Miracle-dz/UniFAR-FTUs huggingface dataset:
PII: S0925-4773(01)00570-6

The Notch signaling pathway plays an important role in many cell-fate decisions during development. Here we investigate the regulation and function of the conserved gene XNAP, which is a member of the Delta-Notch synexpression group in Xenopus. XNAP encodes a small protein with two C-terminal tandem ankyrin repeats which is expressed in the neurectoderm and in the presomitic mesoderm in a pattern that resembles that of other component of the Notch pathway. When a myc-tag form of XNAP is overexpressed in Xenopus or Hela cells, XNAP protein is detected both in the nucleus and the cytoplasm. In embryos and in animal cap assays, XNAP expression is activated, perhaps directly, by the Notch pathway and this activation appears to be Su(H) dependent. Overexpression of XNAP in embryos decreases Notch signaling, which leads to an increase in the number of primary neurons that form within the domains of the neural plate where neurogenesis normally occurs. In culture Hela cells, XNAP overexpression interferes with ICD activation of a Notch regulated reporter gene. Together, these data indicate that XNAP is a novel target of the Notch pathway that may, in a feedback loop, modulate its activity. q 2002 Elsevier Science Ireland Ltd. All rights reserved.
The Integrin avb6 Binds and Activates Latent TGFb1: A Mechanism for Regulating Pulmonary Inflammation and Fibrosis

Summary The mechanisms involved in activating latent TGFb are not fully understood, but recently there has been Transforming growth factor b (TGFb) family members important progress in this area. Plasmin can activate are secreted in inactive complexes with a latency- latent TGFb in cell-free systems (Lyons et al., 1990) and associated peptide (LAP), a protein derived from the in cell culture (Sato et al., 1990). However, plasminogen N-terminal region of the TGFb gene product. Extracel- knockout mice display none of the pathologic features lular activation of these complexes is a critical but of TGFb knockout mice, suggesting that plasmin is unincompletely understood step in regulation of TGFb likely to be the only molecule activating TGFb. Reactive function in vivo. We show that TGFb1 LAP is a ligand oxygen species can activate TGFb in vitro (Barcellosfor the integrin avb6 and that avb6-expressing cells Hoff and Dix, 1996), and radiation treatment appears induce spatially restricted activation of TGFb1. This able to activate TGFb in vivo via this mechanism (Barfinding explains why mice lacking this integrin develop cellos-Hoff et al., 1994). Thrombospondin (TSP) 1 can exaggerated inflammation and, as we show, are pro- activate TGFb by binding to a defined site on LAP and tected from pulmonary fibrosis. These data identify a inducing a conformational change in the latent complex; novel mechanism for locally regulating TGFb1 function TGFb is then bound to TSP1 in an active state (Schultzin vivo by regulating expression of the avb6 integrin. Cherry and Murphy-Ullrich, 1993; Schultz-Cherry et al.,
PII: S0042-6989(00)00207-8

Since individuals who have lost an eye in early life rely on monocular information, one asked if they would better estimate the time to collision (TTC) with an approaching object based on the monocular cue [(u/(du/dt), i.e. tau] than a control group using only monocular information. Estimates of TTC were measured with a simulated approaching textured object using a staircase procedure. Seven adult observers who were unilaterally enucleated at an early age were compared with 18 normally sighted control observers who viewed the stimuli monocularly. Consistent with previous findings, the majority of the controls (13/18) underestimated TTC. Three enucleated observers had larger estimation errors than the 95% confidence interval of the mean of the control group. One enucleated observer was unable to give reliable results. These results suggest that unilaterally enucleated observers cannot estimate TTC more accurately (and may even be worse) than normal controls when estimates are based on monocular information alone. Further, the majority (83%) of enucleated observers were influenced by perceived distance information derived from the object’s initial size when estimating TTC with an approaching object. The use of this other optical variable could account for their reduction in performance. It was suggested that in every day life enucleated individuals make use of as many optical variables as possible to partially compensate for the lack of binocularity. © 2000 Elsevier Science Ltd. All rights reserved.
PII: S0925-4773(01)00610-4

Response to the insect hormone ecdysone is mediated by a nuclear receptor complex containing Ultraspiracle (USP) and the Ecdysone Receptor (EcR). Among other phenotypes, loss of functional USP in Drosophila eye development results in an accelerated morphogenetic furrow, although loss of ecdysone arrests the furrow. We have shown that USP both represses and activates a gene affecting furrow movement, the ecdysone-responsive Z1 isoform of Broad-Complex, and we report additional usp mutant phenotypes. Using targeted replacement of USP to rescue usp mutant clones in the eye, we have mapped various USP functions and tested whether the USP nuclear receptor has an activating as well as a repressive effect on furrow movement. Furrow movement and related phenotypes are rescued by the presence of USP in a limited domain near the furrow while other phenotypes are rescued by USP expression posterior to the furrow. Our data indicate roles for USP activity at multiple developmental stages and help explain why loss of functional USP leads to furrow advancement while loss of ecdysone stops furrow movement. q 2002 Elsevier Science Ireland Ltd. All rights reserved.
PII: S0925-4773(02)00186-7

The interplay of fibroblast growth factor (FGF) and nodal signaling in the Xenopus gastrula marginal zone specifies distinct populations of presumptive mesodermal cells. Cells in the vegetal marginal zone, making up the presumptive leading edge mesoderm, are exposed to nodal signaling, as evidenced by SMAD2 activation, but do not appear to be exposed to FGF signaling, as evidenced by the lack of MAP kinase (MAPK) activation. However, in the animal marginal zone, activation of both SMAD2 and MAPK occurs. The differential activation of these two signaling pathways in the marginal zone results in the vegetal and animal marginal zones expressing different genes at gastrulation, and subsequently having different fates, with the vegetal marginal zone contributing to ventral mesoderm (e.g. ventral blood island) and the animal marginal zone giving rise to dorsal fates (e.g. notochord and somite). We report here the cloning of a cDNA encoding a novel nuclear protein, Xmenf, that is expressed in the vegetal marginal zone. The expression of Xmenf is induced by nodal signaling and negatively regulated by FGF signaling. Results from animal cap studies indicate that Xmenf plays a role in the pathway of ventral mesoderm induction in the vegetal marginal zone. q 2002 Elsevier Science Ireland Ltd. All rights reserved.
PII: S0042-6989(00)00267-4

This study investigated the cortical mechanisms of visual-spatial attention in a task where subjects discriminated patterned targets in one visual field at a time. Functional magnetic imaging (fMRI) was used to localize attention-related changes in neural activity within specific retinotopic visual areas, while recordings of event-related brain potentials (ERPs) traced the time course of these changes. The earliest ERP components enhanced by attention occurred in the time range 70–130 ms post-stimulus onset, and their neural generators were estimated to lie in the dorsal and ventral extrastriate visual cortex. The anatomical areas activated by attention corresponded closely to those showing increased neural activity during passive visual stimulation. Enhanced neural activity was also observed in the primary visual cortex (area V1) with fMRI, but ERP recordings indicated that the initial sensory response at 50–90 ms that was localized to V1 was not modulated by attention. Modeling of ERP sources over an extended time range showed that attended stimuli elicited a long-latency (160–260 ms) negativity that was attributed to the dipolar source in area V1. This finding is in line with hypotheses that V1 activity may be modulated by delayed, reentrant feedback from higher visual areas. © 2001 Elsevier Science Ltd. All rights reserved.
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks

The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial neural networks (ANNs) to spiking neural networks (SNNs) have so far prevented their application to large-scale, complex regression tasks. Furthermore, realizing a truly asynchronous and fully neuromorphic pipeline that maximally attains the abovementioned benefits involves rethinking the way in which this pipeline takes in and accumulates information. In the case of perception, spikes would be passed as-is and one-by-one between an event camera and an SNN, meaning all temporal integration of information must happen inside the network. In this article, we tackle these two problems. We focus on the complex task of learning to estimate optical flow from event-based camera inputs in a self-supervised manner, and modify the state-of-the-art ANN training pipeline to encode minimal temporal information in its inputs. Moreover, we reformulate the self-supervised loss function for event-based optical flow to improve its convexity. We perform experiments with various types of recurrent ANNs and SNNs using the proposed pipeline. Concerning SNNs, we investigate the effects of elements such as parameter initialization and optimization, surrogate gradient shape, and adaptive neuronal mechanisms. We find that initialization and surrogate gradient width play a crucial part in enabling learning with sparse inputs, while the inclusion of adaptivity and learnable neuronal parameters can improve performance. We show that the performance of the proposed ANNs and SNNs are on par with that of the current state-of-the-art ANNs trained in a self-supervised manner.
Homeostasis: Design and Implementation of a Self-Stabilizing Compiler

Mainstream compilers perform a multitude of analyses and optimizations on the given input program. Each analysis (such as points-to analysis) may generate a program-abstraction (such as points-to graph). Each optimization is typically composed of multiple alternating phases of inspection of such program-abstractions and transformations of the program. Upon transformation of a program, the program-abstractions generated by various analyses may become inconsistent with the modified program. Consequently, the correctness of the downstream inspection (and consequent transformation) phases cannot be ensured until the relevant program-abstractions are stabilized ; that is, the program-abstractions are either invalidated or made consistent with the modified program. In general, the existing compiler frameworks do not perform automated stabilization of the program-abstractions and instead leave it to the compiler pass writers to deal with the complex task of identifying the relevant program-abstractions to be stabilized, the points where the stabilization is to be performed, and the exact procedure of stabilization. In this paper, we address these challenges by providing the design and implementation of a novel compiler-design framework called Homeostasis . Homeostasis automatically captures all the program changes performed by each transformation phase, and later, triggers the required stabilization using the captured information, if needed. We also provide a formal description of Homeostasis and a correctness proof thereof. To assess the feasibility of using Homeostasis in compilers of parallel programs, we have implemented our proposed idea in IMOP, a compiler framework for OpenMP C programs. Further, to illustrate the benefits of using Homeostasis , we have implemented a set of standard data-flow passes, and a set of involved optimizations that are used to remove redundant barriers in OpenMP C programs. Implementations of none of these optimizations in IMOP required any additional lines of code for stabilization of the program-abstractions. We present an evaluation in the context of these optimizations and analyses, which demonstrates that Homeostasis is efficient and easy to use.
Direct (3+1)D laser writing of graded-index optical elements

We propose the single-step fabrication of (3+1)D graded-index (GRIN) optical elements by introducing the light exposure as the additional dimension. Following this method, we demonstrate two different optical devices: Volume holograms that are superimposed using angular and peristrophic multiplexing and optical waveguides with a well-defined refractiveindex profile. In the latter, we precisely control the propagating modes via tuning the 3D-printed waveguide parameters and report step-index and graded-index core-cladding transitions.
Stein’s method, smoothing and functional approximation

Stein's method for Gaussian process approximation can be used to bound the differences between the expectations of smooth functionals $h$ of a c\`adl\`ag random process $X$ of interest and the expectations of the same functionals of a well understood target random process $Z$ with continuous paths. Unfortunately, the class of smooth functionals for which this is easily possible is very restricted. Here, we prove an infinite dimensional Gaussian smoothing inequality, which enables the class of functionals to be greatly expanded -- examples are Lipschitz functionals with respect to the uniform metric, and indicators of arbitrary events -- in exchange for a loss of precision in the bounds. Our inequalities are expressed in terms of the smooth test function bound, an expectation of a functional of $X$ that is closely related to classical tightness criteria, a similar expectation for $Z$, and, for the indicator of a set $K$, the probability $\mathbb{P}(Z \in K^\theta \setminus K^{-\theta})$ that the target process is close to the boundary of $K$.
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.
Nonperturbative effects and resurgence in Jackiw-Teitelboim gravity at finite cutoff

We investigate the nonperturbative structure of Jackiw-Teitelboim gravity at finite cutoff, as given by its proposed formulation in terms of a $T\bar{T}$-deformed Schwarzian quantum mechanics. Our starting point is a careful computation of the disk partition function to all orders in the perturbative expansion in the cutoff parameter. We show that the perturbative series is asymptotic and that it admits a precise completion exploiting the analytical properties of its Borel transform, as prescribed by resurgence theory. The final result is then naturally interpreted in terms of the nonperturbative branch of the $T\bar{T}$-deformed spectrum. The finite-cutoff trumpet partition function is computed by applying the same strategy. In the second part of the paper, we propose an extension of this formalism to arbitrary topologies, using the basic gluing rules of the undeformed case. The Weil-Petersson integrations can be safely performed due to the nonperturbative corrections and give results that are compatible with the flow equation associated with the $T\bar{T}$ deformation. We derive exact expressions for general topologies and show that these are captured by a suitable deformation of the Eynard-Orantin topological recursion. Finally, we study the"slope"and"ramp"regimes of the spectral form factor as functions of the cutoff parameter.
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification

Deep equilibrium models are based on implicitly defined functional relations and have shown competitive performance compared with the traditional deep networks. Monotone operator equilibrium networks (monDEQ) retain interesting performance with additional theoretical guaranties. Existing certification tools for classical deep networks cannot directly be applied to monDEQs for which much fewer tools exist. We introduce a semialgebraic representation for ReLU based monDEQs which allows to approximate the corresponding input output relation by semidefinite programming (SDP). We present several applications to network certification and obtain SDP models for the following problems : robustness certification, Lipschitz constant estimation, ellipsoidal uncertainty propagation. We use these models to certify robustness of monDEQs w.r. t. a general $L_q$ norm. Experimental results show that the proposed models outperform existing approaches for monDEQ certification. Furthermore, our investigations suggest that monDEQs are much more robust to $L_2$ perturbations than $L_{\infty}$ perturbations.
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech

Countermeasures to effectively fight the ever increasing hate speech online without blocking freedom of speech is of great social interest. Natural Language Generation (NLG), is uniquely capable of developing scalable solutions. However, off-the-shelf NLG methods are primarily sequence-to-sequence neural models and they are limited in that they generate commonplace, repetitive and safe responses regardless of the hate speech (e.g.,"Please refrain from using such language.") or irrelevant responses, making them ineffective for de-escalating hateful conversations. In this paper, we design a three-module pipeline approach to effectively improve the diversity and relevance. Our proposed pipeline first generates various counterspeech candidates by a generative model to promote diversity, then filters the ungrammatical ones using a BERT model, and finally selects the most relevant counterspeech response using a novel retrieval-based method. Extensive Experiments on three representative datasets demonstrate the efficacy of our approach in generating diverse and relevant counterspeech.
SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients

Federated Learning allows training machine learning models by using the computation and private data resources of a large number of distributed clients such as smartphones and IoT devices. Most existing works on Federated Learning (FL) assume the clients have ground-truth labels. However, in many practical scenarios, clients may be unable to label task-speciﬁc data, e.g., due to lack of expertise. In this work, we consider a server that hosts a labeled dataset, and wishes to leverage clients with unlabeled data for supervised learning. We propose a new Federated Learning framework referred to as SemiFL in order to address the problem of Semi-Supervised Federated Learning (SSFL). In SemiFL, clients have completely unlabeled data, while the server has a small amount of labeled data. SemiFL is communication efﬁcient since it separates the training of server-side supervised data and client-side unsupervised data. We demonstrate various efﬁcient strategies of SemiFL that enhance learning performance. Extensive empirical evaluations demonstrate that our communication efﬁcient method can signiﬁcantly improve the performance of a labeled server with unlabeled clients. Moreover, we demonstrate that SemiFL can outperform many existing FL results trained with fully supervised data, and perform competitively with the state-of-the-art centralized Semi-Supervised Learning (SSL) methods. For instance, in standard communication efﬁcient scenarios, our method can perform 93% accuracy on the CIFAR10 dataset with only 4000 labeled samples at the server. Such accuracy is only 2% away from the result trained from 50000 fully labeled data, and it improves about 30% upon existing SSFL methods in the communication efﬁcient setting.
Chiral selection, isotopic abundance shifts, and autocatalysis of meteoritic amino acids

The discovery of amino acids in meteorites has presented two clues to the origin of their processing subsequent to their formation: a slight preference for left-handedness in some of them, and isotopic anomalies in some of their constituent atoms. Numerous models have been developed to explain these phenomena. In this article we present theoretical results from the Supernova Neutrino Amino Acid Processing (SNAAP) model, which uses electron anti-neutrinos and the magnetic fields from source objects such as supernovae or colliding neutron stars to selectively destroy one amino acid chirality, and the same anti-neutrinos to create isotopic anomalies. For plausible magnetic fields and electron anti-neutrino fluxes, non-zero, positive enantiomeric excesses, ees, defined to be the relative left/right asymmetry in an amino acid population, are reviewed for two amino acids, and conditions are suggested that would produce ee > 0 for all of the α-amino acids. This is accomplished by electron anti-neutrinos produced by the cosmic object interacting with the N nuclei in the amino acids. The relatively high energy anti-neutrinos that produce the ees would inevitably also produce isotopic anomalies. A nuclear reaction network was developed to describe the reactions resulting from them and the nuclides in the meteorites. At similar anti-neutrino fluxes, assumed recombination of the detritus from the anti-neutrino interactions is shown to produce appreciable isotopic anomalies in qualitative agreement with those observed for D/H and N/N. The isotopic anomalies for C/C are predicted to be small, as are also observed. Autocatalysis, a chemical enrichment of a small imbalance, may be necessary for the SNAAP model, or for any model suggested so far, to produce the largest ees observed in meteorites. Thus a new autocatalysis model is developed that includes both the spins of the nuclei and the chirality of the amino acids. Autocatalysis allows the constraints of the original SNAAP model to be relaxed, providing more flexibility in its application, and increasing the probability of meteoroid survival in sites where amino acid processing is possible. The SNAAP Model thus explains all of the observed phenomena related to processing of the amino acids in meteorites. These results have obvious implications for the origin of life on Earth.