Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes

The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process.
OsPSTOL but not TaPSTOL can play a role in nutrient use efficiency and works through conserved pathways in both wheat and rice

There is a large demand to reduce inputs for current crop production, particularly phosphate and nitrogen inputs which are the two most frequently added supplements to agricultural production. Gene characterization is often limited to the native species from which it was identified, but may offer benefits to other species. To understand if OsPSTOL, a gene identified from rice which improves tolerance to low P growth conditions, might improve performance and provide the same benefit in wheat, OsPSTOL was transformed into wheat and expressed from a constitutive promoter. The ability of OsPSTOL to improve nutrient acquisition under low phosphate or low nitrogen was evaluated. Here we show that OsPSTOL works through a conserved pathway in wheat and rice to improve yields under both low phosphate and low nitrogen. This increase is yield is mainly driven by improved uptake from the soil driving increased biomass and ultimately increased seed number, but does not change the concentration of N in the straw or grain. Overexpression of OsPSTOL in wheat modifies N regulated genes to aid in this uptake whereas the putative homolog TaPSTOL does not suggesting that expression of OsPSTOL in wheat can help to improve yields under low input agriculture.
MTM: a multi-task learning framework to predict individualized tissue gene expression profiles

Predicting tissue expression profiles from peripheral ‘surrogate’ samples, especially blood transcriptome, has become an effective alternative when invasive procedures are not ideal. However, existing approaches ignore tissue-shared intrinsic relevance, inevitably limiting predictive performance. Here, we propose a unified deep learning-based multi-task learning framework, Multi-tissue Transcriptome Mapping (MTM), enabling the prediction of individualized expression profiles from any available tissue of an individual. By jointly leveraging individualized cross-tissue information through multi-task learning, MTM achieves superior sample-level and gene-level performance. With the high prediction accuracy and the ability to preserve individualized biological variations, MTM could facilitate both fundamental and clinical biomedical research.
Effect of Selective Lesions of Nucleus Accumbens µ-Opioid Receptor-Expressing Cells on Heroin Self-Administration in Male and Female Rats: A Study with Novel Oprm1-Cre Knock-in Rats

The brain µ-opioid receptor (MOR) is critical for the analgesic, rewarding, and addictive effects of opioid drugs. However, in rat models of opioid-related behaviors, the circuit mechanisms of MOR-expressing cells are less known because of a lack of genetic tools to selectively manipulate them. We introduce a CRISPR-based Oprm1-Cre knock-in transgenic rat that provides cell type-specific genetic access to MOR-expressing cells. After performing anatomic and behavioral validation experiments, we used the Oprm1-Cre knock-in rats to study the involvement of NAc MOR-expressing cells in heroin self-administration in male and female rats. Using RNAscope, autoradiography, and FISH chain reaction (HCR-FISH), we found no differences in Oprm1 expression in NAc, dorsal striatum, and dorsal hippocampus, or MOR receptor density (except dorsal striatum) or function between Oprm1-Cre knock-in rats and wildtype littermates. HCR-FISH assay showed that iCre is highly coexpressed with Oprm1 (95%-98%). There were no genotype differences in pain responses, morphine analgesia and tolerance, heroin self-administration, and relapse-related behaviors. We used the Cre-dependent vector AAV1-EF1a-Flex-taCasp3-TEVP to lesion NAc MOR-expressing cells. We found that the lesions decreased acquisition of heroin self-administration in male Oprm1-Cre rats and had a stronger inhibitory effect on the effort to self-administer heroin in female Oprm1-Cre rats. The validation of an Oprm1-Cre knock-in rat enables new strategies for understanding the role of MOR-expressing cells in rat models of opioid addiction, pain-related behaviors, and other opioid-mediated functions. Our initial mechanistic study indicates that lesioning NAc MOR-expressing cells had different effects on heroin self-administration in male and female rats. SIGNIFICANCE STATEMENT The brain µ-opioid receptor (MOR) is critical for the analgesic, rewarding, and addictive effects of opioid drugs. However, in rat models of opioid-related behaviors, the circuit mechanisms of MOR-expressing cells are less known because of a lack of genetic tools to selectively manipulate them. We introduce a CRISPR-based Oprm1-Cre knock-in transgenic rat that provides cell type-specific genetic access to brain MOR-expressing cells. After performing anatomical and behavioral validation experiments, we used the Oprm1-Cre knock-in rats to show that lesioning NAc MOR-expressing cells had different effects on heroin self-administration in males and females. The new Oprm1-Cre rats can be used to study the role of brain MOR-expressing cells in animal models of opioid addiction, pain-related behaviors, and other opioid-mediated functions.
Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain–computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main results. We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.
IPSILESIONAL HIPPOCAMPAL GABA IS ELEVATED AND CORRELATES WITH COGNITIVE IMPAIRMENT AND MALADAPTIVE NEUROGENESIS AFTER CORTICAL STROKE IN MICE

Background Cognitive dysfunction is a frequent stroke sequela but its pathogenesis and treatment remain unresolved. Involvement of aberrant hippocampal neurogenesis and maladaptive circuitry remodelling has been proposed but their mechanisms are unknown. Our aim was to evaluate potential underlying molecular/cellular events implicated. Methods Stroke was induced by permanent occlusion of the middle cerebral artery (MCAO) in 2-month-old C57BL/6 male mice. Hippocampal metabolites/neurotransmitters were analysed longitudinally by in vivo magnetic resonance spectroscopy (MRS). Cognitive function was evaluated with the contextual fear conditioning test. Microglia, astrocytes, neuroblasts and interneurons were analysed by immunofluorescence. Results Approximately 50% of mice exhibited progressive post-MCAO cognitive impairment. Notably, immature hippocampal neurons in the impaired group displayed more severe aberrant phenotypes than those from the non-impaired group. Using MRS, significant bilateral changes in hippocampal metabolites such as myo-Inositol (mIns) or N-acetylaspartic acid (NAA) were found that correlated, respectively, with numbers of glia and immature neuroblasts in the ischemic group. Importantly, some metabolites were specifically altered in the ipsilateral hippocampus suggesting its involvement in aberrant neurogenesis and remodelling processes. Specifically, MCAO animals with higher hippocampal GABA levels displayed worse cognitive outcome. Implication of GABA in this setting was supported by the amelioration of ischemia-induced memory deficits and aberrant hippocampal neurogenesis after blocking pharmacologically GABAergic neurotransmission. These data suggest that GABA exerts its detrimental effect, at least partly, by affecting morphology and integration of newborn neurons into the hippocampal circuits. Conclusions Hippocampal GABAergic neurotransmission could be considered a novel diagnostic and therapeutic target for post-stroke cognitive impairment.
A Construction of Subfactors of Planar Algebra

We present more planar algebraic construction of subfactors than those of Guionet-Jones-Shlyakhtenko-Walker and Kodiyalam-Sunder which start from a subfactor planar algebra and give in a direct way a subfactor of the same standard invariant with the planar algebra. Our construction is based on using the ordinary concepts in planar algebras such as involution, inclusion and conditional expectation mappings as it is
QCR7 affects the virulence of Candida albicans and the uptake of multiple carbon sources present in different host niches

Background Candida albicans is a commensal yeast that may cause life-threatening infections. Studies have shown that the cytochrome b-c1 complex subunit 7 gene (QCR7) of C. albicans encodes a protein that forms a component of the mitochondrial electron transport chain complex III, making it an important target for studying the virulence of this yeast. However, to the best of our knowledge, the functions of QCR7 have not yet been characterized. Methods A QCR7 knockout strain was constructed using SN152, and BALb/c mice were used as model animals to determine the role of QCR7 in the virulence of C. albicans. Subsequently, the effects of QCR7 on mitochondrial functions and use of carbon sources were investigated. Next, its mutant biofilm formation and hyphal growth maintenance were compared with those of the wild type. Furthermore, the transcriptome of the qcr7Δ/Δ mutant was compared with that of the WT strain to explore pathogenic mechanisms. Results Defective QCR7 attenuated the virulence of C. albicans infection in vivo. Furthermore, the mutant influenced the use of multiple alternative carbon sources that exist in several host niches. Moreover, it led to mitochondrial dysfunction. Furthermore, the QCR7 knockout strain showed defects in biofilm formation or the maintenance of filamentous growth. The overexpression of cell-surface-associated genes (HWP1, YWP1, XOG1, and SAP6) can restore defective virulence phenotypes and the carbon-source utilization of qcr7Δ/Δ. Conclusion This study provides new insights into the mitochondria-based metabolism of C. albicans, accounting for its virulence and the use of variable carbon sources that promote C. albicans to colonize host niches.
Integrating Network Pharmacology And Experimental Verification To Explore The Mechanism Of Qionggui Power Against Atherosclerosis

Qionggui Power (QP), a classic prescription in Traditional Chinese Medicine (TCM), has shown potential in the treatment of atherosclerosis during the past decades. However, the mechanism that mediates these cardiovascular benefits remains to be fully elucidated. Here, we investigated the effects and mechanisms of QP against atherosclerosis with network pharmacology approaches and in vitro model. The active ingredients and related targets of QP were collected from public databases. The hub targets and signaling pathways of QP against AS were defined by extensive application of bioinformatics approaches, including the protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). The predicted major targets were validated in LPS-stimulated murine macrophages RAW264.7. The anti-inflammatory properties of QP were also evaluated in this model. In silico investigation of QP resulted in the identification of 18 active ingredients and 49 chemical targets intersecting with AS-related genes. And KEGG pathway analysis revealed a high enrichment in the Lipid and Atherosclerosis pathway of these chemical targets. Biochemical analysis showed marked effects of QP on the expression of predicted chemical targets (PPARr, CAT, PTGS2) and LPS-induced inflammatory genes (IL1, IL6, and TNFα). And these inhibitory effects were linked to the suppression of the NF-κB signaling pathway, which was activated by the LPS stimulus. Our findings revealed the therapeutic potential of QP in the prevention and treatment of atherosclerosis. Graphical Abstract
Assistive Teaching of Motor Control Tasks to Humans

Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teaching
Streaming Weighted Sampling over Join Queries

Join queries are a fundamental database tool, capturing a range of tasks that involve linking heterogeneous data sources. However, with massive table sizes, it is often impractical to keep these in memory, and we can only take one or few streaming passes over them. Moreover, building out the full join result (e.g., linking heterogeneous data sources along quasi-identifiers) can lead to a combinatorial explosion of results due to many-to-many links. Random sampling is a natural tool to boil this oversized result down to a representative subset with well-understood statistical properties, but turns out to be a challenging task due to the combinatorial nature of the sampling domain. Existing techniques in the literature focus solely on the setting with tabular data residing in main memory, and do not address aspects such as stream operation, weighted sampling and more general join operators that are urgently needed in a modern data processing context. The main contribution of this work is to meet these needs with more lightweight practical approaches. First, a bijection between the sampling problem and a graph problem is introduced to support weighted sampling and common join operators. Second, the sampling techniques are refined to minimise the number of streaming passes. Third, techniques are presented to deal with very large tables under limited memory. Finally, the proposed techniques are compared to existing approaches that rely on database indices and the results indicate substantial memory savings, reduced runtimes for ad-hoc queries and competitive amortised runtimes. All pertinent code and data can be found at:
Structures for pairs of mock modular forms with the Zagier duality

Zagier introduced special bases for weakly holomorphic modular forms to give the new proof of Borcherds' theorem on the infinite product expansions of integer weight modular forms on $\SL_2(\ZZ)$ with a Heegner divisor. These good bases appear in pairs, and they satisfy a striking duality, which is now called the Zagier duality. After the result of Zagier, this type duality was studied broadly in various view points including the theory of a mock modular form. In this paper, we consider this problem with the Eichler cohomology theory, especially the supplementary function theory developed by Knopp. Using holomorphic Poincar\'e series and their supplementary functions, we construct a pair of families of vector-valued harmonic weak Maass forms satisfying the Zagier duality with integer weights $-k$ and $k+2$ respectively, $k>0$, for a $H$-group. We also investigate the structures of them such as the images under the differential operators $D^{k+1}$ and $\xi_{-k}$ and quadric relations of the critical values of their $L$-functions.
The Davies method revisited for heat kernel upper bounds of regular Dirichlet forms on metric measure spaces

We apply the Davies method to prove that for any regular Dirichlet form on a metric measure space, an off-diagonal stable-like upper bound of the heat kernel is equivalent to the conjunction of the on-diagonal upper bound, a cutoff inequality on any two concentric balls, and the jump kernel upper bound, for any walk dimension. If in addition the jump kernel vanishes, that is, if the Dirichlet form is strongly local, we obtain a sub-Gaussian upper bound. This gives a unified approach to obtaining heat kernel upper bounds for both the non-local and the local Dirichlet forms.
A global analysis proof of the stability of Minkowski space and the polyhomogeneity of the metric

We first give a new proof of the non-linear stability of the $(3+1)$-dimensional Minkowski spacetime as a solution of the Einstein vacuum equation. We then show that the metric admits a full asymptotic expansion at infinity, more precisely at the boundary hypersurfaces (corresponding to spacelike, null, and timelike infinity) of a suitable compactification of $\mathbb{R}^4$ adapted to the bending of outgoing light cones. We work in a wave map/DeTurck gauge closely related to the standard wave coordinate gauge. Similarly to our previous work on the stability of cosmological black holes, we construct the solution of the non-linear equation using an iteration scheme in which we solve a linearized equation globally at each step; to fix the geometry of null infinity throughout the iteration scheme, we devise a hyperbolic formulation of Einstein's equation which ensures constraint damping. The weak null condition of Lindblad and Rodnianski arises naturally as a nilpotent coupling of certain metric components in a linear model operator at null infinity. Furthermore, we relate the Bondi mass to a logarithmic term in the expansion of the metric at null infinity and prove the Bondi mass loss formula.
Uniformizing the Moduli Stacks of Global G-Shtukas

This is the 2nd in a sequence of articles, in which we explore moduli stacks of global $\mathfrak{G}$-shtukas, the function field analogs for Shimura varieties. Here $\mathfrak{G}$ is a flat affine group scheme of finite type over a smooth projective curve $C$ over a finite field. Global $\mathfrak{G}$-shtukas are generalizations of Drinfeld shtukas and analogs of abelian varieties with additional structure. We prove that the moduli stacks of global $\mathfrak{G}$-shtukas are algebraic Deligne–Mumford stacks separated and locally of finite type. They generalize various moduli spaces used by different authors to prove instances of the Langlands program over function fields. In the 1st article we explained the relation between global $\mathfrak{G}$-shtukas and local ${{\mathbb{P}}}$-shtukas, which are the function field analogs of $p$-divisible groups. Here ${{\mathbb{P}}}$ is the base change of $\mathfrak{G}$ to the complete local ring at a point of $C$. When ${{\mathbb{P}}}$ is smooth with connected reductive generic fiber we proved the existence of Rapoport–Zink spaces for local ${{\mathbb{P}}}$-shtukas. In the present article we use these spaces to (partly) uniformize the moduli stacks of global $\mathfrak{G}$-shtukas for smooth $\mathfrak{G}$ with connected fibers and reductive generic fiber. This is our main result. It has applications to the analog of the Langlands–Rapoport conjecture for our moduli stacks.
Global properties of the symmetrized $s$-divergence

In this paper we give a study of the symmetrized divergences $U_s(p,q)=K_s(p||q)+K_s(q||p)$ and $V_s(p,q)=K_s(p||q)K_s(q||p)$, where $K_s$ is the relative divergence of type $s, s\in\mathbb R$. Some basic properties as symmetry, monotonicity and log-convexity are established. An important result from the Convexity Theory is also proved.
Search Frictions and Product Design in the Municipal Bond Market

This paper shows that product design shapes search frictions and that intermediaries leverage this channel to increase their rents in the context of the U.S. municipal bond market. The majority of bonds are designed via negotiations between a local government and its underwriter. They are then traded in a decentralized market, where the underwriter often also acts as an intermediary. Exploiting variations in state regulations that limit government officials’ conflicts of interest, we provide evidence that bond design from the government’s perspective involves a trade-off between flexibility and liquidity, but the underwriter benefits from designing and trading complex bonds. Motivated by these findings, we build and estimate a model of bond origination and trades to quantify market inefficiency driven by underwriters’ role in intermediating trades and discuss policy implications.