Miracle-dz/UniFAR-FTUs huggingface dataset:
Proactive Attack Detection at the Edge through an Ensemble Deep Learning Model

The new form of the Web involves numerous devices present in two infrastructures, i.e., the Internet of Things (IoT) and the Edge Computing (EC) infrastructure. IoT devices are adopted to record ambient data and host lightweight processing to provide support for applications offered to end users. EC is placed between the IoT and Cloud and can be the host of more advanced processing activities. It has gained popularity due to the increased computational resources compared to the IoT and the decreased latency in the provision of responses compared to the Cloud. A high number of nodes may be present at the EC that should secure the Quality of Service (QoS) of the desired applications. Apparently, EC nodes become central points where the collected data are collected and processed. Data processing (especially when data are sensitive) imposes various security issues that should be mitigated in order to maintain high QoS levels and the uninterrupted functioning of EC nodes. In this paper, motivated by the need of the increased security, we propose an ensemble scheme for the detection of attacks in the EC. Our distributed scheme relies on the adoption of deep learning to proactively detect potential malfunctions. Our model is embedded in EC nodes and is continuously applied upon the streams of data transferred by IoT devices to the EC. We present the details of our approach and evaluate it through a variety of simulation scenarios. Our intention is to reveal the strengths and weaknesses of the provided model when adopted in a very dynamic environment like the EC.
Low-Complexity Lattice Reduction Aided Schnorr Euchner Sphere Decoder Detection Schemes with MMSE and SIC Pre-processing for MIMO Wireless Communication Systems

The LRAD-MMSE-SIC-SE-SD (Lattice Reduction Aided Detection - Minimum Mean Squared Error-Successive Interference Cancellation - Schnorr Euchner - Sphere Decoder) detection scheme that introduces a trade-off between performance and computational complexity is proposed for Multiple-Input Multiple-Output (MIMO) in this paper. The Lenstra-Lenstra-Lovász (LLL) algorithm is employed to orthogonalise the channel matrix by transforming the signal space of the received signal into an equivalent reduced signal space. A novel Lattice Reduction aided SE-SD probing for the Closest Lattice Point in the transformed reduced signal space is hereby proposed. Correspondingly, the computational complexity of the proposed LRAD-MMSE-SIC-SE-SD detection scheme is independent of the constellation size while it is polynomial with reference to the number of antennas, and signal-to-noise-ratio (SNR). Performance results of the detection scheme indicate that SD complexity is significantly reduced at only marginal performance penalty.
Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained Recognition

Manually labeling fine-grained datasetsis laborious and typically requires domain-specific expert knowledge. Conversely, a vast amount of web data is relatively easy to obtain with nearly no human effort. Therefore, learning from noisy web data for fine-grained tasks is attracting increasing attention in recent years. However, the presence of noise in web images is a huge obstacle for training robust fine-grained recognition models. To this end, we propose a novel approach to identify noisy images as well as specifically distinguish in- and out-of-distribution samples. It can purify the noisy web training set by discarding out-of-distribution noise and relabeling in-distribution noisy samples. Then we can train the model on the purified dataset to alleviate the harmful effects of noise and make the most of web images to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: https://github.com/NUST-Machine-Intelligence-Laboratory/Dataset-Purification.
Time-frequency Based EEG Motor Imagery Signal Classification with Deep Learning Networks

In this research, a wavelet transform-based feature extraction approach with time-frequency analysis is proposed for motor imaginary EEG signal classification. The proposed approach selects specific channels such as C3 and C4 to identify event-related synchronization (ERS) or event-related desynchronization (ERD) phenomenon to filter out the artifacts and noisy data from signals. As EEG dataset is noisy and size of the dataset reduces after filtering, the proposed approach adopts multi-scale analysis ability of wavelet transform to utilize small input. It allows to extract features from the dataset and generate input images for training the models. Considering abstraction ability of Convolutional Neural Network (CNN), deep CNN with two convolutional layers, and VGGnet with six convolutional layers are employed. The model performance is evaluated in terms of accuracy, loss, and epochs. The proposed approach is applied to EEG dataset III from BCI competition II. The primary results show that VGGnet performs better than deep CNN with respect to training loss and training accuracy.
Simulation of 5G Dense-Urban Deployment at 3.5 GHz Frequency: A Case Study of Central Jakarta

The number of data service customers is expected to rise exponentially in Indonesia starting in 2021, making the current 4G cellular service quality inadequate. Therefore, before deploying 5G New Radio (NR), it is crucial to prepare the simulations to understand its performance. This can be done by determining the Maximum Allowable Path Loss (MAPL) and obtaining the signal strength level from the Synchronization Signal-Reference Signal Received Power (SS-RSRP) and the signal quality level from the Synchronization Signal-Signal Interference Noise Ratio (SS-SINR) of the mid-band 3.5 GHz frequency. This research aims to determine these values and simulate the results using a commercial network simulator software, Forsk Atoll V3.4, across the whole 52.38 square kilo-meters of dense-urban and the main economic area of Indonesia, Central Jakarta. An urban macro (UMa) propagation model based on the 3GPP TR 38.901 standard is utilized for uplink and downlink circumstances, each employing an Outdoor-to-Outdoor (O2O) Line of Sight (LOS) and a Non-Line of Sight (NLOS) scheme. The average values of the SS-RSRP and SS-SINR in the LOS scheme are −104.71 and 8.8 dB, respectively. Meanwhile, in the NLOS scheme, the SS-RSRP and SS-SINR average values are −87.03 dBm and 10.48 dB, respectively. The NLOS scheme shows superior performance than the LOS scheme.
A Novel 3-D Printed Microwave Probe for ISM Band Ablation Systems of Breast Cancer Treatment Applications

In this article, a 3-D printed microwave (MW) probe is proposed to operate within 2.4–2.5-GHz industrial, scientific, and medical (ISM) band along with the numerical and experimental results for the local annihilation of contained tumor tissue that is the type of a human breast cancer cell line (MCF7) in MW ablation systems. The proposed MW ablation probe is fabricated by using an acrylonitrile butadiene styrene (ABS) material to construct a lightweight, cost effective, and easy to fabricate an MW applicator. The design principle of 3-D printed MW probe is based on the structural formation of coaxial transmission-line fed dielectric-loaded cone-shaped radiator for the near-field energy concentration in the close proximity of the cancerous tissue to be ablated at 2.45 GHz. Since conductive coating techniques have been improved recently, the inner surfaces of the designed MW applicator have been coated with the copper spray conveniently for the final fabricated probe prototype. The designed MW applicator has been numerically studied in CST MW Studio in an attempt to develop the near-field radiation performance, which can affect the ablation performance of MCF7 cells. The computation and experimental measurement results agree quite well with the indication of 3-D printed MW ablation probe to be utilized as an alternative medical device for cancer treatment.
Sparse Reduced-Rank Regression With Adaptive Selection of Groups of Predictors

Sparse reduced-rank regression (SRRR) model has been widely used for dimension reduction and variable selection with wide applications in machine learning, signal processing, econometrics, etc. In this paper, the SRRR estimation problem is investigated which aims at adaptively selecting the whole group of predictors. The problem is formulated to minimize the least squares loss with a sparsity-inducing penalty. For effective variable selection, a novel sparse estimation scheme, called Sorted L-One Penalized Estimation (SLOPE), is considered, where a group-SLOPE type penalty is adopted. An algorithm leveraging the block majorization-minimization framework is developed for efficient problem resolution. Simulations on synthetic data illustrate the effectiveness of the proposed algorithm. The competitive performance of the proposed model in prediction accuracy is numerically demonstrated on real data with comparison to the state-of-the-art methods.
VLSI Hardware Architecture of Stochastic Low-rank Tensor Decomposition

Canonical polyadic decomposition (CPD) is one of the most popular tensor decomposition approaches used in various practical applications. Recently, AdaCPD, a doubly randomization-based reduced-complexity CPD, is proposed with exhibiting high decomposition performance. However, facing the similar challenges of all the other dense tensor-oriented decomposition methods, AdaCPD is still computation-intensive and thus poses severe challenges for practical deployment on resource-constrained platforms and real-time applications. In this paper, we propose an efficient hardware architecture for AdaCPD accelerator. With customized hardware design and memory optimization at the architecture level, the proposed AdaCPD hardware accelerator enables significant performance improvement than conventional software implementation. Evaluation results show that this customized hardware design only occupies 1.6 mm2 area and consumes 300 mW power with CMOS 28nm technology, thereby bringing 6762 times and 1483 times reduction over the software implementation on Intel Core i9-9900K CPU in terms of area and energy efficiency, respectively.
Automated Analysis of Neuronal Morphology through an Unsupervised Classification Model of Neurites

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these billions of often far-reaching connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering often mm distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and error prone. Especially fully automated segmentation and classification of all neurites remain unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in fluorescence micrographs with minimal requirements for user interaction. Neurons are segmented using a Hessian-based algorithm to detect thin neurite structures combined with intensity- and shape-based detection of the cell body. To measure the number of branches in a neuron accurately, rather than just determining branch points, neurites are classified into axon, dendrites and their branches of increasing order by their length using a geodesic distance transform of the cell skeleton. The software was benchmarked against a large, published dataset and reproduced the phenotype observed after manual annotation before. Our tool promises greatly accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.
CMV reactivation initiates long-term expansion and differentiation of the NK cell repertoire

Introduction NK cells play an important role in suppression of viral replication and are critical for effective control of persistent infections such as herpesviruses. Cytomegalovirus infection is associated with expansion of ‘adaptive-memory’ NK cells with a characteristic CD56dimCD16bright NKG2C+ phenotype but the mechanisms by which this population is maintained remain uncertain. Methods We studied NK cell reconstitution in patients undergoing haemopoietic stem cell transplantation and related this to CMV reactivation. Results NK cells expanded in the early post-transplant period but then remained stable in the absence of viral reactivation. However, CMV reactivation led to a rapid and sustained 10-fold increase in NK cell number. The proportion of NKG2C-expressing cells increases on all NK subsets although the kinetics of expansion peaked at 6 months on immature CD56bright cells whilst continuing to rise on the mature CD56dim pool. Phenotypic maturation was observed by acquisition of CD57 expression. Effective control of viral reactivation was seen when the peripheral NK cell count reached 20,000/ml. Discussion These data show that short term CMV reactivation acts to reprogramme hemopoiesis to drive a sustained modulation and expansion of the NK cell pool and reveal further insight into long term regulation of the innate immune repertoire by infectious challenge.
Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.
A feature-specific prediction error model explains dopaminergic heterogeneity

The hypothesis that midbrain dopamine (DA) neurons broadcast an error for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1–3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. For instance, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward4. We argue that the previously predominant family of extensions to the RPE model, which replicate the classic model in multiple parallel circuits, are ill-suited to explaining these and other results concerning DA heterogeneity within the VTA. Instead, we introduce a complementary “feature-specific RPE” model positing that DA neurons within VTA report individual RPEs for different elements of a population vector code for an animal’s state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task and compare the feature-specific RPE derived from the network to population recordings from DA neurons during the same task. The model recapitulates key aspects of VTA DA neuron heterogeneity. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among SNc DA neurons5. Thus, our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.
Redundant PLETHORA activity promotes development of early embryonic cell lineages in Arabidopsis

The BABY BOOM/PLETHORA4 (BBM/PLT4) transcription factor has received much attention due to its ability to induce somatic and zygotic embryogenesis, two processes of pivotal importance in plant breeding. Loss of additional AINTEGUMENTA-LIKE/PLETHORA (AIL/PLT) genes, encoding members of the APETALA2 transcription factor family, causes embryo arrest and abortion, but whether BBM/PLT4 provides specific information for embryo development has remained unknown. Here, we reveal that AIL/PLT members are expressed in partially overlapping domains from their first appearance in the apical cell daughter of the zygote. Redundant early embryonic activity of BABY BOOM/PLT4 and PLT2 triggers development of the apical cell lineage and is required to initiate embryonic primordia. Furthermore, promoter swap experiments show that PLT1 and PLT3 expression in the expression domains of PLT2 and BABY BOOM/PLT4 is sufficient to rescue plt2 bbm double mutants. Our data indicate that generic AIL/PLT factors, involved in maintenance of stem cells, promotion of cell division and suppression of cell differentiation, provide the necessary information to initiate embryogenesis in Arabidopsis.
Sights and sounds dolphins, Tursiops truncatus preying on native fish of San Diego Bay and offshore in the Pacific Ocean

For the first time, dolphins wearing video cameras were observed capturing and eating live native fish. While freely swimming in San Diego Bay, one dolphin caught 69 resident fish, 64 demersal, 5 near surface, while the other caught 40, 36 demersal and 4 near the surface. Two other dolphins were observed capturing 135 live native fish in a sea water pool. Two additional dolphins were observed feeding opportunistically during open water sessions in the Pacific Ocean. Notably, one of these dolphins was observed to consume 8 yellow-bellied sea snakes (Hydrophis platurus). Searching dolphins clicked at intervals of 20 to 50 ms. On approaching prey, click intervals shorten into a terminal buzz and then a squeal. Squeals were bursts of clicks that varied in duration, peak frequency, and amplitude. Squeals continued as the dolphin seized, manipulated and swallowed the prey. If fish escaped, the dolphin continued the chase and sonar clicks were heard less often than the continuous terminal buzz and squeal. During captures, the dolphins’ lips flared to reveal nearly all of the teeth. The throat expanded outward. Fish continued escape swimming even as they entered the dolphins’ mouth, yet the dolphin appeared to suck the fish right down.
Deep Learning in Spatial Transcriptomics: Learning From the Next Next-Generation Sequencing

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. The data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially-resolved data (such as spatial resolution, sensitivity and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering among others. However, deep-learning models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially-resolved transcriptomics, while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight the domains in which we anticipate transformational DL applications.
Agency as Planning for Known Unknowns

The utility of a given experience, like interacting with a particular friend or tasting a particular food, fluctuates continually according to homeostatic and hedonic principles. Consequently, to maximize reward, an individual must be able to escape or attain outcomes as preferences change, by switching between actions. Recent work on human and artificial intelligence has defined such control in information theoretic terms. However, information theoretic measures do not necessarily correspond to an agent’s ability to escape or obtain sensory-specific states. In the current study, choice behavior was best described by a forward consideration of action values based on possible changes in outcome utilities. Consistent with previous work on agency and personality, individual differences in dimensional schizotypy were correlated with choice preferences in conditions with the greatest and lowest levels of instrumental control. The results contribute to a growing literature on the role of agency in goal-directed choice.
A Standardized Framework for Taxonomy Analysis in Bacterial Kingdom based on Protein Domain

The taxonomy research on the bacterial kingdom is important and can be usually conducted by the analysis on conserved genes, 16s rRNA, protein domain, and so on. Comparatively, the protein domains maintain a most direct relationship with phenotypes. In this paper, based on the protein domain, we propose a 3-step framework to standardize the classification process of bacteria. Different model candidates are involved and discussed in each step. By comparing the classification results with existing taxonomy, we select the most appropriate candidate to improve the framework, and furthermore, discuss their biological significance. Finally, we put forward taxonomy suggestions based on the best classification results. Importance We standardize a 3-step framework to carry out bacterial taxonomy research based on protein domain. Furthermore, we filter out the best solution in each step that can together generate the most appropriate classification result, and at the same time, we discuss the biological significant it indicates. Finally, we propose suggestions on NCBI bacterial taxonomy based on the classification results.