Common Module Research Articles

A methodology of designing and optimising a 4-bit absolute-value detector for the application of Brain-computer Interface

Digital integrated circuits perform logical operations through several modules composed of logic gates. As a common module in digital circuits, the absolute value detector is applied to compare the absolute value of two binary inputs and can be further used for data sorting and searching. In clinical medicine, the detector can receive biological data from the sensor and control the instrument on the operating table. A typical application is Brain-computer Interface (BCI). Therefore, the performance of detectors is significant to electronic devices like computers. The optimisation of detectors usually concentrates on their propagation delay and power consumption. But theoretical optimisation will inevitably use simple logic and have large fan-in, and it is difficult to be implemented in practice. Here we explore an alternative plan that is more convenient to be produced in the industry, which combines static complementary metal-oxide semiconductor (CMOS) technology and transmission gates. After gate sizing, the critical path calculates the propagation delay and energy based on the logical effort theory. Finally, the relationship between energy and delay is analysed by scaling the voltage supply and the minimum energy occurs when the delay grows to 1.5 times the minimum delay.

A full-function Global Common Module prototype for ATLAS Phase-II upgrade

The High Luminosity Large Hadron Collider (HL-LHC [1]), an upgrade of the LHC, is set to become operational in 2029, aiming to achieve instantaneous luminosities 5–7.5 times larger than the nominal value of the LHC. However, unlocking the full physics potential at this much higher luminosity level necessitates a tenfold increase in the data bandwidth processed by ATLAS. This poses significant challenges to the design of the Trigger and Data Acquisition systems. To address these challenges, a baseline architecture has been chosen for the ATLAS Phase-II upgrade, relying on a single-level hardware trigger known as the Level-0 Trigger. This trigger has a maximum rate of 1 MHz and a latency of 10 μs. Central to this upgrade is the inclusion of a new subsystem — the Global Trigger [2]. This component performs complex algorithms, akin to those currently used in Phase-I high-level trigger software (such as Topoclustering), on full-granularity calorimeter data. The Global Trigger is divided into three sublayers: the Multiplexer Processor (MUX) layer, the Global Event Processor (GEP) layer, and the Global to Central Trigger Processor [3] interface (gCTPi). A full-function Global Common Module (GCM) hardware prototype has been designed to fulfill the requirements of all three sublayers of the Global Trigger, featuring different firmware loads. This GCM prototype, based on the ATCA [4] front board form factor, incorporates two of the latest AMD (Xilinx) Versal Premium devices VP1802 [5]. These devices boast double the density of the Virtex UltraScale+ FPGA VU13P used in the previous design [6] and include an integrated SoC with a completely new architecture. To handle high-speed I/Os, this GCM prototype employs twenty 12-channel 25.7 Gb/s FireFly [7] optical engines. The estimated maximum power consumption of this GCM prototype is 400 W, which falls within the cooling capabilities of the ATLAS ATCA shelf. To ensure power integrity, signal integrity, and thermal performance, extensive PCB simulations and thermal simulations have been done to guide the layout design of the GCM prototype. This paper provides an in-depth overview of the design process for this full-function GCM prototype hardware, with a particular focus on technology choices and simulation results.

Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention.

Deep learning models have gained prominence in human activity recognition using ambient sensors, particularly for telemonitoring older adults' daily activities in real-world scenarios. However, collecting large volumes of annotated sensor data presents a formidable challenge, given the time-consuming and costly nature of traditional manual annotation methods, especially for extensive projects. In response to this challenge, we propose a novel AttCLHAR model rooted in the self-supervised learning framework SimCLR and augmented with a self-attention mechanism. This model is designed for human activity recognition utilizing ambient sensor data, tailored explicitly for scenarios with limited or no annotations. AttCLHAR encompasses unsupervised pre-training and fine-tuning phases, sharing a common encoder module with two convolutional layers and a long short-term memory (LSTM) layer. The output is further connected to a self-attention layer, allowing the model to selectively focus on different input sequence segments. The incorporation of sharpness-aware minimization (SAM) aims to enhance model generalization by penalizing loss sharpness. The pre-training phase focuses on learning representative features from abundant unlabeled data, capturing both spatial and temporal dependencies in the sensor data. It facilitates the extraction of informative features for subsequent fine-tuning tasks. We extensively evaluated the AttCLHAR model using three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan). We compared its performance against the SimCLR framework, SimCLR with SAM, and SimCLR with the self-attention layer. The experimental results demonstrate the superior performance of our approach, especially in semi-supervised and transfer learning scenarios. It outperforms existing models, marking a significant advancement in using self-supervised learning to extract valuable insights from unlabeled ambient sensor data in real-world environments.

Er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at an unprecedented speed and in a head-to-head scenario, using independently developed software on open-wheel race cars. This paper presents the complete software architecture used by the team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes, and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modeling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed second and third, respectively.

Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

Efficient Reflectance Capture With a Deep Gated Mixture-of-Experts.

We present a novel framework to efficiently acquire anisotropic reflectance in a pixel-independent fashion, using a deep gated mixture-of-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module that takes photometric measurements as input and selects one out of a number of specialized decoders for reflectance reconstruction, essentially trading generality for quality. A common pre-trained latent-transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging near-planar samples with a lightstage. Compared with the state-of-the-art technique, our quality is improved with the same number of input images, and our input image number can be reduced to about 1/3 for equal-quality results. We further generalize the framework to enhance a state-of-the-art technique on non-planar reflectance scanning.

Parallel Multi-Path Network for Ocular Disease Detection Inspired by Visual Cognition Mechanism.

Various ocular diseases such as cataracts, glaucoma, and diabetic retinopathy have become several major factors causing non-congenital visual impairment, which seriously threatens people's vision health. The shortage of ophthalmic medical resources has brought huge obstacles to large-scale ocular disease screening. Therefore, it is necessary to use computer-aided diagnosis (CAD) technology to achieve large-scale screening and diagnosis of ocular diseases. In this work, inspired by the human visual cognition mechanism, we propose a parallel multi-path network for multiple ocular diseases detection, called PMP-OD, which integrates the detection of multiple common ocular diseases, including cataracts, glaucoma, diabetic retinopathy, and pathological myopia. The bottom-up features of the fundus image are extracted by a common convolutional module, the Low-level Feature Extraction module, which simulates the non-selective pathway. Simultaneously, the top-down vessel and other lesion features are extracted by the High-level Feature Extraction module that simulates the selective pathway. The retinal vessel and lesion features can be regarded as task-driven high-level semantic information in the physician's disease diagnosis process. Then, the features are fused by a feature fusion module based on the attention mechanism. Finally, the disease classifier gives prediction results according to the integrated multi-features. The experimental results indicate that our PMP-OD model outperforms other state-of-the-art (SOTA) models on an ocular disease dataset reconstructed from ODIR-5K, APTOS-2019, ORIGA-light, and Kaggle.

Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.

Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and lowering quality of life. Treating physicians encounter numerous challenges attributed to the complex nature of pelvic anatomy, limitations of diagnostic techniques, and lack of available resources. This article is an overview of the current state of artificial intelligence (AI) in tackling the difficulties of managing benign anorectal disorders and pelvic floor disorders. A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed database to identify all potentially relevant studies published from January 2000 to August 2023. Search queries were built using the following terms: AI, machine learning, deep learning, benign anorectal disease, pelvic floor disorder, fecal incontinence, obstructive defecation, anal fistula, rectal prolapse, and anorectal manometry. Malignant anorectal articles and abstracts were excluded. Data from selected articles were analyzed. 139 articles were found, 15 of which met our inclusion and exclusion criteria. The most common AI module was convolutional neural network. researchers were able to develop AI modules to optimize imaging studies for pelvis, fistula, and abscess anatomy, facilitated anorectal manometry interpretation, and improved high-definition anoscope use. None of the modules were validated in an external cohort. There is potential for AI to enhance the management of pelvic floor and benign anorectal diseases. Ongoing research necessitates the use of multidisciplinary approaches and collaboration between physicians and AI programmers to tackle pressing challenges.

Evolution of cereal floral architecture and threshability.

Hulled grains, while providing natural protection for seeds, pose a challenge to manual threshing due to the pair of glumes tightly encasing them. Based on natural evolution and artificial domestication, gramineous crops evolved various hull-like floral organs. Recently, progress has been made in uncovering novel domesticated genes associated with cereal threshability and deciphering common regulatory modules pertinent to the specification of hull-like floral organs. Here we review morphological similarities, principal regulators, and common mechanisms implicated in the easy-threshing traits of crops. Understanding the shared and unique features in the developmental process of cereal threshability may not only shed light on the convergent evolution of cereals but also facilitate the de novo domestication of wild cereal germplasm resources through genome-editing technologies.

A Way to Optimize Delay of Carry-Skip Adders by Using Blocks of Variable Sizes

As one of the most common operational modules in computer processors, the optimization of the propagation delay of adders has attracted extensive attention of researchers. This article proposes a method of optimizing the delay of carry-skip adders by using blocks of variable sizes. By increasing the sizes of the bypass stages gradually and then decreasing it stage by stage, keeping the delay of the first and last stage short, the total propagation delay is optimized. Compared with fixed size carry-skip adders, the delay of variable sizes carry-skip adders has a square root relationship with the number of bits. Thus, when the number of bits is large, the propagation delay is significantly reduced. After calculation, when the number of bits is 16, the propagation delay decreases by 16.67%, and when the number of bits is 256, it decreases by 52.78%. Due to the shorter propagation delay, variable sizes carry-skip adders have faster operation speed and can be applied to improve the performance of computer processors.

CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach

Multimodal visual information fusion aims to integrate the multi-sensor data into a single image which contains more complementary information and less redundant features. However the complementary information is hard to extract, especially for infrared and visible images which contain big similarity gap between these two modalities. The common cross attention modules only consider the correlation, on the contrary, image fusion tasks need focus on complementarity (uncorrelation). Hence, in this paper, a novel cross attention mechanism (CAM) is proposed to enhance the complementary information. Furthermore, a two-stage training strategy based fusion scheme is presented to generate the fused images. For the first stage, two auto-encoder networks with same architecture are trained for each modality. Then, with the fixed encoders, the CAM and a decoder are trained in the second stage. With the trained CAM, features extracted from two modalities are integrated into one fused feature in which the complementary information is enhanced and the redundant features are reduced. Finally, the fused image can be generated by the trained decoder. The experimental results illustrate that our proposed fusion method obtains the SOTA fusion performance compared with the existing fusion networks. The codes of our fusion method will be available soon.

Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation.

Intraguild predation (IGP)-predation between generalist predators (IGPredator and IGPrey) that potentially compete for a shared prey resource-is a common interaction module in terrestrial food webs. Understanding temporal variation in webs with widespread IGP is relevant to testing food web theory. We investigated temporal constancy in the structure of such a system: the spider-focused food web of the forest floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders collected from the floor of a deciduous forest during spring, summer, and fall over four years. Because only spiders were defined as consumers, the web was tripartite, with 11 consumer nodes (spider families) and 22 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and the 11 spider families. Most (99%) spider-spider predation was on spider IGPrey, and ~90% of these interactions were restricted to spider families within the same broadly defined foraging mode (cursorial or web-spinning spiders). Bootstrapped-derived confidence intervals (BCI's) for two indices of web structure, restricted connectance and interaction evenness, overlapped broadly across years and seasons. A third index, % IGPrey (% IGPrey among all prey of spiders), was similar across years (~50%) but varied seasonally, with a summer rate (65%) ~1.8x higher than spring and fall. This seasonal pattern was consistent across years. Our results suggest that extensive spider predation on spider IGPrey that exhibits consistent seasonal variation in frequency, and that occurs primarily within two broadly defined spider-spider interaction pathways, must be incorporated into models of the dynamics of forest-floor food webs.

Step-Nucleation In Situ Self-Repair to Prepare Rollable Large-Area Ultrathin MOF Membranes.

Ultrathin membranes with ultrahigh permeance and good gas selectivity have the potential to greatly decrease separation process costs, but it requires the practical preparation of large area membranes for implementation. Metal-organic frameworks (MOFs) are very attractive for membrane gas separation applications. However, to date, the largest MOF membrane area reported in the literature is only about 100 cm2 . In the present study, a new step-nucleation in situ self-repair strategy is proposed that enables the preparation of large-area (2400 cm2 ) ultrathin and rollable MOF membranes deposited on an inexpensive flexible polymer membrane support layer for the first time, combining a polyvinyl alcohol (PVA)-metal-ion layer and a pure metal-ion layer. The main role of the pure metal-ion layer is to act as the main nucleation sites for MOF membrane growth, while the PVA-metal-ion layer acts as a slow-release metal-ion source, which supplements MOF crystal nucleation to repair any defects occurring. Membrane modules are necessary components for membrane applications, and spiral-wound modules are among the most common module formats that are widely applied in gas separation. A 4800 cm2 spiral-wound membrane modulewas successfully prepared, demonstrating the practical implementation of large-area MOF membranes.

313.4: The tissue common response module (tCRM) score predicts treatment response for acute cellular rejection following pancreas transplant

313.4: The tissue common response module (tCRM) score predicts treatment response for acute cellular rejection following pancreas transplant

Maximizing Mutual Information Across Feature and Topology Views for Representing Graphs

Recently, maximizing mutual information has emerged as a powerful tool for unsupervised graph representation learning. Existing methods are typically effective in capturing graph information from the topology view but consistently ignore the node feature view. To circumvent this problem, we propose a novel method by exploiting mutual information maximization across feature and topology views. Specifically, we first construct the feature graph to capture the underlying structure of nodes in feature spaces by measuring the distance between pairs of nodes. Then we use a cross-view representation learning module to capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we develop a common representation learning module by using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, the proposed method achieves comparable or even better performance under the unsupervised representation and linear evaluation protocol.

Genome and transcriptome of Selaginella kraussiana reveal evolution of root apical meristems in vascular plants

The evolution of roots allowed vascular plants to adapt to land environments. Fossil evidence indicates that roots evolved independently in euphyllophytes (ferns and seed plants) and lycophytes, the two lineages of extant vascular plants. Based on a high-quality genome assembly, mRNA sequencing (mRNA-seq) data, and single-cell RNA-seq data for the lycophyte Selaginella kraussiana, we show that the two root origin events in lycophytes and euphyllophytes adopted partially similar molecular modules in the regulation of root apical meristem (RAM) development. In S.kraussiana, the RAM initiates from the rhizophore primordium guided by auxin and duplicates itself by dichotomous branching. The auxin signaling pathway directly upregulates euAINTEGUMENTAb (SkeuANTb), and then SkeuANTb directly promotes the expression of SkeuANTa and the WUSCHEL-RELATED HOMEOBOX13b (SkWOX13b) for RAM maintenance, partially similar to the molecular pathway involving the euANT-branch PLETHORA (AtPLT) genes and AtWOX5 in root initiation in the seed plant Arabidopsis thaliana. Other molecular modules, e.g., SHORT-ROOT and SCARECROW, also have partially similar expression patterns in the RAMs of S.kraussiana and A.thaliana. Overall, our study not only provides genome and transcriptome tools of S.kraussiana but also indicates the employment of some common molecular modules in RAMs during root origins in lycophytes and euphyllophytes.

Spatially activated conserved auxin-transcription factor regulatory module controls de novo root organogenesis in rice.

This study reveals that the process of crown root development and auxin-induced de novo root organogenesis during in vitro plantlet regeneration share a common auxin-OsWOX10 regulatory module in rice. In the fibrous-type root system of rice, the crown roots (CR) are developed naturally from the shoot tissues. Generation of robust auxin response, followed by activation of downstream cell fate determinants and signaling pathways at the onset of crown root primordia (CRP) establishment is essential for new root initiation. During rice tissue culture, embryonic calli are induced to regenerate shoots in vitro which undergo de novo root organogenesis on an exogenous auxin-supplemented medium, but the mechanism underlying spatially restricted root organogenesis remains unknown. Here, we reveal the dynamics of progressive activation of genes involved in auxin homeostasis and signaling during initiation and outgrowth of rice crown root primordia. By comparative global dataset analysis, we identify the crown root primordia-expressed genes whose expression is also regulated by auxin signaling. In-depth spatio-temporal expression pattern analysis shows that the exogenous application of auxin induces a set of key transcription factors exclusively in the spatially positioned CRP. Further, functional analysis of rice WUSCHEL-RELATED HOMEOBOX 10 (OsWOX10) during in vitro plantlet regeneration from embryogenic calli shows that it promotes de novo root organogenesis from regenerated shoots. Expression of rice OsWOX10 also induces adventitious roots (AR) in Arabidopsis, independent of homologous endogenous Arabidopsis genes. Together, our findings reveal that a common auxin-transcription factor regulatory module is involved in root organogenesis under different conditions.

Gastric polyp detection module based on improved attentional feature fusion

Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected.

Cortical polarity ensures its own asymmetric inheritance in the stomatal lineage to pattern the leaf surface.

Asymmetric cell divisions specify differential cell fates across kingdoms. In metazoans, preferential inheritance of fate determinants into one daughter cell frequently depends on polarity-cytoskeleton interactions. Despite the prevalence of asymmetric divisions throughout plant development, evidence for analogous mechanisms that segregate fate determinants remains elusive. Here, we describe a mechanism in the Arabidopsis leaf epidermis that ensures unequal inheritance of a fate-enforcing polarity domain. By defining a cortical region depleted of stable microtubules, the polarity domain limits possible division orientations. Accordingly, uncoupling the polarity domain from microtubule organization during mitosis leads to aberrant division planes and accompanying cell identity defects. Our data highlight how a common biological module, coupling polarity to fate segregation through the cytoskeleton, can be reconfigured to accommodate unique features of plant development.

Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental results demonstrate that the proposed method surpasses alternative approaches in terms of accuracy and the recognition interval. By extracting common features of the PPG and EMG signals, this approach achieves effective emotion recognition without mutual interference. The findings provide a significant advancement in real-time emotion analysis and offer a clear and concise framework for understanding individuals’ emotional states using physiological signals.