Miracle-dz/UniFAR-FTUs huggingface dataset:
Filesystem Encryption or Direct-Access for NVM Filesystems? Let’s Have Both!

Emerging Non-Volatile Memories (NVMs) are promising candidates to build ultra-low idle power memory and storage devices in future computing systems. Unlike DRAM, NVMs do not require frequent refresh operations, and they can retain data after crashes and power loss. With such features, NVM memory modules can be used partly as a conventional memory to host memory pages and partly as file storage to host filesystems and persistent data. Most importantly, and unlike current storage technologies, NVMs can be directly attached to the memory bus and accessed through conventional load/store operations.As NVMs feature ultra-low access latency, it is necessary to minimize software overheads for accessing files to enable the full potential. In legacy storage devices, e.g., Flash and Harddisk drives, access latency dominates the software overheads. However, emerging NVMs’ performance can be burdened by the software overheads since memory access latency is minimal. Modern Operating Systems (OSes) allow direct-access (DAX) for NVM-hosted files through direct load/store operations by eliminating intermediate software layers. Unfortunately, we observe that such a direction ignores filesystem encryption and renders most of the current filesystem encryption implementations inapplicable to future NVM systems. In this paper, we propose a novel hardware/software co-design architecture that enables transparent filesystem encryption without sacrificing the direct-access feature of files in emerging NVMs with minimal change in OS and memory controller. Our proposed model incurs a negligible overall slowdown of 3.8% for workloads representative of real-world applications, while software-based encryption can incur as high as 5x slowdown for some applications.
A 112 Gb/s -8.2 dBm Sensitivity 4-PAM Linear TIA in 16nm CMOS with Co-Packaged Photodiodes

Low-cost optical receivers (RX) operating at 100+ Gb/s 4-PAM with low power are in high demand to support 400GBASE-DR4/FR4 links in data centers. Existing pluggable solutions generally realize the RX front-end in BiCMOS. However, a more integrated solution, with the RX front-ends integrated onto a CMOS host IC and co-packaged alongside the photodiodes (PDs), offers the potential for smaller size, lower cost, and lower power [1], [2]. This work demonstrates a 112 Gb/s 4-PAM linear TIA in CMOS flip-chip co-packaged with commercial PDs and different PD-to-RX interconnect lengths (Fig. 1a).
Pathway and directional specificity of Hebbian plasticity induction in the cortical visual motion processing network

Cortico-cortical paired associative stimulation (ccPAS), which repeatedly pairs single pulse TMS over two distant brain regions with a specific time interval, is thought to modulate synaptic plasticity. Applied to the motion cortical pathway, ccPAS has been shown to improve motion discrimination when specifically targeting backward projections, stimulating the medio-temporal area (MT) followed by the primary visual cortex (V1). However, there is no direct neuroimaging evidence of the spatial selectivity of the ccPAS effects (i.e., pathway or direction specificity) or detailing the exact nature of the ccPAS effects (i.e., the oscillatory signature, timing…). In this study, we applied ccPAS along the motion discrimination pathway, in the top-down direction (MT-to-V1: “Backward ccPAS”) and in the bottom-up direction (V1-to-MT: “Forward ccPAS”) in sixteen healthy volunteers and compared changes in visual network activity in response to single pulse TMS over V1 and MT using spectral granger causality (sGC). The sGC results showed common increases in direct V1-to-MT and V1-to-IPS bottom-up inputs in the high Beta/low Gamma band (25-40 Hz) for both ccPAS, probably reflecting task exposure. However, a clear distinction in information transfer occurred in the re-entrant MT-to-V1 signals, which were only modulated by Backward ccPAS. This difference was predictive of the behavioural improvements at the motion discrimination task. Our results support the view of the possibility to specifically enhance re-entrant Alpha oscillatory signals from MT-to-V1 to promote motion discrimination performance through Backward ccPAS. These findings contribute to better understanding visual processing in healthy subjects and how it can be modulated to pave the way to clinical translation in vision handicapped patients. The changes in re-entrant MT-to-V1 inputs could help to provide single-subject prediction scenarios in patients suffering from a visual system stroke, in whom visual recovery might partly rely on the top-down inputs to the spared V1 neurons.
3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning

Cryo-soft X-ray tomography (cryo-SXT) is a powerful method to investigate the ultrastructure of cells, offering resolution in the tens of nm range and strong contrast for membranous structures without requirement for labeling or chemical fixation. The short acquisition time and the relatively large volumes acquired allow for fast acquisition of large amounts of tomographic image data. Segmentation of these data into accessible features is a necessary step in gaining biologically relevant information from cryo-soft X-ray tomograms. However, manual image segmentation still requires several orders of magnitude more time than data acquisition. To address this challenge, we have here developed an end-to-end automated 3D-segmentation pipeline based on semi-supervised deep learning. Our approach is suitable for high-throughput analysis of large amounts of tomographic data, while being robust when faced with limited manual annotations and variations in the tomographic conditions. We validate our approach by extracting three-dimensional information on cellular ultrastructure and by quantifying nanoscopic morphological parameters of filopodia in mammalian cells.
TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformer

Transformer-based models are state-of-the-art for many machine learning (ML) tasks. Executing Transformer usually requires a long execution time due to the large memory footprint and the low data reuse rate, stressing the memory system while under-utilizing the computing resources. Memory-based processing technologies, including processing in-memory (PIM) and near-memory computing (NMC), are promising to accelerate Transformer since they provide high memory bandwidth utilization and extensive computation parallelism. However, the previous memory-based ML accelerators mainly target at optimizing dataflow and hardware for compute-intensive ML models (e.g., CNNs), which do not fit the memory-intensive characteristics of Transformer. In this work, we propose TransPIM, a memory-based acceleration for Transformer using software and hardware co-design. In the software-level, TransPIM adopts a token-based dataflow to avoid the expensive inter-layer data movements introduced by previous layer-based dataflow. In the hardware-level, TransPIM introduces lightweight modifications in the conventional high bandwidth memory (HBM) architecture to support PIM-NMC hybrid processing and efficient data communication for accelerating Transformer-based models. Our experiments show that TransPIM is 3.7× to 9.1× faster than existing memory-based acceleration. As compared to conventional accelerators, TransPIM is 22.1× to 114.9× faster than GPUs and provides 2.0× more throughput than existing ASIC-based accelerators.
Arbuscular mycorrhizae reduce stress-induced plasticity of plant functional traits. A meta-analysis study

We aimed at exploring the plant functional traits whose stress-induced plasticity is altered by the presence of AM fungi, considering the direction of their changes. We also sought for a coordinated variation of plant biomass and functional traits, during plant adaptation to environmental stressors, and the role of AM status on the variation. We performed a meta-analysis across 114 articles spanning 110 plant species or cultivars. We quantified the size effect of AM symbiosis on the stress-induced plasticity of several reported and calculated functional traits, and using linear mixed model analysis (LMM). Correlation between traits plasticity and total biomass variation were also performed through LMM. The literature search and further selection yielded seven functional traits, extracted from 114 laboratory studies, including 888 observations and 110 plant species/cultivars. Evidence for significant effects of predictor variables (type of stress, AM symbiosis and/or their interaction) on plasticity were found for three of these functional traits: leaf-area ratio (LAR), root mass fraction (RMF) and root-shoot (R:S) ratio. Our results provided evidence to accept the hypothesis that AM fungal inoculation may reduce the phenotypic plasticity of important plant functional traits leaf area ratio (LAR), root mass fraction (RMF) and root-shoot (R:S) ratio, by decreasing its magnitude. We also found a weak correlation between traits plasticity and total biomass variation. Although our literature search and data collection were intensive and our results robust, the scope of our conclusions is limited by the agronomical bias of plant species targeted by the meta-analysis. Further knowledge on non-cultivable plant species and better understanding of the mechanisms ruling resources allocation in plants would allow more generalized conclusions.
Surgical Procedure Understanding, Evaluation, and Interpretation: A Dictionary Factorization Approach

In this study, we present a novel machine learning-based technique to help surgical mentors assess surgical motion trajectories and corresponding surgical skills levels in surgical training programs. The proposed method is a variation of sparse coding and dictionary learning that is straightforward to optimize and produces approximate trajectory decomposition for structured tasks. Our approach is superior to existing stochastic or deep learning-based methods in terms of transparency of the model and interpretability of the results. We introduce a dual-sparse coding algorithm which encourages the elimination of redundant and unnecessary atoms and targets to reach the most informative dictionary, representing the most important temporal variations within a given surgical trajectory. Since surgical tool trajectories are time series signals, we further incorporate the idea of floating atoms along the temporal axis in trajectory analysis, which improves the model’s accuracy and prevents information loss in downstream tasks. Using JIGSAWS data set, we present preliminary results showing the feasibility of the proposed method for clustering and interpreting surgical trajectories in terms of user’s skills-related behaviors.
A 177 TOPS/W, Capacitor-based In-Memory Computing SRAM Macro with Stepwise-Charging/Discharging DACs and Sparsity-Optimized Bitcells for 4-Bit Deep Convolutional Neural Networks

Capacitor-based in-memory computing (IMC) SRAM has recently gained significant attention as it achieves high energy-efficiency for deep convolutional neural networks (DCNN) and robustness against PVT variations [1], [3], [7], [8]. To further improve energy-efficiency and robustness, we identify two places of bottleneck in prior capacitive IMC works, namely (i) input drivers (or digital-to-analog converters, DACs) which charge and discharge various capacitors, and (ii) analog-to-digital converters (ADCs) which convert analog voltage/current signals into digital values.
Data Mining Techniques Applied to Recommender Systems for Outdoor Activities: A Systematic Literature Review

: Currently, many pollutants are released into the air, representing a risk to the environment and human health. There are significant volumes of data generated by the devices that monitor these pollutants. This information can represent a relevant input that allows the construction of applications, techniques, and methodologies to reach a prediction of the state of the air. On the other hand, recommender systems are present in numerous data processing methods, supporting the decision-making and promoting the improvement of the quality of service of solutions. Although several studies have been presented, no secondary studies have been proposed. Therefore, this paper presents a systematic review of the literature, which aims to identify the knowledge areas, tools, methods, and data mining approaches used in recommender systems for outdoor activities related to atmospheric pollutants. The results obtained contribute to creating new ways of recommendation systems based on the previous topics.
A deep representation learning speech enhancement method using β-VAE

In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL). Based on our previous work, we in this paper propose to use β-VAE to further improve PVAE's ability of representation learning. More specifically, our β-VAE can improve PVAE's capacity of disentangling different latent variables from the observed signal without the trade-off problem between disentanglement and signal reconstruction. This trade-off problem widely exists in previous β-VAE algorithms. Unlike the previous β-VAE algorithms, the proposed β-VAE strategy can also be used to optimize the DNN's structure. This means that the proposed method can not only improve PVAE's SE performance but also reduce the number of PVAE training parameters. The experimental results show that the proposed method can acquire better speech and noise latent representation than PVAE. Meanwhile, it also obtains a higher scale-invariant signal-to-distortion ratio, speech Quality, and speech intelligibility.
Combining Spectral, Spatial-Contextual, and Structural Information in Multispectral UAV Data for Spruce Crown Delineation

Precise delineation of individual tree crowns is critical for accurate forest biophysical parameter estimation, species classification, and ecosystem modelling. Multispectral optical remote sensors mounted on low-flying unmanned aerial vehicles (UAVs) can rapidly collect very-high-resolution (VHR) photogrammetric optical data that contain the spectral, spatial, and structural information of trees. State-of-the-art tree crown delineation approaches rely mostly on spectral information and underexploit the spatial-contextual and structural information in VHR photogrammetric multispectral data, resulting in crown delineation errors. Here, we propose the spectral, spatial-contextual, and structural information-based individual tree crown delineation (S3-ITD) method, which accurately delineates individual spruce crowns by minimizing the undesirable effects due to intracrown spectral variance and nonuniform illumination/shadowing in VHR multispectral data. We evaluate the performance of the S3-ITD crown delineation method over a white spruce forest in Quebec, Canada. The highest mean intersection over union (IoU) index of 0.83, and the lowest mean crown-area difference (CAD) of 0.14 m2, proves the superior crown delineation performance of the S3-ITD method over state-of-the-art methods. The reduction in error by 2.4 cm and 1.0 cm for the allometrically derived diameter at breast height (DBH) estimates compared with those from the WS-ITD and the BF-ITD approaches, respectively, demonstrates the effectiveness of the S3-ITD method to accurately estimate biophysical parameters.
A Meta-analytical View on the Acceptance of Mhealth Apps

: Never before has there been such a driving transformation of healthcare concerning digitalization as in early 2020 due to the pandemic. The digital transformation in the health sector holds great potential to meet the challenges of demographic change, the ﬁnancial burden on the health system and the shortage of skilled workers. One digital solution is the usage of modern information and communication technologies (ICT), such as mhealth applications (mhealth apps). However, the usage of mhealth apps especially in the German healthcare system is still evolving and the usage rate of mhealth apps among the population is rather low. To understand acceptance-relevant factors for the usage, this meta-analytical study presents two samples before and during pandemic times (survey times 2019 and 2021). To analyse these samples the UTAUT2 acceptance model in an extended version was applied. The additional factors are online privacy, trust and ehealth literacy. In total 644 participants took part. The results show, that the factors habit, hedonic motivation, performance expectancy, social inﬂuence and effort expectancy contribute most strongly to the intention to use mhealth apps as well as the added factor trust. The decisive factor for the user proﬁles was age.
Sleep Deprivation During Memory Consolidation, but Not Before Memory Retrieval, Widens Threat Generalization to New Stimuli

Past aversive experiences shape our ability to deal with future dangers, through the encoding of implicit and explicit memory traces and through the ability to generalize defensive reactions to new stimuli resembling learned threats. Numerous evidence demonstrate that sleep is important for the consolidation of memories related to threatening events. However, there is a lack of studies examining the effects of sleep deprivation on the retrieval of consolidated threat memories, and previous studies on the role of sleep in threat generalization have produced mixed results. To address these issues, here we adopted a differential threat conditioning and a delayed (second half of the night) sleep deprivation during the first or the seventh night after learning. We found no effects of sleep deprivation on either implicit or explicit threat memories, regardless of its occurrence timing. Conversely, implicit but not explicit responses to novel cues similar to a learned threat displayed a widened generalization pattern, but only if sleep deprivation took place during the first night after conditioning and not if it occurred during the seventh night after conditioning. Therefore, we propose that sleeping after exposure to danger may support optimal implicit discrimination processes to evaluate new signals in the future and that even a brief period of sleeplessness may widen threat generalization to new stimuli, which is a hallmark of several threat-related disorders.
A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.
Crosstalk of Exosomal Non-Coding RNAs in The Tumor Microenvironment: Novel Frontiers

The tumor microenvironment (TME) is defined as a complex and dynamic tissue entity composed of endothelial, stromal, immune cells, and the blood system. The homeostasis and evolution of the TME are governed by intimate interactions among cellular compartments. The malignant behavior of cancer cells, such as infiltrating growth, proliferation, invasion, and metastasis, is predominantly dependent on the bidirectional communication between tumor cells and the TME. And such dialogue mainly involves the transfer of multifunctional regulatory molecules from tumor cells and/or stromal cells within the TME. Interestingly, increasing evidence has confirmed that exosomes carrying regulatory molecules, proteins, and nucleic acids act as an active link in cellular crosstalk in the TME. Notably, extensive studies have identified non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), that could be encapsulated by exosomes, which regulate the coordinated function within the TME and thus participate in cancer development and progression. In this review, we summarize recent literature around the topic of the functions and mechanisms of exosomal ncRNAs in the TME and highlight their clinical significance.
Speaker Accent Modulates the Effects of Orthographic and Phonological Similarity on Auditory Processing by Learners of English

The cognate effect refers to translation equivalents with similar form between languages—i.e., cognates, such as “band” (English) and “banda” (Spanish)—being processed faster than words with dissimilar forms—such as, “cloud” and “nube. ” Substantive literature supports this claim, but is mostly based on orthographic similarity and tested in the visual modality. In a previous study, we found an inhibitory orthographic similarity effect in the auditory modality—i.e., greater orthographic similarity led to slower response times and reduced accuracy. The aim of the present study is to explain this effect. In doing so, we explore the role of the speaker's accent in auditory word recognition and whether native accents lead to a mismatch between the participants' phonological representation and the stimulus. Participants carried out a lexical decision task and a typing task in which they spelled out the word they heard. Words were produced by two speakers: one with a native English accent (Standard American) and the other with a non-native accent matching that of the participants (native Spanish speaker from Spain). We manipulated orthographic and phonological similarity orthogonally and found that accent did have some effect on both response time and accuracy as well as modulating the effects of similarity. Overall, the non-native accent improved performance, but it did not fully explain why high orthographic similarity items show an inhibitory effect in the auditory modality. Theoretical implications and future directions are discussed.