Miracle-dz/UniFAR-FTUs huggingface dataset:
Hashing Beam Training for Near-Field Integrated Sensing and Communication Systems

In millimeter-wave (mmWave) integrated sensing and communication networks, users may be within the coverage of multiple access points (AP), which typically employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. Furthermore, as the antenna aperture increases, users are more likely to be situated in the near-field region of the AP antenna array. This motivates our investigation into the near-field beam training problem to achieve effective positioning services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first construct a near-field single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed near-field HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead. Furthermore, we verify its applicability under the far-field scenario as well.
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data

Deep Generative Models (DGMs) have been shown to be powerful tools for generating tabular data, as they have been increasingly able to capture the complex distributions that characterize them. However, to generate realistic synthetic data, it is often not enough to have a good approximation of their distribution, as it also requires compliance with constraints that encode essential background knowledge on the problem at hand. In this paper, we address this limitation and show how DGMs for tabular data can be transformed into Constrained Deep Generative Models (C-DGMs), whose generated samples are guaranteed to be compliant with the given constraints. This is achieved by automatically parsing the constraints and transforming them into a Constraint Layer (CL) seamlessly integrated with the DGM. Our extensive experimental analysis with various DGMs and tasks reveals that standard DGMs often violate constraints, some exceeding $95\%$ non-compliance, while their corresponding C-DGMs are never non-compliant. Then, we quantitatively demonstrate that, at training time, C-DGMs are able to exploit the background knowledge expressed by the constraints to outperform their standard counterparts with up to $6.5\%$ improvement in utility and detection. Further, we show how our CL does not necessarily need to be integrated at training time, as it can be also used as a guardrail at inference time, still producing some improvements in the overall performance of the models. Finally, we show that our CL does not hinder the sample generation time of the models.
Confounder importance learning for treatment effect inference

We address applied and computational issues for the problem of multiple treatment effect inference under many potential confounders. While there is abundant literature on the harmful effects of omitting relevant controls (under-selection), we show that over-selection can be comparably problematic, introducing substantial variance and a bias related to the non-random over-inclusion controls. We provide a novel empirical Bayes framework to mitigate both under-selection problems in standard high-dimensional methods and over-selection issues in recent proposals, by learning whether each control’s inclusion should be encouraged or discouraged. We develop efficient gradient-based and Expectation-Propagation model-fitting algorithms to render the approach practical for a wide class of models. A motivating problem is to ∗DR gratefully acknowledges support from Spanish Government grants Europa Excelencia EUR2020112096, RYC-2015-18544, PGC2018-101643-B-I00. 1 ar X iv :2 11 0. 00 31 4v 1 [ st at . M E ] 1 O ct 2 02 1 estimate the salary gap evolution in recent years in relation to potentially discriminatory characteristics such as gender, race, ethnicity and place of birth. We found that, albeit smaller, some wage differences remained for female and black individuals. A similar trend is observed when analyzing the joint contribution of these factors to deviations from the average salary. Our methodology also showed remarkable estimation robustness to the addition of spurious artificial controls, relative to existing proposals.
Cross-talk between tissues is critical for intergenerational acclimation to environmental change

An organism’s reaction to environmental changes is mediated by coordinated responses of multiple tissues. Additionally, parental priming may increase offsprings’ acclimation potential to changing environmental conditions. As the effects of human-induced climate change, such as ocean acidification (OA), continue to intensify it is critical to assess the acclimation potential of species at the whole organismal scale. For this we need to understand the cross-talk between tissues in regulating and responding to pH changes. Here by using a multi-tissue approach we determine the influence of 1) variation in parental behavioural tolerance and 2) parental environment, on molecular responses of their offspring in a coral reef fish. The gills and liver showed the highest transcriptional response to OA conditions in juvenile fish regardless of the parental environment, while the brain and liver showed the greatest signal of intergenerational acclimation. Key functional pathways that were altered in the brain upon within-generational CO2 exposure were restored to control levels when parents were exposed to OA conditions. Furthermore, the expression of a new complement of genes involved in key functions was altered in the offspring only when parents were previously exposed to OA conditions. Therefore, previous parental conditioning to OA can reprogram tissue transcriptomic profiles of the offspring enabling them to better cope in an environment with elevated CO2 levels. Overall, our results reveal tissue-specific transcriptional changes underlying intergenerational plastic responses to elevated CO2 exposure and highlight the integration of these changes in promoting organismal acclimation to OA.
Neural basis of perceptual surface qualities: Evidence from EEG decoding

The human visual system can easily recognize object material categories and estimate surface properties such as glossiness and smoothness. A number of psychophysical and computational studies suggest that the material perception depends on global feature statistics of the entire image at multiple processing levels. Neural representations of such global features, which is independent of precise retinotopy, may be captured even by EEG that have low spatial resolution. To test this possibility, here we measured visual evoked potentials (VEPs) for 191 natural images consisting of 20 categories of materials. We then sought to classify material categories and surface properties from the VEPs, and to reconstruct the rich phenomenal appearance of materials themselves via neural representations of global features as estimated from the VEPs. As a result, we found that material categories were correctly classified by the VEPs even at latencies of ∼150 ms. The apparent surface properties were also significantly classified at ∼175 ms (lightness, chromaticity, and smoothness) and >200 ms (glossiness, hardness, and heaviness). In a subsequent reverse-correlation analysis, we further found that the VEPs at these latencies are highly correlated with low- and high-level global features statistics of the surface images; Portilla-Simoncelli texture statistics and style information in deep convolutional neural network (dCNN), indicating that neural activities about such global features are reflected in the VEPs that enabled successful classification of materials. To demonstrate this idea more directly, we trained deep generative models (MVAE models) that reconstruct the surface image itself from the VEPs via style information (gram matrix of the dCNN output). The model successfully reconstructed realistic surface images, a part of which were nearly indistinguishable from the original images. These findings suggest that the neural representation of statistical image features, which were formed at short latencies in the visual cortex and reflected even in EEG signals, not simply enable human visual system to recognize material categories and evaluate surface properties but provides the essential basis for rich and complex phenomenal qualities of natural surfaces.
Neural substrates underlying the expectation of rewards resulting from effortful exertion

Expectations as a reference point shape our decisions to motivate effortful activity. Despite the important role of reference points in human performance, little is known about how the brain processes expectations to guide motivated exertion. Participants completed a reward-based effort task in an MRI scanner. During each trial, participants were presented with a risky option that would either result in a fixed monetary payment, regardless of their effort exertion, or a piece-rate payment where the payment was proportional to the amount of effort exerted. We found that participants exerted more effort as the fixed payment increased, suggesting that the fixed payment influenced the reference point for effort exertion. The strength of this reference-dependent behavior was correlated with neural activity in the ventral striatum, which was significantly modulated by the deviation from the payoff to reward expectations after effort exertion. Our results suggest that value-related brain areas, particularly the ventral striatum, encode expectations of reward as a reference point to motivate effort exertion. One-Sentence Summary Value-related brain areas process reward expectations and how we evaluate outcomes based on that.
Does method of field preparation affect survival and growth of juvenile Chinook salmon in agricultural floodplains?

There is growing interest to integrate conservation initiatives into agricultural practices using a reconciliation ecology framework. In California’s Central Valley, one approach to improving crucial nursery habitat for threatened and endangered fish species is by re-creating floodplain habitats through the inundation of agricultural fields during the non-growing season. We conducted a series of field experiments in agricultural floodplains (winter-flooded rice fields that were once natural floodplains) to explore whether different field preparation methods enhanced growth and survival of floodplain-dependent fish species. Approximately 8,000 juvenile fall-run Chinook Salmon (Oncorhynchus tshawytscha) were reared for at least 28 days on eight, 0.2 ha experimental fields. Each experimental field represented one of four treatments (two replicates per treatment): addition of large wood, addition of in-field canals, addition of both large wood and in-field canals, and control-where no additional field preparations were implemented beyond standard post-growing practices: i.e., chopping rice into stubble, baling and removing excess rice straw, and discing the field with a single pass by plowing rice straw into the ground to promote decomposition. We found no significant difference in water temperature, fish growth or fish survival among habitat treatments. Across treatments, survival ranged from 50.1%-78.3% and averaged 65.75% (⁺/- 7.89% SE). These findings suggest agricultural floodplains require no additional modifications to promote fish growth and survival. Results illustrate the benefits of integrating working landscapes with conservation initiatives by creating more accessible and beneficial habitat for native fishes.
Ready, set, yellow! Color Preference of Indian Free-ranging Dogs

Most of the research on color vision related behaviors in dogs has involved training the dogs to perform visual discrimination tasks. We investigated the meaning of color to untrained Indian free-ranging dogs (FRDs). Using one-time multi-option choice tests for color preference in 134 adult dogs, we found the dogs to prefer yellow objects over blue or gray ones (p < 0.001, Cramer’s V = 0.433) while there was no preference between blue and gray (N = 102, p = 0.165). We next pitted a yellow object against a gray object that had food. Here, the dogs ignored the food to approach the yellow object first, both when the food was biscuit (N = 52, p < 0.001, Cramer’s V = 0.576) and chicken (N = 61, p < 0.001, Cramer’s V = 0.540), indicating the color preference to be quite strong. Color preference has previously been investigated in many other animals and has implications for behaviors like mate choice and foraging. Our study provides a new perspective into the ecology of Indian FRDs and might have implications for pet dogs as well, if they too show this preference. Highlights Indian free-ranging dogs (FRDs) show preference toward the color yellow over blue and gray. Indian FRDs show no preference between blue and gray colors. Attraction towards a yellow object can be stronger than attraction towards food rewards for Indian FRDs.
Autorepression of Yeast Hsp70 co-chaperones by intramolecular interactions involving their J-domains

The Hsp70 chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) co-evolved with Hsp70s to trigger ATP-hydrolysis and catalytically upload various substrate polypeptides in need to be structurally modified by the chaperone. Here, we measured the protein disaggregation and refolding activities of the main yeast cytosolic Hsp70, Ssa1, in the presence of its most abundant JDPs, Sis1 and Ydj1, and two swap mutants, in which the J-domains have been interchanged. The observed differences by which the four constructs differently cooperate with Ssa1 and cooperate with each other, as well as their observed intrinsic ability to bind misfolded substrates and trigger Ssa1’s ATPase, indicates the presence of yet uncharacterized intra-molecular dynamic interactions between the J-domains and their remaining C-terminal domains. Taken together, the data suggest an auto-regulatory role to these intra-molecular interactions within both type A and B JDPs, which might have evolved to reduce energy-costly ATPase cycles by the Ssa1-4 chaperones that are the most abundant Hsp70s in the yeast cytosol. Graphical abstract: Lower panel: autoinhibited DnaJA or DnaJB dimers, drawn here as Swiss army knives with sequestered J-domains as folded blades, can bind misfolded polypeptides (violet). Upper panel: DnaJA or DnaJB become active when their J-domains are exposed and can bind ATP-Hsp70s, and transfer the misfolded polypeptides, respectively, onto Hsp70’s nucleotide binding (Cyan) and protein binding domains (Orange and Green). Hsp70’s interdomain linker (DLLLLDV, Magenta).
Deriving the cone fundamentals: a subspace intersection method

Two ideas, proposed by Thomas Young and James Clerk Maxwell, form the foundations of color science: (1) Three types of retinal receptors encode light under daytime conditions, and (2) color matching experiments establish the critical spectral properties of this encoding. Experimental quantification of these ideas are used in international color standards. But, for many years the field did not reach consensus on the spectral properties of the biological substrate of color matching: the sensitivity of the in situ cones (cone fundamentals). By combining auxiliary data (thresholds, inert pigment analyses), complex calculations, and color matching from genetically analyzed dichromats, the human cone fundamentals have now been standardized. Here we describe a new computational method to estimate the cone fundamentals using only color matching from dichromatic observers. We show that it is not necessary to include data from trichromatic observers in the analysis or to know the primary lights used in the matching experiments. Remarkably, it is even possible to estimate the fundamentals by combining data from experiments using different, unknown primaries. We then suggest how the new method may be applied to color management in modern image systems.
In vivo affinity maturation of the HIV-1 Env-binding domain of CD4

Many human proteins have been repurposed as biologics for clinical use. These proteins have been engineered with in vitro techniques that improve affinity for their ligands. However, these approaches do not select against properties that impair efficacy such as protease sensitivity or self-reactivity. Here we engineer the B-cell receptor of primary murine B cells to express a human protein biologic without disrupting their ability to affinity mature. Specifically, CD4 domains 1 and 2 (D1D2) of a half-life enhanced-HIV-1 entry inhibitor CD4-Ig (CD4-Ig-v0) were introduced into the heavy-chain loci of murine B cells, which were then adoptively transferred to wild-type mice. After immunization, transferred B cells proliferated, class switched, affinity matured, and efficiently produced D1D2-presenting antibodies. Somatic hypermutations found in the D1D2-encoding region of engrafted B cells improved binding affinity of CD4-Ig-v0 for the HIV-1 envelope glycoprotein (Env) and the neutralization potency of CD4-Ig-v0 by more than ten-fold across a global panel of HIV-1 isolates, without impairing its pharmacokinetic properties. Thus, affinity maturation of non-antibody protein biologics in vivo can guide development of more effective therapeutics.
Characterizing and addressing error modes to improve sequencing accuracy

The accuracy of a sequencing platform has traditionally been measured by the %Q30, or percentage of data exceeding a basecall accuracy of 99.9%. Improvements to accuracy beyond Q30 may be beneficial for certain applications such as the identification of low frequency alleles or the improvement of reference genomes. Here we demonstrate how we achieved over 70% Q50 (99.999% accuracy) data on the AVITI™ sequencer. This level of accuracy required us to not only improve sequencing quality but also to mitigate library preparation errors and analysis artifacts.
Tunable Macroscopic Alignment of Self-Assembling Peptide Nanofibers

Fibrous proteins that comprise the extracellular matrix (ECM) guide cellular growth and tissue organization. A lack of synthetic strategies able to generate aligned, ECM-mimetic biomaterials has hampered bottom-up tissue engineering of anisotropic tissues and led to a limited understanding of cell-matrix interactions. Here, we present a facile extrusion-based fabrication method to produce anisotropic, nanofibrous hydrogels using self-assembling peptides. The application of shear force coinciding with ion-triggered gelation is used to kinetically trap supramolecular nanofibers into aligned, hierarchical structures. We establish how modest changes in phosphate buffer concentration during peptide self-assembly can be used to tune their alignment and packing. In addition, increases in the nanostructural anisotropy of fabricated hydrogels are found to enhance their strength and stiffness under hydrated conditions. To demonstrate their utility as an ECM-mimetic biomaterial, aligned nanofibrous hydrogels are used to guide directional spreading of multiple cell types, but strikingly, increased matrix alignment is not always correlated with increased cellular alignment. Nanoscale observations reveal differences in cell-matrix interactions between variably aligned scaffolds and implicate the need for mechanical coupling for cells to understand nanofibrous alignment cues. In total, innovations in the supramolecular engineering of self-assembling peptides allow us to generate a gradient of anisotropic nanofibrous hydrogels, which are used to better understand directed cell growth.
Dysregulation of Neuropilin-2 Expression in Inhibitory Neurons Impairs Hippocampal Circuit Development Leading to Autism-Epilepsy Phenotype

Dysregulation of development, migration, and function of interneurons, collectively termed interneuronopathies, have been proposed as a shared mechanism for autism spectrum disorders (ASDs) and childhood epilepsy. Neuropilin-2 (Nrp2), a candidate ASD gene, is a critical regulator of interneuron migration from the median ganglionic eminence (MGE) to the pallium, including the hippocampus. While clinical studies have identified Nrp2 polymorphisms in patients with ASD, whether dysregulation of Nrp2-dependent interneuron migration contributes to pathogenesis of ASD and epilepsy has not been tested. We tested the hypothesis that the lack of Nrp2 in MGE-derived interneuron precursors disrupts the excitation/inhibition balance in hippocampal circuits, thus predisposing the network to seizures and behavioral patterns associated with ASD. Embryonic deletion of Nrp2 during the developmental period for migration of MGE derived interneuron precursors (iCKO) significantly reduced parvalbumin, neuropeptide Y, and somatostatin positive neurons in the hippocampal CA1. Consequently, when compared to controls, the frequency of inhibitory synaptic currents in CA1 pyramidal cells was reduced while frequency of excitatory synaptic currents was increased in iCKO mice. Although passive and active membrane properties of CA1 pyramidal cells were unchanged, iCKO mice showed enhanced susceptibility to chemically evoked seizures. Moreover, iCKO mice exhibited selective behavioral deficits in both preference for social novelty and goal-directed learning, which are consistent with ASD-like phenotype. Together, our findings show that disruption of developmental Nrp2 regulation of interneuron circuit establishment, produces ASD-like behaviors and enhanced risk for epilepsy. These results support the developmental interneuronopathy hypothesis of ASD epilepsy comorbidity.
APCalign: an R package workflow and app for aligning and updating flora names to the Australian Plant Census

Here we present “APCalign”, an R package and accompanying browser-sourced application to align and update scientific names for Australian vascular plants to the most likely currently accepted name using the Australian Plant Census (APC) or a name in the Australian Plant Names Index (APNI). Scientific names are the label assigned to unique taxon concepts by the scientific community, but this common terminology is most useful if a taxon concept is consistently referred to by the same name. These links can be broken due to either spelling mistakes or taxonomic changes. Automated tools are required to resolve taxon lists, aligning and updating long lists of possibly erroneous scientific names to the most likely currently accepted names. It is essential that tools specific to the APC/APNI be developed, as these lists specify an endorsed national-level nomenclature used in government legislation and include the uniquely Australian concept of phrase names, absent in global taxonomic datasets. To align input names to names within the APC or APNI, “APCalign” works progressively through a sequence of checks that combine different permutations of the input name, exact versus fuzzy matches, matches that consider the entire name input versus a subset of words, and character strings that indicate a name can only be resolved to a genus or family. The aligned names are then, when possible, updated to a currently accepted taxon concept within the APC. This package should facilitate all research outputs that require diverse scientific name lists to be merged or outdated lists to be updated.