Miracle-dz/UniFAR-FTUs huggingface dataset:
Warp drive solutions in spherical coordinates with anisotropic matter configurations

In this work we study the influence of isotropic and anisotropic fluids on the spherically symmetric warp metric. We evaluate the energy conditions and the influence of including a cosmological constant type term. We find that, considering this term, there is a trade-off between the weak and strong energy conditions. The obtained solutions are numerical and we solve the system for both the stationary and the full regime. The influence of imposing the zero expansion condition has been explored. We find a wide diversity of behaviours for the solutions. In general there are regions of spacetime where the energy conditions can be at least partially satisfied. Finally, we calculate the value of the total mass using the density found in the numerical simulations, finding examples where it remains positive during the entire evolution of the system.
Generalized Linear Models of T$_{90}$-T$_{50}$ relation to classify GRBs

Gamma-ray bursts (GRBs) can be classified with their linearly dependent parameters alongside the standard $T_{90}$ distribution. The Generalized linear mixture model(GLM) identifies the number of linear dependencies in a two-parameter space. Classically, GRBs are classified into two classes by the presence of bimodality in the histogram of T$_{90}$. However, additional classes and sub-classes of GRBs are fascinating topics to explore. In this work, we investigate the GRBs classes in the $ T_{90} {-}T_{50}$ plane using the Generalized Linear Models(GLM) for Fermi GBM and BATSE catalogs. This study shows five linear features for the Fermi GBM catalog and four linear features for the BATSE catalog, directing towards the possibility of more than two GRB classes.
Textbook On Spherical Astronomy

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Historical constraints on the evolution of efficient color naming

Color naming in natural languages is not arbitrary: it reflects efficient partitions of perceptual color space modulated by the relative needs to communicate about different colors. These psychophysical and communicative constraints help explain why languages around the world have remarkably similar, but not identical, mappings of colors to color terms. Languages converge on a small set of efficient representations. But languages also evolve, and the number of terms in a color vocabulary may change over time. Here we show that history, i.e. the existence of an antecedent color vocabulary, acts as a non-adaptive constraint that biases the choice of efficient solution as a language transitions from a vocabulary of size n to n+1 terms. Moreover, as vocabularies evolve to include more terms they explore a smaller fraction of all possible efficient vocabularies compared to equally-sized vocabularies constructed de novo. This path dependence on the cultural evolution of color naming presents an opportunity. Historical constraints can be used to reconstruct ancestral color vocabularies, allowing us to answer long-standing questions about the evolutionary sequences of color words, and enabling us to draw inferences from phylogenetic patterns of language change.
Sliced Inverse Regression with Large Structural Dimensions

The central space of a joint distribution $(\vX,Y)$ is the minimal subspace $\mathcal S$ such that $Y\perp\hspace{-2mm}\perp \vX \mid P_{\mathcal S}\vX$ where $P_{\mathcal S}$ is the projection onto $\mathcal S$. Sliced inverse regression (SIR), one of the most popular methods for estimating the central space, often performs poorly when the structural dimension $d=\operatorname{dim}\left( \mathcal S \right)$ is large (e.g., $\geqs 5$). In this paper, we demonstrate that the generalized signal-noise-ratio (gSNR) tends to be extremely small for a general multiple-index model when $d$ is large. Then we determine the minimax rate for estimating the central space over a large class of high dimensional distributions with a large structural dimension $d$ (i.e., there is no constant upper bound on $d$) in the low gSNR regime. This result not only extends the existing minimax rate results for estimating the central space of distributions with fixed $d$ to that with a large $d$, but also clarifies that the degradation in SIR performance is caused by the decay of signal strength. The technical tools developed here might be of independent interest for studying other central space estimation methods.
Sub-100 nm {\beta}-Ga2O3 MOSFET with 55 GHz fMAX and>100 V breakdown

This letter reports a highly scaled 90 nm gate length beta-Ga2O3 T-gate MOSFET with no current collapse and record power gain cut off frequency (fMAX). The epitaxial stack of 60 nm thin channel MOSFET was grown by Molecular Beam Epitaxy (MBE) and highly doped (n++) contact regrowth was carried out by Metal Organic Chemical Vapour Deposition (MOCVD) in the source/drain region. Maximum on current (IDS, MAX) of 160 mA/mm and transconductance (gm) around 36 mS/mm was measured at VDS= 10 V for LSD= 1.5 micrometer channel length. Transconductance is limited by higher channel sheet resistance (Rsheet). We observed no current collapse for both drain and gate lag measurement even at higher VDG,Q quiescent bias points. This is the first report of Ga2O3 FET showing no current collapse without any external passivation. Breakdown voltage around 125 V was reported for LGD= 1.2 micrometer. We extracted 27 GHz current gain cut off frequency (fT) and 55 GHz fMAX for 20 V drain bias. fMAX value mentioned here is the highest for Ga2O3 and the first demonstration of 55 GHz operation. fT. VBR product of 3.375 THz.V has been calculated which is comparable with state-of-art GaN HEMT. This letter suggests that Ga2O3 can be a suitable candidate for X-band application.
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset

Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED dataset, covering 12 typologically diverse languages from 9 language families, which is created by machine-translating TACRED instances and automatically projecting their entity annotations. We analyze translation and annotation projection quality, identify error categories, and experimentally evaluate fine-tuned pretrained mono- and multilingual language models in common transfer learning scenarios. Our analyses show that machine translation is a viable strategy to transfer RE instances, with native speakers judging more than 83% of the translated instances to be linguistically and semantically acceptable. We find monolingual RE model performance to be comparable to the English original for many of the target languages, and that multilingual models trained on a combination of English and target language data can outperform their monolingual counterparts. However, we also observe a variety of translation and annotation projection errors, both due to the MT systems and linguistic features of the target languages, such as pronoun-dropping, compounding and inflection, that degrade dataset quality and RE model performance.
Computation of Rate-Distortion-Perception Function under f-Divergence Perception Constraints

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of f-divergences. For that, we leverage the fact that RDPF, assuming mild regularity conditions on the perception constraint, forms a convex programming problem. We first develop parametric characterizations of the optimal solution and utilize them in an alternating minimization approach for which we prove convergence guarantees. The resulting structure of the iterations of the alternating minimization approach renders the implementation of a generalized Blahut-Arimoto (BA) type of algorithm infeasible. To overcome this difficulty, we propose a relaxed formulation of the structure of the iterations in the alternating minimization approach, which allows for the implementation of an approximate iterative scheme. This approximation is shown, via the derivation of necessary and sufficient conditions, to guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and the perception constraints which guarantee that our algorithm converges exponentially fast. We corroborate our theoretical results with numerical simulations, and we draw connections with existing results.
Block Crossings in One-Sided Tanglegrams

Tanglegrams are drawings of two rooted binary phylogenetic trees and a matching between their leaf sets. The trees are drawn crossing-free on opposite sides with their leaf sets facing each other on two vertical lines. Instead of minimizing the number of pairwise edge crossings, we consider the problem of minimizing the number of block crossings, that is, two bundles of lines crossing each other locally. With one tree fixed, the leaves of the second tree can be permuted according to its tree structure. We give a complete picture of the algorithmic complexity of minimizing block crossings in one-sided tanglegrams by showing NP-completeness, constant-factor approximations, and a fixed-parameter algorithm. We also state first results for non-binary trees.
Estimation and Testing of the Jump Component in Levy Processes

In this thesis, a new method based on characteristic functions is proposed to estimate the jump component in a finite-activity Levy process, which includes the jump frequency and the jump size distribution. Properties of the estimators are investigated, which show that this method does not require high frequency data. The implementation of the method is discussed, and examples are provided. We also perform a comparison which shows that our method has advantages over an existing threshold method. Finally, two applications are included: one is the classification of the increments of the model, and the other is the testing for a change of jump frequency.
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation

Conversational recommender systems ( CRS s) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user. Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios. To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues. Different from it, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR). In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED ) from real-world E-commerce scenarios. U-NEED consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148 product knowledge tuples. To facilitate the research of UNECR, we propose 5 critical tasks: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue generation and (v) pre-sales dialogue evaluation. We establish baseline methods and evaluation metrics for each task. We report experimental results of 5 tasks on U-NEED . We also report results on 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different.
Interpreting Training Aspects of Deep-Learned Error-Correcting Codes

As new deep-learned error-correcting codes continue to be introduced, it is important to develop tools to interpret the designed codes and understand the training process. Prior work focusing on the deep-learned TurboAE has both interpreted the learned encoders post-hoc by mapping these onto nearby "interpretable" encoders, and experimentally evaluated the performance of these interpretable encoders with various decoders. Here we look at developing tools for interpreting the training process for deep-learned error-correcting codes, focusing on: 1) using the Goldreich-Levin algorithm to quickly interpret the learned encoder; 2) using Fourier coefficients as a tool for understanding the training dynamics and the loss landscape; 3) reformulating the training loss, the binary cross entropy, by relating it to encoder and decoder parameters, and the bit error rate (BER); 4) using these insights to formulate and study a new training procedure. All tools are demonstrated on TurboAE, but are applicable to other deep-learned forward error correcting codes (without feedback).
Andreev reflection at the altermagnet-superconductor interface

Altermagnets are a new class of magnetic materials, which exhibit large spin splitting, but due to the combined spin and real space group symmetry protection maintain zero net macroscopic magnetization. Such a characteristic may prove them to be superior in applications in superconducting heterostructures and thus here we investigate the Andreev reflection at the altermagnet/superconductor interface. We compare and contrast altermagnets to other magnetic materials, revealing qualitative differences in the behavior of altermagnetic junction depending on the Fermi surface orientation. We study the resonant states arising in setups with strong tunneling barriers and show that sensitivity to non-magnetic disorder is also dependent on the orientation. Our results provide a building block for altermagnetic superconducting heterostructures such as Josephson $\pi$ junctions with superior properties.
Spatiotemporal Transformer for Stock Movement Prediction

Financial markets are an intriguing place that offer investors the potential to gain large profits if timed correctly. Unfortunately, the dynamic, non-linear nature of financial markets makes it extremely hard to predict future price movements. Within the US stock exchange, there are a countless number of factors that play a role in the price of a company's stock, including but not limited to financial statements, social and news sentiment, overall market sentiment, political happenings and trading psychology. Correlating these factors is virtually impossible for a human. Therefore, we propose STST, a novel approach using a Spatiotemporal Transformer-LSTM model for stock movement prediction. Our model obtains accuracies of 63.707 and 56.879 percent against the ACL18 and KDD17 datasets, respectively. In addition, our model was used in simulation to determine its real-life applicability. It obtained a minimum of 10.41% higher profit than the S&P500 stock index, with a minimum annualized return of 31.24%.
Optimized Pilot Distribution to Track the Phase Noise in DFT-s-OFDM for sub-THz Systems

In this paper, we focus on the selection of a pilot pattern to track the phase noise in high frequency bands in a DFT-s-OFDM chain. By considering the Wiener filter at the receiver side to perform the tracking, we use the inner cost function of the said filter as the cost function for the pilot selection. To obtain this cost function, the phase noise autocorrelation is required. Therefore, we introduce a new mathematical approximation of the autocorrelation function of the practical 3GPP phase noise model. At first, this leads to an analytical expression of the Wiener filter coefficients. Then, the said coefficients allow us to obtain an analytical expression of the cost function. Thus, by means of this result, we are able to provide a pilot pattern that jointly satisfies a constraint on the pilot overhead and a constraint on the minimum performance of the Wiener filter.
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation

Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation. However, the discrete nature of text increases the difficulty of CDM to generate coherent and fluent texts, and also causes the incompatibility problem between CDM and advanced NLP techniques, especially the popular pre-trained language models (PLMs). To solve it, we propose Diffusion-NAT, which introduces discrete diffusion models (DDM) into NAR text-to-text generation and integrates BART to improve the performance.By revising the decoding process of BART and the typical settings of DDM, we unify the inference process of BART and the denoising process of DDM into the same NAR masked tokens recovering task.In this way, DDM can rely on BART to perform denoising, which can benefit from both the rich pre-learned knowledge of BART and the iterative refining paradigm of DDM.Besides, we also propose the iterative self-prompting strategy to further improve the generation quality.Experimental results on 7 datasets show that our approach can outperform competitive NAR methods, and even surpass autoregressive methods.Our code and data are released at https://github.com/RUCAIBox/DiffusionNAT.
Gradient descent with a general cost

We present a new class of gradient-type optimization methods that extends vanilla gradient descent, mirror descent, Riemannian gradient descent, and natural gradient descent. Our approach involves constructing a surrogate for the objective function in a systematic manner, based on a chosen cost function. This surrogate is then minimized using an alternating minimization scheme. Using optimal transport theory we establish convergence rates based on generalized notions of smoothness and convexity. We provide local versions of these two notions when the cost satisfies a condition known as nonnegative cross-curvature. In particular our framework provides the first global rates for natural gradient descent and the standard Newton's method.
Two-step calibration of design weights in survey sampling

ABSTRACT In this article, a new two-step calibration technique of design weights is proposed. In the first step, the calibration weights are set proportional to the design weights in a given sample. In the second step, the constants of proportionality are determined based on different objectives of the investigator such as bias reduction or minimum mean squared error. Many estimators available in the literature can be shown to be special cases of the proposed two-step calibrated estimator. A simulation study, based on a real data set, is also included at the end. A few technical issues are raised with respect to the use of the proposed calibration technique: both limitations and benefits are discussed.
Polynomial superpotential for Grassmannian $Gr(k,n)$ from a limit of vertex function

In this note we discuss an integral representation for the vertex function of the cotangent bundle over the Grassmannian, $X=T^{*} Gr(k,n)$. This integral representation can be used to compute the $\hbar\to \infty$ limit of the vertex function, where $\hbar$ denotes the equivariant parameter of a torus acting on $X$ by dilating the cotangent fibers. We show that in this limit the integral turns into the standard mirror integral representation for the $A$-series of the Grassmannian $Gr(k,n)$ with the Laurent polynomial Landau-Ginzburg superpotential of Eguchi, Hori and Xiong. We also observe some Dwork type congruences for the coefficients of the $A$-series.
Advanced skills for general surgeons and carving out a niche: Endoscopic retrograde cholangiopancreatography in regional Australia

The provision of high‐quality healthcare in rural Australian hospitals has necessitated general surgeons to take up the challenge and expand their expertise to advanced endoscopy techniques including endoscopic retrograde cholangiopancreatography (ERCP). This study examines the utility of ERCP in a regional setting especially in decreasing patient transfers and associated costs while achieving exceptional safety by measuring key indicators of safety and efficacy.
Locally Attentional SDF Diffusion for Controllable 3D Shape Generation

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and control the local geometry of generated shapes. To address these challenges, we propose a diffusion-based 3D generation framework --- locally attentional SDF diffusion, to model plausible 3D shapes, via 2D sketch image input. Our method is built on a two-stage diffusion model. The first stage, named occupancy-diffusion, aims to generate a low-resolution occupancy field to approximate the shape shell. The second stage, named SDF-diffusion, synthesizes a high-resolution signed distance field within the occupied voxels determined by the first stage to extract fine geometry. Our model is empowered by a novel view-aware local attention mechanism for image-conditioned shape generation, which takes advantage of 2D image patch features to guide 3D voxel feature learning, greatly improving local controllability and model generalizability. Through extensive experiments in sketch-conditioned and category-conditioned 3D shape generation tasks, we validate and demonstrate the ability of our method to provide plausible and diverse 3D shapes, as well as its superior controllability and generalizability over existing work.
Cross-Modal Retrieval for Motion and Text via MildTriple Loss

Cross-modal retrieval has become a prominent research topic in computer vision and natural language processing with advances made in image-text and video-text retrieval technologies. However, cross-modal retrieval between human motion sequences and text has not garnered suﬃcient attention despite the extensive application value it holds, such as aiding virtual reality applications in better understanding users’ actions and language. This task presents several challenges, including joint modeling of the two modalities, demanding the understanding of person-centered information from text, and learning behavior features from 3D human motion sequences. Previous work on motion data modeling mainly relied on autoregressive feature extractors that may forget previous information, whileweproposeaninnovativemodelthatincludessimpleyetpowerfultransformer-basedmotionandtextencoders,whichcanlearnrepresentationsfromthetwodiﬀerentmodalitiesandcapturelong-term dependencies.Furthermore,theoverlapofthesameatomicactionsofdiﬀerenthumanmotionscancausesemanticconﬂicts,leadingustoexploreanewtripletlossfunction,MildTripleLoss.itleverages thesimilaritybetweensamplesinintra-modalspacetoguidesoft-hardnegativesamplemininginthejointembeddingspacetotrainthetripletlossandreducetheviolationcausedbyfalsenegative samples.WeevaluatedourmodelandmethodonthelatestHumanML3DandKITMotion-Languagedatasets,achievinga62.9%recallformotionretrievalanda71.5%recallfortextretrieval(basedon R@10)ontheHumanML3Ddataset.Ourcodeisavailableathttps://github.com/eanson023/rehamot.