HONE) traffic management. HONE’s programming environment gives a simple, integrated, and logically-centralized view of the data center for defining measurement, analysis, and control tasks across hosts and switches. Programmers can think globally and rely on HONE to distribute the program for local execution and scalable aggregation of data across hosts and switches. HONE successfully balances the inherent tension between ease-of-use and performance. We evaluate HONE by implementing several canonical traffic-management applications, measuring its efficiency with micro-benchmarks, and demonstrating its scalability with larger-scale experiments on Amazon EC2.
Optimal Pair Matching Combined with Machine Learning Predicts that Omega-3 Fatty Acid Supplementation Markedly Reduces the Risk of Myocardial Infarction in African Americans

Conflicting results from clinical trials have contributed to a lack of consensus about cardioprotective effects of omega-3 (n-3) highly unsaturated fatty acids (HUFA). Although the VITAL trial did not demonstrate an overall benefit of n-3 HUFA supplementation on composite cardiovascular disease (CVD) and cancer outcomes, the substantial enrollment of African Americans (AfAm) afforded a unique opportunity for a post-hoc analysis of racial differences in the supplementation response. We employed propensity score matching to address potential covariate imbalances between AfAm and European American (EuAm) participants in VITAL (N=3,766 participants). Using Kaplan-Meier curves and two machine learning methodologies, we found that n-3 HUFA supplementation was significantly associated with a reduced risk of myocardial infarction (MI) exclusively in the AfAm subgroup, as evidenced by an odds ratio of 0.17 (95% CI [0.048, 0.59]). These findings indicate a potential cardioprotective benefit of n-3 supplementation in AfAm, specifically in reducing MI risks; a risk not identified in EuAm. Further investigation of n-3 HUFA effects through a hypothesis-driven randomized clinical trial among AfAm is needed to test a race-specific response that may inform recommendations for n-3 HUFA supplementation.
Discriminative Training of Hyper-feature Models for Object Identification

Object identification is the task of identifying specific obj ects belonging to the same class such as cars. We often need to recognize an object that we have only seen a few times. In fact, we often observe only one example of a particular object before we need to recognize it again. Thus we are interested in building a system which can learn to extract distinctive markers from a single example and which can then be used to identify the object in another image as “same” or “different”. Previous work by Ferencz et al. introduced the notion of hyper-features, which are properties of an image patch that can be used to estimate the utility of the patch in subsequent matching tasks. In this work, we show that hyper-feature based models can be more efficiently estimate d using discriminative training techniques. In particular, we describe a n ew hyper-feature model based upon logistic regression that shows improved performance over previously published techniques. Our approach significant ly outperforms Bayesian face recognition that is considered as a standard benchmark for face recognition.
VISTA Enhancer Browser—a database of tissue-specific human enhancers

Despite the known existence of distant-acting cis-regulatory elements in the human genome, only a small fraction of these elements has been identified and experimentally characterized in vivo. This paucity of enhancer collections with defined activities has thus hindered computational approaches for the genome-wide prediction of enhancers and their functions. To fill this void, we utilize comparative genome analysis to identify candidate enhancer elements in the human genome coupled with the experimental determination of their in vivo enhancer activity in transgenic mice [L. A. Pennacchio et al. (2006) Nature, in press]. These data are available through the VISTA Enhancer Browser (). This growing database currently contains over 250 experimentally tested DNA fragments, of which more than 100 have been validated as tissue-specific enhancers. For each positive enhancer, we provide digital images of whole-mount embryo staining at embryonic day 11.5 and an anatomical description of the reporter gene expression pattern. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to a particular tissue, or download entire collections of enhancers with a defined tissue specificity or conservation depth. These experimentally validated training sets are expected to provide a basis for a wide range of downstream computational and functional studies of enhancer function.
CBP01 Variable distance line-transect sampling of bird population numbers in different habitats on Konza Prairie

Records of bird species based on line transect sampling, giving perpendicular distance of sighting from the transect line on 16 separate transects. Bird surveys were conducted 2-4 times per year in January, April, June, and October for a 29-year period from 1981 to 2009. Transects were designed to determine bird communities and population numbers associated with tallgrass prairie habitats with different experimental treatments (fire frequency, grazed by bison vs. ungrazed), riparian habitats on forest edge, and gallery forests dominated by oak woodland.
Effects of a dietary ketone ester on hippocampal glycolytic and tricarboxylic acid cycle intermediates and amino acids in a 3xTgAD mouse model of Alzheimer's disease

In patients with Alzheimer's disease (AD) and in a triple transgenic (3xTgAD) mouse model of AD low glucose metabolism in the brain precedes loss of memory and cognitive decline. The metabolism of ketones in the brain by‐passes glycolysis and therefore may correct several deficiencies that are associated with glucose hypometabolism. A dietary supplement composed of an ester of D‐β‐hydroxybutyrate and R‐1,3 butane diol referred to as ketone ester (KE) was incorporated into a rodent diet and fed to 3xTgAD mice for 8 months. At 16.5 months of age animals were killed and brains dissected. Analyses were carried out on the hippocampus and frontal cortex for glycolytic and TCA (Tricarboxylic Acid) cycle intermediates, amino acids, oxidized lipids and proteins, and enzymes. There were higher concentrations of d‐β‐hydroxybutyrate in the hippocampus of KE‐fed mice where there were also higher concentrations of TCA cycle and glycolytic intermediates and the energy‐linked biomarker, N‐acetyl aspartate compared to controls. In the hippocampi of control‐fed animals the free mitochondrial [NAD+]/[NADH] ratio were highly oxidized, whereas, in KE‐fed animals the mitochondria were reduced. Also, the levels of oxidized protein and lipids were lower and the energy of ATP hydrolysis was greater compared to controls. 3xTgAD mice maintained on a KE‐supplemented diet had higher concentrations of glycolytic and TCA cycle metabolites, a more reduced mitochondrial redox potential, and lower amounts of oxidized lipids and proteins in their hippocampi compared to controls. The KE offers a potential therapy to counter fundamental metabolic deficits common to patients and transgenic models.
Subacute thyroiditis after SARS‐CoV‐2 vaccination: A case report

Key Clinical Message Subacute thyroiditis which is typically characterized by cervical pain and fever is caused by viral infection and is seen after SARS‐CoV‐2 vaccination. Here we report a post‐vaccination subacute thyroiditis after SARS‐CoV‐2 vaccination. Abstract Subacute thyroiditis (SAT) is possibly caused by a viral infection and is typically characterized by cervical pain and fever. SAT associated with SARS‐CoV‐2 infection or SARS‐CoV‐2 vaccination has been reported, albeit in limited numbers. A 34‐year‐old woman was referred to our clinic with typical SAT symptoms. The diagnosis was confirmed through thyroid scintigraphy after receiving the SARS‐CoV‐2 vaccination, despite testing negative for COVID‐19 via RT‐PCR. There is a theoretical correlation between SARS‐CoV‐2 vaccination and SAT. Vaccination may have a direct or indirect impact on the thyroid, but further studies are required to confirm this relationship. A systematic review of the literature of similar cases was performed for comparison. Ultimately, the overall benefits of SARS‐CoV‐2 vaccination outweigh the potential adverse effects. Therefore, these types of reports should not divert attention from the actual reality.
A compact folded loop antenna for LTE/GSM band mobile phone applications

This paper presents a multiband meandered loop antenna presently being used in LTE/WWAN systems for smart phone applications. The proposed antenna which features a meandered folded-loop generates two resonance modes in the LTE/GSM bands. The mode characteristic is characterized as a parallel monopole-like mode combined with a PCB ground plane mode. By using an inverted-L-shape capacitively-coupled element connected to the feed port on the backplane, the impedance bandwidth is broadened to cover LTE700/GSM850/GSM900/PCS/UMTS/LTE2300 bands. The simulation performed in this research used a high frequency structure simulator (HFSS) to optimally design the antenna, and a practical structure was constructed for the test. Details of the proposed antenna parameters, including reflection coefficient, radiation characteristics, peak gain, and radiation efficiency are presented and discussed in this paper.
Small subunit ribosomal RNA gene sequence of Minchinia teredinis (Haplosporidia: Haplosporidiidae) and a specific DNA probe and PCR primers for its detection.

Minchinia teredinis is a pathogen of wood-boring molluscs (shipworms), Teredo spp. , along the middle Atlantic coast of the U.S. Genomic DNA was extracted from M. teredinis spores and small subunit (SSU) rDNA was amplified by PCR, cloned, and sequenced. The sequence of M. teredinis SSU rDNA was aligned with that of Haplosporidium nelsoni and various protists in GenBank. A 22-base oligonucleotide probe unique to M. teredinis, designated MIN702, was commercially synthesized and tested for sensitivity and specificity. In dot-blot hybridizations the probe detected 500 pg of cloned M. teredinis rDNA. The probe did not hybridize with cloned SSU rDNA of Teredo spp. or H. nelsoni. The probe was further tested for specificity with in situ hybridizations on AFA-fixed, paraffin-embedded tissue sections. The probe hybridized well with M. teredinis plasmodia and immature spores, but poorly with mature spores. The probe did not hybridize with shipworm tissue or with the haplosporidians Haplosporidium louisiana from mud crabs (Panopeus spp.) or H. nelsoni and H. costale from Crassostrea virginica. The probe and a second 18-base oligonucleotide, when used as PCR primers, amplified a 536-bp fragment of the M. teredinis SSU rRNA gene. The PCR assay was able to detect 10 fg of the cloned gene and also detected the presence of M. teredinis DNA in shipworms in which infections were observed microscopically. The 536-bp amplification product was not obtained in one Teredo sp. or in one Bankia gouldi, both categorized as uninfected after microscopic inspection. The DNA probe and PCR primers appear to be specific for M. teredinis and should be useful as diagnostic tools and for life cycle investigations.
Analysis of Patients with Severe ARDS on VV ECMO Treated with Inhaled NO: A Retrospective Observational Study

(1) Background: This retrospective study focused on severe acute respiratory distress syndrome (ARDS) patients treated with veno-venous (VV) extracorporeal membrane oxygenation (ECMO) and who inhaled nitric oxide (NO) for pulmonary arterial hypertension (PAH) and/or right ventricular failure (RV failure). (2) Methods: Out of 662 ECMO-supported patients, 366 received VV ECMO, including 48 who inhaled NO. We examined the NO’s indications, dosing, duration, and the ability to lower PAH. We compared patients with and without inhaled NO in terms of mechanical ventilation duration, ECMO weaning, organ dysfunction, in-hospital mortality, and survival. (3) Results: Patients received 14.5 ± 5.5 ppm NO for 3 days with only one-third experiencing decreased pulmonary arterial pressure. They spent more time on VV ECMO, had a higher ECMO weaning failure frequency, and elevated severity scores (SAPS II and TIPS). A Kaplan–Meier analysis revealed reduced survival in the NO group. Multiple variable logistic regression indicated a twofold increased risk of death for ARDS patients on VV ECMO with NO. We observed no increase in continuous renal replacement therapy. (4) Conclusions: This study suggests that persistent PAH and/or RV failure is associated with poorer outcomes in severe ARDS patients on VV-ECMO, with an inhaled NO responder rate of only 30%, and it does not impact acute kidney failure rates.