Miracle-dz/UniFAR-FTUs huggingface dataset:
CoQEx: Entity Counts Explained

For open-domain question answering, queries on entity counts, such ashow many languages are spoken in Indonesia, are challenging. Such queries can be answered through succinct contexts with counts:estimated 700 languages, and instances:Javanese and Sundanese. Answer candidates naturally give rise to a distribution, where count contexts denoting the queried entity counts and their semantic subgroups often coexist, while the instances ground the counts in their constituting entities. In this demo we showcase the CoQEx methodology (Count Queries Explained) [5,6], which aggregates and structures explanatory evidence across search snippets, for answering user queries related to entity counts [4]. Given a entity count query, our system CoQEx retrieves search-snippets and provides the user with a distribution-aware prediction prediction, categorizes the count contexts into semantic groups and ranks instances grounding the counts, all in real-time. Our demo can be accessed athttps://nlcounqer.mpi-inf.mpg. de/.
Can Pre-trained Language Models Understand Chinese Humor?

Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: whether PLMs are capable of humor understanding? This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.
Dynamic cellular and molecular characteristics of spermatogenesis in the viviparous marine teleost Sebastes schlegelii

Abstract Spermatogenesis is a dynamic cell developmental process that is essential for reproductive success. Vertebrates utilize a variety of reproductive strategies, including sperm diversity, and internal and external fertilization. Research on the cellular and molecular dynamic changes involved in viviparous teleost spermatogenesis, however, is currently lacking. Here, we combined cytohistology, 10 × genomic single-cell RNA-seq, and transcriptome technology to determine the dynamic development characteristics of the spermatogenesis of Sebastes schlegelii. The expressions of lhcgr (Luteinizing hormone/Choriogonadotropin receptor), fshr (follicle-stimulating hormone receptor), ar (androgen receptor), pgr (progesterone receptor), and cox (cyclo-oxygen-ase), as well as the prostaglandin E and F levels peaked during the maturation period, indicating that they were important for sperm maturation and mating. Fifteen clusters were identified based on the 10 × genomic single-cell results. The cell markers of the sub-cluster were identified by their upregulation; piwil, dazl, and dmrt1 were upregulated and identified as spermatogonium markers, and sycp1/3 and spo11 were identified as spermatocyte markers. For S. schlegelii, the sperm head nucleus was elongated (spherical to streamlined in shape), which is a typical characteristic for sperm involved in internal fertilization. We also identified a series of crucial genes associated with spermiogenesis, such as spata6, spag16, kif20a, trip10, and klf10, while kif2c, kifap3, fez2, and spaca6 were found to be involved in nucleus elongation. The results of this study will enrich our cellular and molecular knowledge of spermatogenesis and spermiogenesis in fish that undergo internal fertilization. Summary Sentence In S. schlegelii, luteinizing hormone can induce prostanoid production owing to cox upregulation and prostaglandin may be a key pheromone in copulation and sperm maturation.
Next-generation Challenges of Responsible Data Integration

Data integration has been extensively studied by the data management community and is a core task in the data pre-processing step of ML pipelines. When the integrated data is used for analysis and model training, responsible data science requires addressing concerns about data quality and bias. We present a tutorial on data integration and responsibility, highlighting the existing efforts in responsible data integration along with research opportunities and challenges. In this tutorial, we encourage the community to audit data integration tasks with responsibility measures and develop integration techniques that optimize the requirements of responsible data science. We focus on three critical aspects: (1) the requirements to be considered for evaluating and auditing data integration tasks for quality and bias; (2) the data integration tasks that elicit attention to data responsibility measures and methods to satisfy these requirements; and, (3) techniques, tasks, and open problems in data integration that help achieve data responsibility.
Preference-Based Offline Evaluation

A core step in production model research and development involves the offline evaluation of a system before production deployment. Traditional offline evaluation of search, recommender, and other systems involves gathering item relevance labels from human editors. These labels can then be used to assess system performance using offline evaluation metrics. Unfortunately, this approach does not work when evaluating highly effective ranking systems, such as those emerging from the advances in machine learning. Recent work demonstrates that moving away from pointwise item and metric evaluation can be a more effective approach to the offline evaluation of systems. This tutorial, intended for both researchers and practitioners, reviews early work in preference-based evaluation and covers recent developments in detail.
Cooperative Explanations of Graph Neural Networks

With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations. In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, Cooperative GNN Explanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.
A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter

Memes can be a useful way to spread information because they are funny, easy to share, and can spread quickly and reach further than other forms. With increased interest in COVID-19 vaccines, vaccination-related memes have grown in number and reach. Memes analysis can be difficult because they use sarcasm and often require contextual understanding. Previous research has shown promising results but could be improved by capturing global and local representations within memes to model contextual information. Further, the limited public availability of annotated vaccine critical memes datasets limit our ability to design computational methods to help design targeted interventions and boost vaccine uptake. To address these gaps, we present VaxMeme, which consists of 10,244 manually labelled memes. With VaxMeme, we propose a new multimodal framework designed to improve the memes' representation by learning the global and local representations of memes. The improved memes' representations are then fed to an attentive representation learning module to capture contextual information for classification using an optimised loss function. Experimental results show that our framework outperformed state-of-the-art methods with an F1-Score of 84.2%. We further analyse the transferability and generalisability of our framework and show that understanding both modalities is important to identify vaccine critical memes on Twitter. Finally, we discuss how understanding memes can be useful in designing shareable vaccination promotion, myth debunking memes and monitoring their uptake on social media platforms.
Model for electrical isolation of GaN by light-ion bombardment

We present a model for electrical isolation of GaN by light-ion bombardment. In our model, a decrease in the concentration of free carriers responsible for isolation is assumed to be due to the formation of complexes of ion-beam-generated point defects with shallow donor or acceptor dopants. These defect interaction processes are described in terms of quasichemical reactions. Results show that our model can adequately describe experimental data for electrical isolation in the case of MeV light-ion irradiation of n-GaN.
Bidirectional multiwavelength Brillouin fiber laser generation in a ring cavity

Bidirectional multiwavelength Brillouin fiber laser (BFL) generation is demonstrated using a 25 km long single-mode fiber as a Brillouin gain medium in a ring cavity. Odd-order Brillouin Stokes waves appear in the backward direction whereas Brillouin pump and the even Stokes orders are in the forward direction with the line spacing 0.16 nm (∼20 GHz) between each two consecutive waves in forward and backward directions. In addition, by a combination of the backward and forward outputs, we have a higher number comb generation of a multiwavelength BFL with the line spacing 0.08 nm (∼10 GHz). The proposed configuration can work at any wavelength which is a benefit to the others.
Metrics with exceptional holonomy

It is proved that there exist metrics with holonomy G2 and Spin(7), thus settling the remaining cases in Berger's list of possible holonomy groups. We first reformulate the "holonomy H" condition as a set of differential equations for an associated H-structure on a given manifold. We collect the needed algebraic facts about G2 and Spin(7). We then apply the machinery of over-determined partial differential equations (in the form of the Cartan-Kahler theorem) to prove the existence of solutions whose holonomy is G2 or Spin(7). We also provide explicit examples and some information about the "generality" of the space of
Small-scale effects on buckling of multiwalled carbon nanotubes under axial compression

Based on the theory of nonlocal continuum mechanics, a multiple shell model is developed for the axial buckling of multiwalled carbon nanotubes under axial compression. The effects of small length scale are incorporated in this model. In particular, an explicit expression is derived for the axial buckling strain for a double-walled carbon nanotube. On the basis of this expression, the influence of the small length scale on the axial buckling strain is discussed. As a result, the effect of small length scale on the axial buckling strain is related to the buckling mode and the length-to-radial ratio.
Enterovirus evolution reveals the mechanism of an RNA-targeted antiviral and determinants of viral replication

Selective pressures on positive-strand RNA viruses provide opportunities to establish target site specificity and mechanisms of action of antivirals. Here, Enterovirus-A71 revertant viruses with resistant mutations in the SLII IRES domain (SLIIresist) were selected at low doses of the antiviral DMA-135. The EV-A71 revertant viruses were resistant to DMA-135 at concentrations that robustly inhibit replication of wild-type virus. EV-A71 IRES structures harboring the suppressor mutations induced efficient expression of reporter Luciferase mRNA in the presence of non-cytotoxic doses of DMA-135 whereas DMA-135 dose-dependently inhibited Luciferase expression from the wild-type IRES element. NMR studies indicate that the resistant mutations change the structure of SLII at the bulge loop binding site of DMA-135 and at part of an extended surface recognized by host RNA-binding protein AUF1. Comparisons of biophysical analysis of complexes formed between AUF1, DMA-135, or either SLII or SLIIresist show that DMA-135 stabilizes a ternary complex with AUF1-SLII but not AUF1-SLIIresist. Further studies demonstrate that the hnRNP A1 protein retains binding affinity for SLIIresist, illustrating that DMA-135 inhibition and viral resistance do not perturb the SLII-hnRNP A1 arm of the regulatory axis. Taken together, this work demonstrates how viral evolution under selective pressures of small molecules can elucidate RNA binding site specificity, mechanisms of action, and provide additional insights into the viral pathways inhibited by the antiviral DMA-135.
Interferon inhibits a model RNA virus via a limited set of inducible effector genes

Interferons control viral infection by inducing the expression of antiviral effector proteins encoded by interferon-stimulated genes (ISGs). The field has mostly focused on identifying individual antiviral ISG effectors and defining their mechanisms of action. However, fundamental gaps in knowledge about the interferon response remain. For example, it is not known how many ISGs are required to protect cells from a particular virus, though it is theorized that numerous ISGs act in concert to achieve viral inhibition. Here, we used CRISPR-based loss-of-function screens to identify a markedly limited set of ISGs that confer interferon-mediated suppression of a model alphavirus, Venezuelan equine encephalitis virus (VEEV). We show via combinatorial gene targeting that three antiviral effectors – ZAP, IFIT3, and IFIT1 – together constitute the majority of interferon-mediated restriction of VEEV, while accounting for less than 0.5% of the interferon-induced transcriptome. Together, our data suggests a refined model of the antiviral interferon response in which a small subset of “dominant” ISGs may confer the bulk of the inhibition of a given virus.
A hierarchical network approach to symbolic analysis of large-scale networks

A hierarchical approach to the problem of symbolic circuit analysis of large-scale circuits is presented in this paper. The methodology has been implemented in a computer program called SCAPP (Symbolic Circuit Analysis Program with Partitioning). The method solves the problem by utilizing a hierarchical network approach and the sequence of expressions concept rather than a topological approach and the single expression idea which have dominated symbolic analysis in the past. The result is a linear growth (for real circuits) in the number of terms in the symbolic solutions for the network approach versus the exponential growth exhibited by traditional methods. The analysis methodology uses a Reduced Modified Nodal Analysis (RMNA) technique that allows the characterization of symbolic networks in terms of only a small subset of the network variables (external variables). The analysis algorithm is most efficient when network partitioning is used. Partitioning results in a reduction in the number of terms in the symbolic solutions. >
Human Arm Redundancy - A New Approach for the Inverse Kinematics Problem

The inverse kinematics problem deals with the question of how the nervous system coordinates movement to resolve redundancy, such as in the case of arm reaching movements where more degrees of freedom are available at the joint versus hand level. In particular, this work focuses on determining which coordinate frames can best represent human movements, allowing the motor system to solve the inverse kinematics problem in the presence of kinematic redundancies. In particular, in this work we used a multi-dimensional sparse source separation method called FADA to derive sets of basis functions (here called sources) for both the task and joint spaces, with joint space being represented in terms of either the absolute or anatomical joint angles. We assessed the similarities between the joint and task sources in each of these joint representations. We found that the time-dependent profiles of the absolute reference frame’s sources show greater similarity to those of the corresponding sources in the task space. This result was found to be statistically significant. Hence, our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, to allow for simple transformations between task and joint spaces. Importantly, joint space seems to be represented in terms of an absolute reference frame to achieve successful performance and simplify inverse kinematics transformations in the face of the existing kinematic redundancies. Further studies will be needed to determine the generalizability of this finding and its implications for neural control of movement.
Evolving optical lithography without EUV

This talk will examine the evolutionary path of optical lithography without EUV. We explore the previous history to understand the various solutions that have emerged. This will give insight into future possibilities. We will examine the possible limits and fundamental problems moving into the sub-10nm node regime. Finally, the cost implications and future technologies are explored.
Predicting induction motor circuit parameters

Although boundary-element and finite-element-based numerical codes can now predict torque as a function of slip frequency with extremely high accuracy, most motor manufacturers and factory representatives still favor representing their induction motor as an equivalent circuit. This paper describes how to obtain an equivalent circuit based solely on three parameters: peak torque, frequency at peak torque, and induced stator voltage at peak torque.
Extremfrühgeborene: Schützt die rhIGF-1/rhIGFBP-Substitution vor Hirnschäden?

Extrem unreife Frühgeborene haben postnatal unphysiologisch niedrige IGF-1-Spiegel (IGF-1: insulin-like growth factor-1). Eine internationale Phase II-Studie untersuchte, ob die Behandlung mit einem Komplex aus rekombinantem humanem (rh)IGF-1 und IGF-Binding-Protein-3 (rhIGF-1/ rhIGFBP-3) im Vergleich zur Standardbehandlung Frühgeburtskomplikationen reduziert. Nun berichtet das Forscherteam, ob die Therapie verschiedenen zerebralen Schäden vorbeugt.
Spatially Discrete Nonlinear Diffusion Equations

We consider spatially discrete nonlinear dif- fusion equations that are similar in form to the Cahn-Hilliard and Cahn-Allen equations. Since these equations are spatially discrete, solutions exist even for negative gradient energy co-eﬃcients. In order to study these equations analytically on ﬁnite subsets of one, two and three dimensional lattices, we propose a discrete variational calculus. It is shown that, under very general boundary conditions, these equations possess a gradient structure. We prove the existence of a global attrac- tor and show that when all equilibria are hyperbolic the global attractor consists of the equilibria and the connecting orbits between the equilibria. The equilibria of speciﬁc one-, two-and three-dimensional equations are studied. We exhibit con- stant, two-periodic and three-periodic equilibrium solutions and study their stability properties. Numerical methods for solving the time dependent equations are proposed. We employ a fully implicit time integration scheme and solve the equations on a massively parallel SIMD machine. To take advantage of the structure of our problem and the data parallel computing equipment, we solve the linear systems using the iterative methods CGS and CGNR. Finally, we exhibit the results of our numerical simulations. The numerical results show the robust pattern formation that exists for diﬀerent values of the parameters. these are gradient systems we are able to prove that there exists a compact, connected invariant set for a large range of parameter values that includes the case in which there is no continuum limit PDE. We show that for a negative coeﬃcient in the gradient energy term of the Lyapunov function the period two solution
Deformations of Poisson brackets, Dirac brackets and applications

After a short review of results which we recently obtained on deformations of Lie algebras associated with symplectic manifolds, we discuss physical applications and treat some examples with deformed Poisson brackets. We make explicit a connection between classical and quantum mechanics, and the theory of Dirac brackets for second class constraints, from the viewpoint of deformation theory. Finally we discuss the general Dirac constraints formalism.
On the interpretation of galaxy counts.

New models are presented for the interpretation of recent counts of galaxies to 24th magnitude, and predictions are shown to 28th magnitude for future comparison with data from the Space Telescope. The results supersede earlier, more schematic models by the author. Tyson and Jarvis found in their counts a ''local'' density enhancement at 17th magnitude, on comparison with the earlier models; the excess is no longer significant when a more realistic mixture of galaxy colors is used. Bruzual and Kron's conclusion that Kron's counts show evidence for evolution at faint magnitudes is confirmed, and it is predicted that some 23d magnitude galaxies have redshifts greater than unity. These may include spheroidal systems, elliptical galaxies, and the bulges of early-type spirals and S0's, seen during their primeval rapid star formation.
Modernizing the original influenza vaccine to enhance protective antibody responses against neuraminidase antigens

Most seasonal influenza vaccines are produced using hemagglutinin (HA) surface antigens from inactivated virions. However, virions are thought to be a suboptimal source for the less abundant NA surface antigen, which is also protective against severe disease. Here, we demonstrate that inactivated influenza virions are compatible with two modern approaches for improving protective NA antibody responses. Using a DBA/2J mouse model, we confirmed that the strong infection-induced NA inhibitory (NAI) antibody responses are only achieved by high dose immunizations of inactivated virions, likely due to low viral NA content. Based on this observation, we first produced virions with higher NA content by using reverse genetics to exchange the viral internal gene segments. Single immunizations with these inactivated virions enhanced NAI antibody responses, improved NA-based protection from a lethal viral challenge and allowed the development of natural immunity to the heterotypic challenge virus HA. Second, we combined inactivated virions with recombinant NA proteins. These mixtures increased NA-based protection following viral challenge and elicited stronger NA antibody responses than either component alone, especially when the NAs were homologous. Together, these results indicate that viral- and protein-based vaccines can be combined in a single syringe to improve protective antibody responses to influenza antigens.