Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Convergence Behavior of Partitioned-Block Frequency-Domain Adaptive Filters

Partitioned-block frequency-domain adaptive filter (PBFDAF) algorithms have become very popular, in particular, for acoustic signal processing. However, the stochastic behavior of PBFDAFs has not been extensively examined. This paper presents a comprehensive statistical analysis for a family of the overlap-save PBFDAF algorithms with 50% overlap, including the transient and steady-state performance. The frequency-domain equations of the PBFDAFs are transformed into the time-domain counterparts, which allows us to carry out the analysis completely in the time domain. By means of the independence assumption and vectorization operation, theoretical models for the mean and mean-square behavior of the PBFDAF algorithms are established without restricting the distribution of the inputs to being Gaussian. Specifically, the mean weight behavior and corresponding steady-state solution are provided. Closed-form expressions for the mean-square deviation (MSD) and mean-square error (MSE) are derived. The upper bound on the step size for the mean and mean-square stability of the PBFDAFs is specified. The theoretical model presents new insights into the convergence properties of the PBFDAFs with a sufficient number of coefficients. It was revealed that both the constrained and unconstrained PBFDAFs converge to the Wiener solution. However, the mean weight vector of the unconstrained PBFDAF algorithms cannot converge to the true solution for any inputs, while that of the constrained version can. The presented theory explains why the steady-state MSD of the unconstrained PBFDAF algorithm is larger than that of the constrained version but their minimum MSE is the same. Monte Carlo simulations provide very good support for our theory.
Sharpening Sparse Regularizers via Smoothing

Non-convex sparsity-inducing penalties outperform their convex counterparts, but generally sacrifice the cost function convexity. As a middle ground, we propose the sharpening sparse regularizers (SSR) framework to design non-separable non-convex penalties that induce sparsity more effectively than convex penalties such as $\ell _1$ and nuclear norms, but without sacrificing the cost function convexity. The overall problem convexity is preserved by exploiting the data fidelity relative strong convexity. The framework constructs penalties as the difference of convex functions, namely the difference between convex sparsity-inducing penalties and their smoothed versions. We propose a generalized infimal convolution smoothing technique to obtain the smoothed versions. Furthermore, SSR recovers and generalizes several non-convex penalties in the literature as special cases. The SSR framework is applicable to any sparsity regularized least squares ill-posed linear inverse problem. Beyond regularized least squares, the SSR framework can be extended to accommodate Bregman divergence, and other sparsity structures such as low-rankness. The SSR optimization problem can be formulated as a saddle point problem, and solved by a scalable forward-backward splitting algorithm. The effectiveness of the SSR framework is demonstrated by numerical experiments in different applications.
A Differentiating Receiver With a Transition-Detecting DFE for Dual-Rank Mobile Memory Interface

The signal integrity of dual-rank LPDDR5 interface is challenged by reflections from the stub of the inactive rank within the redistribution layer (RDL). These reflections arrive at the active rank within the unit-interval of the data-rate and distort not only the post-cursor but also the pre-cursor of the signal that the decision feedback equalizer (DFE) cannot remove. We introduce a differentiating receiver that restores data through edge information to eliminate the reflective distortions, including the pre-cursor, and a transition-detecting DFE to address remaining inter-symbol interference. A prototype receiver, fabricated in a 65 nm CMOS process, eliminates pre-cursor distortion with a reflection flight time of 50 ps and 70 ps, at 8 Gb/s and 6.4 Gb/s, resulting in a wide horizontal margin at a bit-error-rate of 10−12. The energy efficiency is 0.80 pJ/bit at 8 Gb/s.
A Compact Frequency Scanning Planar Array Using Characteristic Mode Analysis

In this letter, a compact planar array with frequency scanning characteristics is proposed. Based on the characteristic mode analysis (CMA), we found that there distribute four modes with distinctive radiation patterns and phase characteristics. And combining them in pairs would generate scanning beams within a narrow band. In addition, CMA offers us useful guidelines on enhancing the gain and suppressing the sidelobes. A prototype was fabricated and measured. Under the excitation of a single port, namely <inline-formula><tex-math notation="LaTeX">$P_{1}$</tex-math></inline-formula>, the scanning range was from <inline-formula><tex-math notation="LaTeX">$14^{\circ }$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$51^{\circ }$</tex-math></inline-formula> with sidelobe level (SLL) lower than −20 dB. As switching the port from <inline-formula><tex-math notation="LaTeX">$P_{1}$</tex-math></inline-formula> to its symmetrical port, <inline-formula><tex-math notation="LaTeX">$P_{2}$</tex-math></inline-formula>, the scanning scope in the <italic>E</italic>-plane would be extended to the mirrored space from <inline-formula><tex-math notation="LaTeX">$-14^{\circ }$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$-51^{\circ }$</tex-math></inline-formula>.
sEMG-based Regression of Hand Kinematics with Temporal Convolutional Networks on a Low-Power Edge Microcontroller

Human-Machine Interfaces based on gesture control are a very active field of research, aiming to enable natural interaction with objects. Nowadays, one of the most promising State-of-the-Art (SoA) methodology for robotic hand control relies on the surface electromyographic (sEMG) signal, a non-invasive approach that can provide accurate and intuitive control when coupled with decoding algorithms based on Deep Learning (DL). However, the vast majority of the approaches so far have focused on sEMG classification, producing control systems that limit gestures to a predefined set of positions. In contrast, sEMG regression is still a new field, providing a more natural and complete control method that returns the complete hand kinematics. This work proposes a regression framework based on TEMPONet, a SoA Temporal Convolutional Network (TCN) for sEMG decoding, which we further optimize for deployment. We test our approach on the NinaPro DB8 dataset, targeting the estimation of 5 continuous degrees of freedom for 12 subjects (10 able-bodied and 2 trans-radial amputees) performing a set of 9 contralateral movements. Our model achieves a Mean Absolute Error of 6.89°, which is 0.15° better than the SoA. Our TCN reaches this accuracy with a memory footprint of only 70.9 kB, thanks to int8 quantization. This is remarkable since high-accuracy SoA neural networks for sEMG can reach sizes up to tens of MB, if deployment-oriented reductions like quantization or pruning are not applied. We deploy our model on the GAP8 edge microcontroller, obtaining 4.76 ms execution latency and an energy cost per inference of 0.243 mJ, showing that our solution is suitable for implementation on resource-constrained devices for real-time control.
Discussion of cross-axis isolation in vector atomic magnetometry via longitudinal field modulation

Atomic magnetometry based on longitudinal field modulation allows transverse x-and y-components measurement of a magnetic field simultaneously. In this paper, cross-axis isolation between these two-axis components is derived and investigated mathematically. An analytical model is established to optimize the cross-axis isolation during the transversal components measurement. By manipulating the amplitude and the frequency of the longitudinal field modulation, two transverse magnetic field components could be divided completely. The proposed technique plays significant role in optimization of vectoral atomic magnetometry.
Design and Implementation of a Full-Bridge LLC Converter With Wireless Power Transfer for Dual Mode Output Load

The proposed research is to accomplish a converter for driving DC load or AC load with wireless power transfer (WPT) technique. In this novel scheme, a full-bridge LLC resonant converter is adopted to deliver the energy from the primary to the secondary side. Besides, the LLC converter has soft-switching characteristic of the switches at the primary to improve the efficiency of the converter if the switching frequency is selected appropriately. In order to achieve the feature of wireless power transfer, the WPT (transmitting/receiving) coils are utilized to replace the transformer. The leakage inductor can be treated as a resonant inductor integrated in the transmitting coil. In the output side, a sinusoidal pulse width modulation (SPWM) technique is added to generate AC voltage for specific load. In other words, the proposed converter can output DC or AC voltage by user’s demand. In the end of this research, a LLC converter with WPT technique for 1kW is implemented. The experimental results and the related waveforms are also shown in the content. In this topology, the highest efficiency of DC output is 89.2%, and the highest efficiency of AC output is 82.6% respectively.
Adaptive cognitive maps for curved surfaces in the 3D world

Terrains in a 3D world can be undulating. Yet, most prior research has exclusively investigated spatial representations on a flat surface, leaving a 2D cognitive map as the dominant model in the field. Here, we investigated whether humans represent a curved surface by building a dimension-reduced flattened 2D map or a full 3D map. Participants learned the location of objects positioned on a flat and curved surface in a virtual environment by driving on the concave side of the surface (Experiment 1), driving and looking vertically (Experiment 2), or flying (Experiment 3). Subsequently, they were asked to retrieve either the path distance or the 3D Euclidean distance between the objects. Path distance estimation was good overall, but we found a significant underestimation bias for the path distance on the curve, suggesting an influence of potential 3D shortcuts, even though participants were only driving on the surface. Euclidean distance estimation was better when participants were exposed more to the global 3D structure of the environment by looking and flying. These results suggest that the representation of the 2D manifold, embedded in a 3D world, is neither purely 2D nor 3D. Rather, it is flexible and dependent on the behavioral experience and demand.
Co-aggregation and secondary nucleation in the life cycle of human prolactin/galanin functional amyloids

Synergistic-aggregation and cross-seeding by two different amyloid proteins/peptides are well evident in various neurological disorders. However, this phenomenon is not well studied in functional amyloid aggregation. Here, we show Prolactin (PRL) is associated with lactation in mammals and neuropeptide galanin (GAL), which are co-stored in the lactotrophs facilitates the synergic aggregation in the absence of secretory granules helper molecules glycosaminoglycans (GAGS). Interestingly, although each partner possesses homotypic seeding ability, a unidirectional cross-seeding of GAL aggregation can be mediated by PRL seeds. The specificity of co-aggregation by PRL and GAL along with unidirectional cross-seeding suggests tight regulation of functional amyloid formation during co-storage of these hormones in secretory granule biogenesis of female rat lactotrophs. Further mixed fibrils release the constituent functional hormone much faster than the corresponding individual amyloid formed in presence of GAGs, suggesting that co-aggregation of functionally distant hormones might have evolved for efficient storage, synergistic and rapid release of both hormones upon stimulation. The co-aggregation and cross seeding by two different hormones of completely different structures and sequences (PRL and GAL) suggest a novel mechanism of heterologous amyloid formation both in disease and functional amyloids.
Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data

Phosphoproteomic experiments routinely observe thousands of phosphorylation sites. To understand the intracellular signaling processes that generated this data, one or more causal protein kinases must be assigned to each phosphosite. However, limited knowledge of kinase specificity typically restricts assignments to a small subset of a kinome. Starting from a statistical model of a high-throughput, in vitro kinase-substrate assay, I have developed an approach to high-coverage, multi-label kinase-substrate assignment called IV-KAPhE (“In vivo-Kinase Assignment for Phosphorylation Evidence”). Tested on human data, IV-KAPhE outperforms other methods of similar scope. Such computational methods generally predict a densely connected kinase-substrate network, with most sites targeted by multiple kinases, pointing either to unaccounted-for biochemical constraints or significant cross-talk and signaling redundancy. I show that such predictions can potentially identify biased kinase-site misannotations within families of closely related kinase isoforms and they provide a robust basis for kinase activity analysis.
A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.
Genome-wide association analysis of cotton salt stress response-related sites

Soil salinization is the main abiotic stress factor affecting agricultural production worldwide, and salt stress has a significant impact on plant growth and development. Cotton is one of the most salt-tolerant crops. Its salt tolerance varies greatly depending on the variety, growth stage, organs, and soil salt types. Therefore, the selection and utilization of excellent salt-tolerant germplasm resources and the excavation of excellent salt-tolerant salt and salt resistance genes play important roles in improving cotton production in saline-alkali soils. In this study, we analysed the population structure and genetic diversity of 144 elite Gossypium hirsutum cultivar accessions collected from around the world, and especially from China. Illumina Cotton SNP 70K was used to obtain genome-wide single-nucleotide polymorphism (SNP) data for 149 experimental materials, and 18,432 highly consistent SNP loci were obtained by filtering. PCA (principal component analysis) indicated that 149 upland cotton materials could be divided into 2 subgroups, including subgroup 1 with 78 materials and subgroup 2 with 71 materials. Using the obtained SNP and other marker genotype test results, under salt stress, the salt tolerance traits 3d_Germination_potential, 3d_Bud_length_drop_rate, 7d_Germination_rate, 7d_Bud_length_drop_rate, 7d_Germination_weight, 3d_Bud_length, 7d_Bud_length, relative_germination_potential, Relative_germination_rate, 7d_Bud_weight_drop_rate, Salt tolerance index 3d_Germination_potential_index, 3d_Bud_length_index, 7d_Bud_length_index, 7d_Bud_weight_index, and 7d_Germination_rate_index were evaluated by genome association analysis. A total of 27 SNP markers closely related to salt tolerance traits and 15 SNP markers closely related to salt tolerance index were detected. At the SNP locus associated with the traits of the bud length decline rate at 7 days, alleles Gh_A01G0034 and Gh_D01G0028 related to plant salt tolerance were detected, and they are related to intracellular transport, membrane microtubule formation and actin network. This study provides a theoretical basis for the selection and breeding of salt-tolerant upland cotton varieties.
Only females show a stable association between neuroticism and microstructural asymmetry of the cingulum across childhood and adolescence: A longitudinal DTI study

Neuroticism is characterized by a tendency to experience negative and anxious emotions. This personality trait is linked to an increased risk of anxiety and mood disorders. In a cross-sectional 3T diffusion tensor imaging (DTI) study in children and adolescents, we found an association between neuroticism and a relative imbalance between left and right (i.e., asymmetry) fractional anisotropy (FA) in the cingulum and white matter underlying the ventromedial prefrontal cortex with opposite directions in females and males. Here we analyzed the longitudinal follow-up DTI data, which was acquired in 76 typically-developing 7- to 18-year-olds, including up to 11 scans per subject. Neuroticism was assessed up to four times. Our longitudinal DTI measurements substantiate robust associations between higher neuroticism scores and increased left relative to right cingulum FA in females and decreased left relative to right cingulum FA in males. In females, the association was already present in late childhood and with a stable expression across childhood and adolescence. In males, the association gradually emerged during adolescence. Future longitudinal studies should clarify which neurobiological factors (e.g., genetic variation, prenatal stress, sex hormones) contribute to the sex-specific associations in the relationship between neuroticism and interhemispheric microstructural asymmetry of the cingulum. Highlight We analyzed a unique longitudinal DTI dataset covering late childhood and adolescence. In the cingulum, left-right fractional anisotropy (FA) asymmetry scaled with neuroticism. Females displayed a stable association of neuroticism with increased cingulum asymmetry. Males showed an association between neuroticism and decreased cingulum FA asymmetry. The association in males became more accentuated during adolescence
Weakly activated core inflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer’s disease

Neuro-inflammation signaling has been identified as an important hallmark of Alzheimer’s disease (AD) in addition to amyloid β plaques (Aβ) and neurofibrillary tangles (NFTs). However, our knowledge of neuro-inflammation is very limited; and the core signaling pathways associated with neuro-inflammation are missing. From a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), in this study, we uncovered the core neuro-inflammation signaling pathways in AD. Our novel hypothesis is that weakly activated neuro-inflammation signaling pathways can cause neuro-degeneration in a chronic process; whereas, strongly activated neuro-inflammation often cause acute disease progression like in COVID-19. Using the two large-scale genomics datasets, i.e., Mayo Clinic (77 control and 81 AD samples) and RosMap (97 control and 260 AD samples), our analysis identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of genes in the virus infection, immune response and x-core signaling pathways, are associated with inflammation molecular functions. Specifically, the x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo and TNF, which indicated the core neuro-inflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia, and leading to the chronic neuro-degeneration. The core neuro-inflammation signaling pathways can be used as novel therapeutic targets for effective AD treatment and prevention.
Modelling the Collective Mechanical Regulation of the Structure and Morphology of Epithelial Cell Layers

The morphology and function of epithelial sheets play an important role in healthy tissue development and cancer progression. The maintenance of structure of closely packed epithelial layers requires the coordination of various mechanical forces within the cells and others resulting from interactions with other cells and other tissues or substrates. However, a general model for the combination of mechanical properties which determine the cell shape and the overall structure of epithelial layers remains elusive. Here, we propose a computational model, based on the Cellular Potts Model, to study the interplay between mechanical properties of cells and dynamical transitions in epithelial structures and cell shapes. We map out phase diagrams as functions of cellular properties and the orientation of cell division. Monolayers of squamous, cuboidal, and columnar cells are found when the axis of cell proliferation is perpendicular to the substrate. Monolayer-to-multilayer transition is promoted via cell extrusion, depending on the mechanical properties of cells and the orientation of cell division. The results and model predictions are discussed in the context of experimental observations.
Cell size matters: nano- and micro-plastics preferentially drive declines of large marine phytoplankton due to co-aggregation

Marine plastic pollution represents a key environmental concern. Whilst ecotoxicological data for plastic is increasingly available, its impact upon marine phytoplankton remains unclear. Owing to their predicted abundance in the marine environment and likely interactions with phytoplankton, here we focus on the smaller fraction of plastic particles (∼50 nm and ∼2 µm polystyrene spheres). Exposure of natural phytoplankton communities and laboratory cultures revealed that plastic exposure does not follow traditional trends in ecotoxicological research, since large phytoplankton appear particularly susceptible towards plastics exposure despite their higher surface-to-volume ratios. Cell declines appear driven by hetero-aggregation and co-sedimentation of cells with plastic particles, recorded visually and demonstrated using confocal microscopy. As a consequence, plastic exposure also caused disruption to photosynthetic functioning, as determined by both photosynthetic efficiency and high throughput proteomics. Negative effects upon phytoplankton are recorded at concentrations orders of magnitude above those estimated in the environment. Hence, it is likely that impacts of NPs and MPs are exacerbated at the high concentrations typically used in ecotoxicological research (i.e., mg L-1).