Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Object Detection With Multi-Frame RGB-Lidar Feature Alignment

Single-frame 3D detection is a well-studied vision problem with dedicated benchmarks and a large body of work. This knowledge has translated to a myriad of real-world applications. However, frame-by-frame detection suffers from inconsistencies between independent frames, such as flickering bounding box shape and occasional misdetections. Safety-critical applications may not tolerate these inconsistencies. For example, automated driving systems require robust and temporally consistent detection output for planning. A vehicle’s 3D bounding box shape should not change dramatically across independent frames. Against this backdrop, we propose a multi-frame RGB-Lidar feature alignment strategy to refine and increase the temporal consistency of 3D detection outputs. Our main contribution is aligning and aggregating object-level features using multiple past frames to improve 3D detection quality in the inference frame. First, a Frustum PointNet architecture extracts a frustum-cropped point cloud using RGB and lidar data for each object frame-by-frame. After tracking, multi-frame frustum features of unique objects are fused through a Gated Recurrent Unit (GRU) to obtain a refined 3D box shape and orientation. The proposed method improves 3D detection performance on the KITTI tracking dataset by more than 4% for all classes compared to the vanilla Frustum PointNet baseline. We also conducted extensive ablation studies to show the efficacy of our hyperparameter selections. Codes are available at https://github.com/emecercelik/Multi-frame-3D-detection.git.
Miniaturized Reconfigurable Filtering Power Divider with Arbitrary Output Phase Difference and Improved Isolation

In this paper, a miniaturized reconfigurable power divider with frequency selectivity, arbitrary output phase difference and improved isolation is proposed. It is a cascade design of a filtering power divider (FPD) with a miniaturized quadrature coupler (MQC). Both the phase difference (0° or 180°) and the power division ratio of the FPD can be changed as desired, and the output phase difference of the cascaded circuit can be arbitrarily tuned within 360° based on the states of the reflected waves at the outputs of the FPD. Furthermore, a modified isolation network is designed and the isolation between the reflected waves of the cascaded circuit has been significantly improved. The experimental results show that the output phase difference can be continuously tuned from 0° to 360° with the isolation better than 14 dB within the whole frequency tuning range from 1 GHz to 1.3 GHz.
Advanced Finite-Time Control For Bilateral Teleoperators With Delays and Uncertainties

In this paper, a finite-time control method is presented for bilateral teleoperators to ensure coordination of the master and slave manipulators in the presence of time-varying delays, external disturbances, and dynamic uncertainties. Without the requirement of acceleration measurement, the master and slave manipulators are assured to coordinate with each other within finite time in free motion. Additionally, the coordination errors are ensured to converge to a neighborhood of the origin in finite time. In the constrained motion, where both the master and slave manipulators are affected by unknown external force from the human operator and the removed environment, the bilateral teleoperation system is proved to be stably provided a certain degree of transparency performance during operation. Stability and finite-time tracking are investigated via Lyapunov-Krasovskii theory for the bilateral teleoperation system under time-varying delays and external forces. Subsequently, numerical examples and experiments are presented to demonstrate the efficiency and efficacy of the proposed control approach for teleoperators.
Characterization of the Impairment and Recovery of GaN-HEMTs in Low-Noise Amplifiers under Input Overdrive

This paper reports an investigation of the dynamic behavior of a GaN-HEMT, which is subjected to high reverse gate voltage pulses. These stress conditions are typical for robust LNAs under RF input overdrive and their quick recovery to the initial operating state is crucial but scarcely investigated. It was found that trapping effects similar to those in power amplifiers may play a major role in the impairment and recovery process of GaN HEMT-based LNAs with Fe-doping in the buffer. The interaction with traps and the resulting adverse effects on the device operation were found to be in the range of microseconds. Measurements at elevated temperatures suggest that the recovery time of the transistor is mainly governed by the emission of occupied traps, which takes place in the order of milliseconds. The characterization method is suited to predict the transient effects of LNAs under input overdrive.
A 178.9-dB FoM 128-dB SFDR VCO-Based AFE for ExG Readouts With a Calibration-Free Differential Pulse Code Modulation Technique

This article presents a voltage-controlled oscillator (VCO)-based analog front end (AFE) for ExG readout applications with both a wide dynamic range (DR) and high linearity. By using a differential pulse code modulation (DPCM) technique, VCO non-linearity is mitigated by operating the VCO in the small-signal linear regime. To minimize power consumption from the power-hungry gain error calibration, the effect of VCO gain variation within the DPCM loop is investigated, leading to a calibration-free DPCM via a <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> truncator. At the circuit level, a coarse–fine phase decoding scheme is proposed to increase signal-to-quantization-noise ratio (SQNR) while avoiding metastability issues. Fabricated in 65 nm, the AFE occupies an area of 0.056 mm<sup>2</sup>. It achieves 94.2-dB SNDR and 95.1-dB DR in 500 Hz of bandwidth while dissipating 1.68-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power from a single 0.8-V supply, yielding a Schreier FoM of 178.9 dB. Importantly, the techniques enable a virtually distortion-less spectrum with a noise-floor limited spurious-free dynamic range (SFDR) of 128 dB. In addition, the AFE is validated with ECG and EOG recording, demonstrating complete signal capturing in the presence of motion/stimulation artifacts.
Unsupervised Clustering for Pattern Recognition of Heating Energy Demand in Buildings Connected to District-Heating Network

This paper presents a novel framework for the identification of different consumption patterns of heating loads of buildings. The approach to analyzing the consumption data is carried out by a combination of unsupervised clustering models. Density based clustering is used for outlier detection in the original dataset and K-means for pattern recognition. The proposed framework is then applied to a real building connected to the district heating in Tartu (Estonia). Three main day-types are identified for the building as an outcome of the clustering process, with different patterns throughout these days. More than 60% of the analyzed Cluster Validation Indexes studied in this paper show that classifying the daily demand profiles in three clusters is the optimal classification.
The Counting Problem of Slot Mereology

Bennett proposed in 2013 a new mereological theory based on the decomposition of the parthood relation into two relations: having a slot and occupying that slot. Slot mereology can be used to represent the mereological structure of a variety of entities that can have the same part multiple times, including (but not only) structural universals. We show here that this theory is not compatible with a counting criterion that would enable us to count appropriately how many times a whole has a part. We propose news axioms to fix those flaws.
FOWL - An OWL to FOL Translator

The Web Ontology Language OWL 2 DL is the language used to build ontologies for a wide range of domains including the life sciences, the financial industry and the domain of energy simulations. Many of these domain ontologies are built beneath upper ontologies (such as DOLCE or BFO), which themselves are written in more expressive logics than OWL 2 DL, in particular first-order logic (although they may provide truncated versions in OWL). In this paper we present FOWL, a tool that enables the translation of OWL ontologies into FOL, and thereby the integration of OWL domain ontologies with first-order logic ontologies. One use case for this tool is the ability to validate OWL domain ontologies against their upper-level ontology. FOWL also allows reasoning over the translation using the VAMPIRE reasoner.
Ensemble Techniques for Lazy Classication Based on Pattern Structures

This paper presents diﬀerent versions of classiﬁcation ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classiﬁcation tasks the clas-siﬁcation algorithms based on pattern structures showed better performance than Random Forest. The quality of the algorithms is noticeably dependent on ensemble aggregation function and on boosting weighting scheme.
BLF: A Blockchain Logging Framework for Mining Blockchain Data

A business model provides a description or architecture of how a company creates, delivers, and captures value. Business model performance management refers to the continuous monitoring of performance indicators and changes in the environment to control business model performance and timely trigger business model (re-)design. The business processes of a company produce a wealth of data and information about a company's operational performance, which may be aggregated to a higher level, i.e., the tactical level of the business model. However, process data may not always be directly mappable to business models because a complex relationship exists between the two concepts. Existing studies provide little insight or guidance on how business model performance indicators can be defined and monitored. To address this knowledge gap, we will employ a design science research approach to develop a method for the definition and monitoring of business model performance indicators. This research aims to contribute to BPM research by exploring the relationship between business models and business processes.
Streamlining FoodOn Seafood Nomenclature using a Semi-automated ROBOT Template-driven Approach

The nomenclature of seafood species and their products is one of the very important areas which needs to be curated and regularly updated in FoodOn. In this paper, we present a semi-automated ROBOT template-driven approach we designed for aligning FoodOn with the FDA issued 'Seafood List’, together with other established resources e.g., NCBI, ITIS, and Wikipedia. The basic data in the FDA Seafood List, which included Type, Common Name, FDA Law, FDA Acceptable Market Name(s) and Scientific Name was exported in an Excel format. FDA Seafood Labels (Scientific Name) were mapped against NCBITaxons and NCBI GenBank Names using ETE 3 toolkit and around 90% of labels were correctly matched. ITIS TSNs were available for over 85% of seafood labels which were fetched using a locally installed ITIS database. Wikipedia-URL was retrieved as a cross-referenced database using the FoodOn-Wikipedia tool. In some cases, Wikidata was also used as an interface to connect to NCBITaxon. The curated seafood data was then converted from a tab-delimited TSV template file to a Web Ontology Language OWL file format using ROBOT template. This method will not only help FoodOn to regularly update seafood organisms but will also help to maximize the seafood product coverage and data interoperability.
Millikan + Ranganathan - From Perception to Classification

We assume that substances in the world are represented by two types of concepts, namely substance concepts, as originally introduced by Ruth Millikan, and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification. Based on this distinction, we introduce a general methodology for building lexico-semantic hierarchies of substance concepts, where nodes are annotated with the media, e.g., videos or photos, from which substance concepts are extracted, and are associated with the corresponding classification concepts. The methodology is based on Ranganathan’s original faceted approach, contextualized to the problem of classifying substance concepts. The key novelty is that the hierarchy is built exploiting the visual properties of substance concepts, while the linguistically defined properties of classification concepts are only used to describe substance concepts. The validity of the approach is exemplified by providing some highlights of an ongoing project whose goal is to build a large scale multimedia multilingual concept hierarchy.
Common Core Conformant Definitions for an Ontology of Commercial Exchange

In “Toward an Ontology of Commercial Exchange” [11], we proposed human readable definitions for terms that are central to an ontology of commercial exchange. This paper furthers that project in two ways. First, the definitions have been modified to be compatible with the Common Core Ontologies (CCO). CCO is used in a wide variety of domains including the industrial and military domains. Having a commerce ontology compatible with CCO allows data about the exchange of goods relevant to those domains to be tagged with compatible ontologies. Second, we propose preliminary formalizations of these definitions, with the aim of informing the creation of an ontology which can be used to assist in reasoning over business-related data.
Fixpoint Characterizations of Disjunctive Hybrid MKNF Knowledge Bases

Combining answer set programming with ontologies is of great theoretical and practical interest. Hybrid MKNF is a semantics that combines ASP with ontologies without increasing the solving complexity. While there are efficient solvers for ASP and ontologies, efficient solvers for hybrid MKNF have yet to be constructed. In this work, we address some issues that must be solved before a CDNL-based (conflict-driven nogood learning) solver for hybrid MKNF knowledge bases can be developed. We formulate a framework to characterize disjunctive hybrid MKNF semantics through a family of fixpoint operators, then contextualize this framework by demonstrating that it could be possible to integrate it into a solver. Crucially, our approach can be performed without relying on a dependency graph. Finally, we recognize a property of our characterization that is analogous to head-cycle free disjunctive logic programs and demonstrate how to exploit this property to improve solver efficiency.
A Study of Zero Anaphora Resolution in Chinese Discourse: From the Perspective of Psycholinguistics

Anaphora is the main linguistic means to establish discourse coherence, and anaphora resolution is the psychological process to maintain this coherence. Chinese discourse is characterized with providing multiplicity of linguistic clues to readers by employing various referential apparatuses such as pronoun anaphora, zero anaphora, and so on. As a way of avoiding repeated reference to an object that is mentioned beforehand, zero anaphora is frequently employed in discourse. The production and resolution of zero anaphora largely concerns some constraints underlying psychological mechanisms. We particularly focus on zero anaphora resolution in the present study to try to discover some specific aspects of the underlying mechanism, hoping to find out some factors unique to the resolution process. We designed the first two experiments to probe into the psychological reality when participants were presented with sentences containing either pronoun anaphora or zero anaphora or both under discourse condition with topic continuity in Experiment 1a and topic discontinuity in Experiment 1b. We did not find any significant difference in the reaction time between zero anaphora resolution and pronoun anaphora resolution, indicating that zero anaphora possibly works within the processing mechanism on which pronoun anaphora resolution depends. However, we found significantly longer time in reading the first sentence in any of the discourse, showing that the first-mention effect exists in anaphora resolution. We further explored the time course of zero anaphora resolution by measuring the reaction time during the period when participants read sentences that varied according to the location where zero anaphora occurred under two conditions: topic continuity (Experiment 2a) vs. topic discontinuity (Experiment 2b). The strategies of searching for the referential information were found divergent: the exhaustive searching strategy was adopted when the topics within a discourse were kept continuous and the heuristic searching strategy was employed when the topics were discontinuous. The design of Experiment 5 took the factor of voice type and situation consistency into consideration, investigating in what way do those factors influence the resolution of zero anaphora. The voice type, according to the results, plays a significant role for its exclusively close relationship with the first-mention effect.
Proprioceptive Training for Knee Osteoarthritis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: There is increased interest in proprioceptive training for knee osteoarthritis (KOA). However, little consensus supports the effectiveness of this intervention. Objective: This meta-analysis aimed to assess the effects of proprioceptive training on symptoms, function, and proprioception in people with KOA. Methods: The PubMed, Cochrane Library, Web of Science, and EMBASE databases were systematically searched from the inception dates to April 16, 2021 for relevant randomized controlled trials (RCTs). Data were pooled by calculating the standardized mean differences (SMDs) and 95% confidence intervals (CIs). A random-effects model was used for the analyses. Results: A total of 24 RCTs involving 1,275 participants were included in our analysis. This study indicated that compared to no intervention, proprioceptive training significantly improved pain, stiffness, physical function, joint position sense (JPS), muscle strength, mobility, and knee ROM (P < 0.05) in people with KOA. When compared to other non-proprioceptive training, proprioceptive training provided better results in terms of JPS (SMD = −1.28, 95%CI: [−1.64, −0.92], I2 = 0%, P < 0.00001) and mobility (timed walk over spongy surface) (SMD = −0.76, 95%CI: [−1.33, −0.18], I2 = 64%, P = 0.01), and other results are similar. When proprioceptive training plus other non-proprioceptive training compared to other non-proprioceptive training, the two groups showed similar outcomes, but there was a greater improvement for JPS (SMD = −1.54, 95%CI: [−2.74, −0.34], I2 = 79%, P = 0.01), physical function (SMD = −0.34, 95%CI: [−0.56, −0.12], I2 = 0%, P = 0.003), and knee ROM (P < 0.05) in the proprioceptive training plus other non-proprioceptive training group. When proprioceptive training plus conventional physiotherapy compared against conventional physiotherapy, the two groups demonstrated similar outcomes, but there was a significant improvement for JPS (SMD = −0.95, 95%CI: [−1.73, −0.18], I2 = 78%, P = 0.02) in the proprioceptive training plus conventional physiotherapy group. Conclusions: Proprioceptive training is safe and effective in treating KOA. There is some evidence that proprioceptive training combined with general non-proprioceptive training or conventional physiotherapy appears to be more effective and should be considered as part of the rehabilitation program. However, given that the majority of current studies investigated the short-term effect of these proprioceptive training programs, more large-scale and well-designed studies with long-term follow up are needed to determine the long-term effects of these proprioceptive training regimes in KOA. Systematic Review Registration: https://www.crd. york.ac. uk/prospero/#recordDetails, PROSPERO, identifier: CRD42021240587.
Knowledge and Self-Protective Practices Against COVID-19 Among Healthcare Workers in Vietnam

Background: In middle-income countries such as Vietnam, where healthcare resources are already constrained, protecting healthcare workers (HCWs) is essential for ensuring the sustainability of COVID-19 response in Vietnam. This study was conducted to assess the knowledge and practices regarding the prevention of the COVID-19 among the HCWs in Vietnam to identify the ways of disseminating information to maximize the safety of these essential workers. Methods: An online cross-sectional study, using respondent-driven sampling, was conducted in Vietnam with 742 participants within 2 weeks. The validity of the questionnaire was examined by exploratory factor analysis. Descriptive statistics were used to identify the level of knowledge and practices among the HCWs to prevent the COVID-19. Inferential statistics and regression modeling were used to identify the associated factors with results. Results: Vietnamese HCWs had a high level of knowledge with more than 75% of the participants demonstrating awareness of all the modes of transmission aside from air. The mean knowledge score was 3.7 ± 0.8 (range 1–5). Nearly all the participants relied on the Ministry of Health (98.3%) and the internet (95.5%) for information regarding the COVID-19. The participants endorsed a moderately high level of self-protective practices with mean scores of 4.2 and 3.6 (band score 1–5) for the precautionary and psychological measures, respectively. Nurses were more likely to practice the precautionary measures than doctors and the HCWs at the central level were more likely to practice the psychological measures than those at the district level. Conclusion: Future education initiatives should consolidate the latest literature in an accessible format, focusing initially on the gaps of knowledge regarding aerosol transmission. These initiatives should primarily focus on the doctors, especially those in emergency and intensive care departments.
Heparin-Binding Protein in Critically Ill Children With Severe Community-Acquired Pneumonia

Objective: The aim of this study was to investigate possible associations between Heparin-binding protein (HBP) and the development of respiratory failure (RF) and sepsis in critically ill children with severe community-acquired pneumonia (CAP). Methods: This study enrolled 157 children with severe CAP admitted to Intensive Care Unit (ICU). At ICU admission, the levels of HBP and other biomarkers, including C-reactive protein, interleukin-6 (IL-6), procalcitonin, white blood cells, neutrophil percentage, and D-dimer, were determined. Results: Of the enrolled patients, 106 developed RF (35 with RF at enrollment and 71 with RF after enrollment), while 51 did not developed RF. The number of patients progressing to sepsis in those with or without RF were 34 (21 with severe sepsis) and 14, respectively. The plasma level of HBP at admission was more than eightfold higher than the upper normal value. HBP, IL-6, and D-dimer could significantly predict the development of RF, and a high level of HBP (odds ratio = 1.008, 95% confidence interval: 1.003–1.013) was independently associated with the development of RF in this population. Compared with other biomarkers, HBP was the best indicator of progression to severe sepsis, with an area under the receiver operating characteristic curve of 0.85, the best specificity at 96.30%, and a positive predictive value of 92.86% at the optimal cut-off value of 340.29 ng/mL. The HBP level was also positively correlated with other conventional biomarkers. Conclusion: HBP might represent a better predictor of disease progression in children with severe CAP than currently used biomarkers.