Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Development of Erect Leaves Independent of Light Irradiation during the Early Stage of Head Formation in Chinese Cabbage (

We re-examined the role of light in head formation of Chinese cabbage ( Brassica rapa L. var. pekinensis Rupr.) in terms of the hyponastic midrib bending that is induced autonomously during leaf growth. This bending occurs in the basal midrib and can be simulated by a double-truncated-cone geometric model. Before head formation, darkness induced hyponastic bending of the midrib in the uppermost mature leaves. This bending was not simulated by the model, and was not induced autonomously by leaf growth. During early head formation, however, the hyponastic midrib bending of the uppermost mature leaves occurred autonomously, irrespective of irradiation. Light intensity on the abaxial face of erect leaves increased compared with levels before head formation. Alternating light and dark treatments did not affect bending at this stage. Con-sequently, the main developmental factor for erect leaves during early head formation is likely to be autonomously induced midrib bending during growth rather than a response to reduced light intensity and shading by outer leaves.
Smallest eigenvalue distribution of the fixed-trace Laguerre beta-ensemble

In this paper we study the entanglement of the reduced density matrix of a bipartite quantum system in a random pure state. It transpires that this involves the computation of the smallest eigenvalue distribution of the fixed-trace Laguerre ensemble of N × N random matrices. We showed that for finite N the smallest eigenvalue distribution may be expressed in terms of Jack polynomials. Furthermore, based on the exact results, we found a limiting distribution when the smallest eigenvalue is suitably scaled with N followed by a large N limit. Our results turn out to be the same as the smallest eigenvalue distribution of the classical Laguerre ensembles without the fixed-trace constraint. This suggests in a broad sense, the global constraint does not influence local correlations, at least, in the large N limit. Consequently, we have solved an open problem: the determination of the smallest eigenvalue distribution of the reduced density matrix—obtained by tracing out the environmental degrees of freedom—for a bipartite quantum system of unequal dimensions.
Admissible groups over two dimensional complete local domains

Let K be the quotient field of a complete local domain of dimension 2 with a separably closed residue field. Let G be a finite group of order not divisible by char(K). Then G is admissible over K if and only if its Sylow subgroups are abelian of rank at most 2.
RELATIVE RUAN AND GROMOV-TAUBES INVARIANTS OF SYMPLECTIC 4-MANIFOLDS

We define relative Ruan invariants that count embedded connected symplectic submanifolds which contact a fixed symplectic hypersurface V in a symplectic 4-manifold (X, ω) at prescribed points with prescribed contact orders (in addition to insertions on X\V). We obtain invariants of the deformation class of (X, V, ω). Two large issues must be tackled to define such invariants: (1) curves lying in the hypersurface V and (2) genericity results for almost complex structures constrained to make V pseudo-holomorphic (or almost complex). Moreover, these invariants are refined to take into account rim-tori decompositions. In the latter part of the paper, we extend the definition to disconnected submanifolds and construct relative Gromov–Taubes invariants.
Deformation Quantization of Principal Fibre Bundles and Classical Gauge Theories

In this dissertation the notion of deformation quantization of principal fibre bundles is established and investigated in order to find a geometric formulation of classical gauge theories on noncommutative space-times. As a generalization, the notion of deformation quantization of surjective submersions is also discussed. It is shown that deformation quantizations of surjective submersions and principal fibre bundles always exist and are unique up to equivalence. These statements concerning complex-valued functions are moreover formulated and proved for sections of arbitrary vector bundles over the total space, in particular equivariant vector bundles. The commutants of the deformed right module structures within the differential operators, playing an inportant role with regard to the infinitesimal gauge transformations, are computed explicitly in each case. Depending on the choice of specific covariant derivatives and connections the commutants are isomorphic to the formal power series of the respective vertical differential operators which thus inherit a deformation of the algebra structure. The resulting deformed bimodules are again unique up to equivalence. With respect to further applications it is finally shown that every deformation quantization of a principal fibre bundle induces a deformation quantization of any associated vector bundle.
Convergence of approximate deconvolution models to the filtered Navier-Stokes Equations

We consider a 3D Approximate Deconvolution Model (ADM) which belongs to the class of Large Eddy Simulation (LES) models. We work with periodic boundary conditions and the filter that is used to average the fluid equations is the Helmholtz one. We prove existence and uniqueness of what we call a "regular weak" solution $(\wit_N,q_N)$ to the model, for any fixed order $N\in\N$ of deconvolution. Then, we prove that the sequence $\{(\wit_N,q_N)\}_{N \in \N}$ converges -in some sense- to a solution of the filtered Navier-Stokes equations, as $N$ goes to infinity. This rigorously shows that the class of ADM models we consider have the most meaningful approximation property for averages of solutions of the Navier-Stokes equations.
Attention-based Knowledge Distillation in Multi-attention Tasks: The Impact of a DCT-driven Loss

Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In this paper, we propose and analyse the use of a 2D frequency transform of the activation maps before transferring them. We pose that\textemdash by using global image cues rather than pixel estimates, this strategy enhances knowledge transferability in tasks such as scene recognition, defined by strong spatial and contextual relationships between multiple and varied concepts. To validate the proposed method, an extensive evaluation of the state-of-the-art in scene recognition is presented. Experimental results provide strong evidences that the proposed strategy enables the student network to better focus on the relevant image areas learnt by the teacher network, hence leading to better descriptive features and higher transferred performance than every other state-of-the-art alternative. We publicly release the training and evaluation framework used along this paper at http://www-vpu.eps. uam.es/publications/DCTBasedKDForSceneRecognition.
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways

Lane detection is a critical function for autonomous driving. With the recent development of deep learning and the publication of camera lane datasets and benchmarks, camera lane detection networks (CLDNs) have been remarkably developed. Unfortunately, CLDNs rely on camera images which are often distorted near the vanishing line and prone to poor lighting condition. This is in contrast with Lidar lane detection networks (LLDNs), which can directly extract the lane lines on the bird’s eye view (BEV) for motion planning and operate robustly under various lighting conditions. However, LLDNs have not been actively studied, mostly due to the absence of large public lidar lane datasets. In this paper, we introduce KAIST-Lane (K-Lane), the world’s first and the largest public urban road and highway lane dataset for Lidar. K-Lane has more than 15K frames and contains annotations of up to six lanes under various road and traffic conditions, e.g., occluded roads of multiple occlusion levels, roads at day and night times, merging (converging and diverging) and curved lanes. We also provide baseline networks we term Lidar lane detection networks utilizing global feature correlator (LLDN-GFC). LLDN-GFC exploits the spatial characteristics of lane lines on the point cloud, which are sparse, thin, and stretched along the entire ground plane of the point cloud. From experimental results, LLDNGFC achieves the state-of-the-art performance with an F1score of 82.1%, on the K-Lane. Moreover, LLDN-GFC shows strong performance under various lighting conditions, which is unlike CLDNs, and also robust even in the case of severe occlusions, unlike LLDNs using the conventional CNN. The K-Lane, LLDN-GFC training code, pretrained models, and complete development kits including evaluation, visualization and annotation tools are available at https://github.com/kaist-avelab/k-lane.
Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol Conformers

Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and trans (anti) and gauche conformers, which are known to be very close in energy. Here we show that based on rigorous quantum calculations of the vibrational zero-point state, using a new ab initio potential energy surface (PES), the ground state resembles the trans conformer, but substantial delocalization to the gauche conformer is present. This explains experimental issues about identification and isolation of the two conformers. This “leak” effect is partially quenched when deuterating the OH group, which further demonstrates the need for a quantum mechanical approach. Diffusion Monte Carlo and full-dimensional semiclassical dynamics calculations are employed. The new PES is obtained by means of a Δ-machine learning approach starting from a pre-existing low level density functional theory surface. This surface is brought to the CCSD(T) level of theory using a relatively small number of ab initio CCSD(T) energies. Agreement between the corrected PES and direct ab initio results for standard tests is excellent. One- and two-dimensional discrete variable representation calculations focusing on the trans–gauche torsional motion are also reported, in reasonable agreement with experiment.
Mechanically transferred large-area Ga$_2$O$_3$ passivates graphene and suppresses interfacial phonon scattering

We demonstrate a large-area passivation layer for graphene by mechanical transfer of ultrathin amorphous Ga 2 O 3 synthesized on liquid Ga metal. A comparison of temperature-dependent electrical measurements of millimetre-scale passivated and bare graphene on SiO 2 /Si indicate that the passivated graphene maintains its high ﬁeld eﬀect mobility desirable for applications. Surprisingly, the temperature-dependent resistivity is reduced in passivated graphene over a range of temperatures below 220 K, due to the interplay of screening of the surface optical phonon modes of the SiO 2 by high-dielectric-constant Ga 2 O 3 , and the relatively high characteristic phonon frequencies of Ga 2 O 3 . Raman spectroscopy and electrical measurements indicate that Ga 2 O 3 passivation also protects graphene from further processing such as plasma-enhanced atomic layer deposition of Al 2 O 3 .
MorisienMT: A Dataset for Mauritian Creole Machine Translation

In this paper, we describe MorisienMT, a dataset for benchmarking machine translation quality of Mauritian Creole. Mauritian Creole (Morisien) is the lingua franca of the Republic of Mauritius and is a French-based creole language. MorisienMT consists of a parallel corpus between English and Morisien, French and Morisien and a monolingual corpus for Morisien. We first give an overview of Morisien and then describe the steps taken to create the corpora and, from it, the training and evaluation splits. Thereafter, we establish a variety of baseline models using the created parallel corpora as well as large French--English corpora for transfer learning. We release our datasets publicly for research purposes and hope that this spurs research for Morisien machine translation.
Learning from Counterfactual Links for Link Prediction

Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and existence of link between a pair of nodes. However, the causal relationship between the two variables was largely ignored for learning to predict links on a graph. In this work, we visit this factor by asking a counterfactual question:"would the link still exist if the graph structure became different from observation? "Its answer, counterfactual links, will be able to augment the graph data for representation learning. To create these links, we employ causal models that consider the information (i.e., learned representations) of node pairs as context, global graph structural properties as treatment, and link existence as outcome. We propose a novel data augmentation-based link prediction method that creates counterfactual links and learns representations from both the observed and counterfactual links. Experiments on benchmark data show that our graph learning method achieves state-of-the-art performance on the task of link prediction.
Complete 4-manifolds with uniformly positive isotropic curvature

We prove the following result: Let $(X,g_0)$ be a complete, connected 4-manifold with uniformly positive isotropic curvature and with bounded geometry. Then there is a finite collection $\mathcal{F}$ of manifolds of the form $\mathbb{S}^3 \times \mathbb{R} /G$, where $G$ is a fixed point free discrete subgroup of the isometry group of the standard metric on $\mathbb{S}^3\times \mathbb{R}$, such that $X$ is diffeomorphic to a (possibly infinite) connected sum of copies of $\mathbb{S}^4,\mathbb{RP}^4$ and/or members of $\mathcal{F}$. This extends recent work of Chen-Tang-Zhu and Huang. We also extend the above result to the case of orbifolds. The proof uses Ricci flow with surgery on complete orbifolds.
Mannheim Partner D-Curves in Minkowski 3-space

In this paper, we give the definition, different types and characterizations of Mannheim partner D-curves in Minkowski 3-space. We find the relations between the geodesic curvatures, the normal curvatures and the geodesic torsions of these associated curves. Furthermore, we show that the definition and the characterizations of Mannheim partner D-curves include those of Mannheim partner curves in some special cases in Minkowski 3-space.
Solvability of invariant systems of differential equations on $\mathbb{H}^2$ and beyond

We show how the Fourier transform for distributional sections of vector bundles over symmetric spaces of non-compact type G/K can be used for questions of solvability of systems of invariant diﬀerential equations in analogy to Hörmander’s proof of the Ehrenpreis-Malgrange theorem. We get complete solvability for the hyperbolic plane H 2 and partial results for products H 2 × · · · × H 2 and the hyperbolic 3-space H 3 . on the 3-hyperbolic space H 3
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows

Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by restricting function classes or model parameters and the inverse transformation is often approximated by root-finding algorithms as a closed-form inverse is unavailable. In this paper, we introduce a new integral-based approach termed"Atomic Unrestricted Time Machine (AUTM)", equipped with unrestricted integrands and easy-to-compute explicit inverse. AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters. Theoretically, we present a constructive proof that AUTM is universal: all monotonic normalizing flows can be viewed as limits of AUTM flows. We provide a concrete example to show how to approximate any given monotonic normalizing flow using AUTM flows with guaranteed convergence. The result implies that AUTM can be used to transform an existing flow into a new one equipped with explicit inverse and unrestricted parameters. The performance of the new approach is evaluated on high dimensional density estimation, variational inference and image generation. Experiments demonstrate superior speed and memory efficiency of AUTM.
Combinatorial Causal Bandits

In combinatorial causal bandits (CCB), the learning agent chooses at most K variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable Y. We study under the context of binary generalized linear models (BGLMs) with a succinct parametric representation of the causal models. We present the algorithm BGLM-OFU for Markovian BGLMs (i.e., no hidden variables) based on the maximum likelihood estimation method and give regret analysis for it. For the special case of linear models with hidden variables, we apply causal inference techniques such as the do calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables. Our novelty includes (a) considering the combinatorial intervention action space and the general causal graph structures including ones with hidden variables, (b) integrating and adapting techniques from diverse studies such as generalized linear bandits and online influence maximization, and (c) avoiding unrealistic assumptions (such as knowing the joint distribution of the parents of Y under all interventions) and regret factors exponential to causal graph size in prior studies.
Infrared plasmons propagate through a hyperbolic nodal metal

Metals are canonical plasmonic media at infrared and optical wavelengths, allowing one to guide and manipulate light at the nanoscale. A special form of optical waveguiding is afforded by highly anisotropic crystals revealing the opposite signs of the dielectric functions along orthogonal directions. These media are classified as hyperbolic and include crystalline insulators, semiconductors, and artificial metamaterials. Layered anisotropic metals are also anticipated to support hyperbolic waveguiding. However, this behavior remains elusive, primarily because interband losses arrest the propagation of infrared modes. Here, we report on the observation of propagating hyperbolic waves in a prototypical layered nodal-line semimetal ZrSiSe. The observed waveguiding originates from polaritonic hybridization between near-infrared light and nodal-line plasmons. Unique nodal electronic structures simultaneously suppress interband loss and boost the plasmonic response, ultimately enabling the propagation of infrared modes through the bulk of the crystal.
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation

Emotion Recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent characteristics of these modalities, multi-modal ERC has always been considered a challenging undertaking. Existing ERC research focuses mainly on using text information in a discussion, ignoring the other two modalities. We anticipate that emotion recognition accuracy can be improved by employing a multi-modal approach. Thus, in this study, we propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. We introduce a new feature extractor to extract latent features from the audio and visual modality. The proposed feature extractor is trained with a novel adaptive margin-based triplet loss function to learn emotion-relevant features from the audio and visual data. In the domain of ERC, the existing methods perform well on one benchmark dataset but not on others. Our results show that the proposed M2FNet architecture outperforms all other methods in terms of weighted average F1 score on well-known MELD and IEMOCAP datasets and sets a new state-of-the-art performance in ERC.
DCAN: Diversified News Recommendation with Coverage-Attentive Networks

Self-attention based models are widely used in news recommendation tasks. However, previous Attention architecture does not constrain repeated information in the user’s historical behavior, which limits the power of hidden representation and leads to some problems such as information redundancy and ﬁlter bubbles. To solve this problem, we propose a personalized news recommendation model called DCAN.It captures multi-grained user-news matching signals through news encoders and user encoders. We keep updating a coverage vector to track the history of news attention and augment the vector in 4 types of ways. Then we fed the augmented Coverage vector into the Multi-headed Self-attention model to help adjust the future attention and added the Coverage regulation to the loss function(CRL), which enabled the recommendation system to consider more about differentiated information. Extensive experiments on Microsoft News Recommendation Dataset (MIND) show that our model signiﬁcantly improve the diversity of news recommendations with minimal sacriﬁce in accuracy.
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength

Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ignore the fact that samples generated by the same causal mechanism follow the same causal relationships. In this paper, we seek to explore such information by leveraging do-operation for reducing supervision strength. We propose a framework which implements do-operation by swapping latent cause and effect factors encoded from a pair of inputs. Moreover, we also identify the inadequacy of existing causal representation metrics empirically and theoretically and introduce new metrics for better evaluation. Experiments conducted on both synthetic and real datasets demonstrate the superiorities of our method compared with state-of-the-art methods. extract