Miracle-dz/UniFAR-FTUs huggingface dataset:
SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation

In this study, we present a Bangla multi-domain sentiment analysis dataset, named as SentiGOLD, developed using 70,000 samples, which was compiled from a variety of sources and annotated by a gender-balanced team of linguists. This dataset was created in accordance with a standard set of linguistic conventions that were established after multiple meetings between the Government of Bangladesh and a nationally recognized Bangla linguistics committee. Although there are standard sentiment analysis datasets available for English and other rich languages, there are not any such datasets in Bangla, especially because, there was no standard linguistics framework agreed upon by national stakeholders. Senti-GOLD derives its raw data from online video comments, social media posts and comments, blog posts and comments, news and numerous other sources. Throughout the development of this dataset, domain distribution and class distribution were rigorously maintained. SentiGOLD was created using data from a total of 30 domains (e.g. politics, entertainment, sports, etc.) and was labeled using 5 classes (e.g. strongly negative, weakly negative, neutral, weakly positive, and strongly positive). In order to maintain annotation quality, the national linguistics committee approved an annotation scheme to ensure a rigorous Inter Annotator Agreement (IAA) in a multi-annotator annotation scenario. This procedure yielded an IAA score of 0.88 using Fleiss' kappa method, which is elaborated upon in the paper. A protocol for intra- and cross-dataset evaluation was utilized in our efforts to develop a classification system as a standard. The cross-dataset evaluation was performed on the SentNoB dataset, which contains noisy Bangla text samples, thereby establishing a demanding test scenario. We also performed cross-dataset testing by employing zero-shot experiments, and our best model produced competitive performance, which exemplify our dataset's generalizability. Our top model attained a macro f1 of 0.62 (intra-dataset) for 5 classes establishing the benchmark for SentiGOLD, and 0.61 (cross-dataset from SentNoB) for 3 classes which stands comparable to the current state-of-the-art. Our fine-tuned sentiment analysis model\footnotehttps://sentiment.bangla. gov.bd can be accessed online.
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings

Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is necessary. However, the currently used representations of three-dimensional (3D) shapes either require extensive computational resources to learn or suffer from significant information loss, which impairs their effectiveness in surrogate modeling. To address this issue, we propose a new two-dimensional (2D) representation of 3D shapes. We develop a surrogate drag model based on this representation to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics (CFD) simulations to train our model. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an R2 value above 0.84 for various car categories. Moreover, the proposed representation method can be generalized to many other product categories beyond cars. Our model is implemented using deep neural networks, making it compatible with recent AI image generation tools (such as Stable Diffusion) and a significant step towards the automatic generation of drag-optimized car designs. We have made the dataset and code publicly available at https://decode.mit. edu/projects/dragprediction/.
To Save Mobile Crowdsourcing from Cheap-talk: A Game Theoretic Learning Approach

Today mobile crowdsourcing platforms invite users to provide anonymous reviews about service experiences, yet many reviews are found biased to be extremely positive or negative. The existing methods find it difficult to learn from biased reviews to infer the actual service state, as the state can also be extreme and the platform cannot verify the truthfulness of reviews immediately. Further, reviewers can hide their (positive or negative) bias types and proactively adjust their anonymous reviews against the platform's inference. To our best knowledge, we are the first to study how to save mobile crowdsourcing from cheap-talk and strategically learn from biased users' reviews. We formulate the problem as a dynamic Bayesian game, including users' service-type messaging and the platform's follow-up rating/inference. Our closed-form PBE shows that an extremely-biased user may still honestly message to convince the platform of listening to his review. Such Bayesian game-theoretic learning obviously outperforms the latest common schemes especially when there are multiple diversely-biased users to compete. For the challenging single-user case, we further propose a time-evolving mechanism with the platform's commitment inferences to ensure the biased user's truthful messaging all the time, whose performance improves with more time periods to learn from more historical data.
HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models

Recently, denoising diffusion models have demonstrated remarkable performance among generative models in various domains. However, in the speech domain, the application of diffusion models for synthesizing time-varying audio faces limitations in terms of complexity and controllability, as speech synthesis requires very high-dimensional samples with long-term acoustic features. To alleviate the challenges posed by model complexity in singing voice synthesis, we propose HiddenSinger, a high-quality singing voice synthesis system using a neural audio codec and latent diffusion models. To ensure high-fidelity audio, we introduce an audio autoencoder that can encode audio into an audio codec as a compressed representation and reconstruct the high-fidelity audio from the low-dimensional compressed latent vector. Subsequently, we use the latent diffusion models to sample a latent representation from a musical score. In addition, our proposed model is extended to an unsupervised singing voice learning framework, HiddenSinger-U, to train the model using an unlabeled singing voice dataset. Experimental results demonstrate that our model outperforms previous models in terms of audio quality. Furthermore, the HiddenSinger-U can synthesize high-quality singing voices of speakers trained solely on unlabeled data.
Period–Luminosity Relations for Galactic Classical Cepheids in the Sloan Bands

We present the first period–luminosity (PL) and period–Wesenheit (PW) relations in the Sloan–Pan-STARRS g P1 r P1 i P1 bands for classical fundamental-mode Cepheids in the Milky Way. We used a relatively modest number of 76 stars for the PL and 84−85 stars for the PW relations calibration. The data for the project were collected with the network of 40 cm telescopes of the Las Cumbres Observatory, and Gaia Data Release 3 parallaxes were used for the calculations. These gri-band PL and PW relations calibrations will be a useful tool for distance determinations in the era of large sky surveys using the Sloan photometric system, especially with the near-future start of the Large Synoptic Survey of Space and Time.
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach

Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data and improve downstream ORL performance, recent work has attempted to expand the dataset's coverage through data augmentation. However, most of these methods are tied to a specific policy (policy-dependent), where the generated data can only guarantee to support the current downstream ORL policy, limiting its usage scope on other downstream policies. Moreover, the quality of synthetic data is often not well-controlled, which limits the potential for further improving the downstream policy. To tackle these issues, we propose \textbf{HI}gh-quality \textbf{PO}licy-\textbf{DE}coupled~(HIPODE), a novel data augmentation method for ORL. On the one hand, HIPODE generates high-quality synthetic data by selecting states near the dataset distribution with potentially high value among candidate states using the negative sampling technique. On the other hand, HIPODE is policy-decoupled, thus can be used as a common plug-in method for any downstream ORL process. We conduct experiments on the widely studied TD3BC and CQL algorithms, and the results show that HIPODE outperforms the state-of-the-art policy-decoupled data augmentation method and most prevalent model-based ORL methods on D4RL benchmarks.
Tight conditions for when the NTK approximation is valid

We study when the neural tangent kernel (NTK) approximation is valid for training a model with the square loss. In the lazy training setting of Chizat et al. 2019, we show that rescaling the model by a factor of $\alpha = O(T)$ suffices for the NTK approximation to be valid until training time $T$. Our bound is tight and improves on the previous bound of Chizat et al. 2019, which required a larger rescaling factor of $\alpha = O(T^2)$.
Effective equidistribution of large dimensional measures on affine invariant submanifolds

The unstable foliation, that locally is given by changing horizontal components of period coordinates, plays an important role in study of translation surfaces, including their deformation theory and in the understanding of horocycle invariant measures. In this article we show that measures of large dimension on the unstable foliation equidistribute in affine invariant submanifolds and give an effective rate. An analogous result in the setting of homogeneous dynamics is crucially used in the effective density and equidistribution results of Lindenstrauss-Mohammadi and Lindenstrauss--Mohammadi--Wang.
Applied Regression Analysis of Correlations for Correlated Data

Correlated data are ubiquitous in today's data-driven society. While regression models for analyzing means and variances of responses of interest are relatively well-developed, the development of these models for analyzing the correlations is largely confined to longitudinal data, a special form of sequentially correlated data. This paper proposes a new method for the analysis of correlations to fully exploit the use of covariates for general correlated data. In a renewed analysis of the Classroom data, a highly unbalanced multilevel clustered data with within-class and within-school correlations, our method reveals informative insights on these structures not previously known. In another analysis of the malaria immune response data in Benin, a longitudinal study with time-dependent covariates where the exact times of the observations are not available, our approach again provides promising new results. At the heart of our approach is a new generalized z-transformation that converts correlation matrices constrained to be positive definite to vectors with unrestricted support, and is order-invariant. These two properties enable us to develop regression analysis incorporating covariates for the modelling of correlations via the use of maximum likelihood.
Implementation of Multiple-Step Quantized STDP based on Linear Weight Update Memristive Synapses

—Memristors have been widely studied as artificial synapses in neuromorphic circuits, due to their functional similarity with biological synapse, low operating power, and high integration density. In this work, a memristor bridge synapse for SNN with excellent linearity and soft-bound synaptic plasticity is designed and utilized for a neuron circuit implementing the robust spike-timing dependent plasticity (STDP) learning. This is the first of its kind demonstrating successful pulse width encoded multiple-step quantized STDP, with mixed-signal neuron possessing linear weight update. Physical models are employed to study the performance of proposed synapse and circuit, and simulations are carried out based on the MATLAB Simulink and Simscape. An improved memristor model which exhibits balanced flexibility, efficiency, convergence, and emulation performance, is developed though including the nonlinear Joule effect and weak signal effect. By using the improved memristor model in pattern learning, the influence of weak signal induced weight variation on circuit performance can be rigorously assessed. Moreover, the robustness and compatibility of the neuron circuit are greatly enhanced by employing the clock-based square-wave pulsed to process and program the synaptic weight. This proposed circuit could give an inspiration for combining the analog memristive synapse and leaky integrate-and-fire neuron with digital control units, prompting their development as edge computing devices
Error estimates for the highly efficient and energy stable schemes for the 2D/3D two-phase MHD

In this paper, we mainly focus on the rigorous convergence analysis for two fully decoupled, unconditional energy stable methods of the two-phase magnetohydrodynamics (MHD) model, which described in our previous work \cite{2022Highly}. The two methods consist of semi-implicit stabilization method/invariant energy quadratization (IEQ) method \cite{2019EfficientCHEN, Yang2016Linear, Yang2017Efficient, 2019EfficientYANG} for the phase field system, the pressure projection correction method for the saddle point MHD system, the exquisite implicit-explicit treatments for nonlinear coupled terms, which leads to only require solving a sequence of small elliptic equations at each time step. As far as we know, it's the first time to establish the optimal convergence analysis of fully decoupled and unconditional energy stable methods for multi-physics nonlinear two-phase MHD model. In addition, several numerical examples are showed to test the accuracy and stability of the presented methods.
Imbalanced Multi-label Classification for Business-related Text with Moderately Large Label Spaces

In this study, we compared the performance of four different methods for multi label text classification using a specific imbalanced business dataset. The four methods we evaluated were fine tuned BERT, Binary Relevance, Classifier Chains, and Label Powerset. The results show that fine tuned BERT outperforms the other three methods by a significant margin, achieving high values of accuracy, F1 Score, Precision, and Recall. Binary Relevance also performs well on this dataset, while Classifier Chains and Label Powerset demonstrate relatively poor performance. These findings highlight the effectiveness of fine tuned BERT for multi label text classification tasks, and suggest that it may be a useful tool for businesses seeking to analyze complex and multifaceted texts.
Einstein-type metrics and generalized Ricci solitons on weak $f$-K-contact manifolds

A weak metric $f$-structure $(f,Q,\xi_i,\eta^i,g)\ (i=1,\ldots,s)$, generalizes the metric $f$-structure on a smooth manifold, i.e., the complex structure on the contact distribution is replaced with a nonsingular skew-symmetric tensor. We study geometry of a weak $f$-K-contact structure, which is a weak $f$-contact structure, whose characteristic vector fields are Killing. We show that $\ker f$ of a weak $f$-contact manifold defines a $\mathfrak{g}$-foliation with an abelian Lie algebra. Then we characterize weak $f$-K-contact manifolds among all weak metric $f$-manifolds by the property known for $f$-K-contact manifolds, and find when a Riemannian manifold endowed with a set of orthonormal Killing vector fields is a weak $f$-K-contact manifold. We show that for $s>1$, an Einstein weak $f$-K-contact manifold is Ricci flat, then find sufficient conditions for a weak $f$-K-contact manifold with parallel Ricci tensor or with a generalized gradient Ricci soliton structure to be Ricci flat or a quasi Einstein manifold. We prove positive definiteness of the Jacobi operators in the characteristic directions and use this to deform a weak $f$-K-contact structure to an $f$-K-contact structure. We define an $\eta$-Ricci soliton and $\eta$-Einstein structures on a weak metric $f$-manifold (which for $s=1$, give the well-known structures on contact metric manifolds) and find sufficient conditions for a compact weak $f$-K-contact manifold with an $\eta$-Ricci soliton structure of constant scalar curvature to be $\eta$-Einstein.
Fast yet predictable braking manoeuvers for real-time robot control

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings.
Colouring random graphs: Tame colourings

Given a graph G, a colouring is an assignment of colours to the vertices of G so that no two adjacent vertices are coloured the same. If all colour classes have size at most t, then we call the colouring t-bounded, and the t-bounded chromatic number of G, denoted by $\chi_t(G)$, is the minimum number of colours in such a colouring. Every colouring of G is then $\alpha(G)$-bounded, where $\alpha(G)$ denotes the size of a largest independent set. We study colourings of the random graph G(n, 1/2) and of the corresponding uniform random graph G(n,m) with $m=\left \lfloor \frac 12 {n \choose 2} \right \rfloor$. We show that $\chi_t(G(n,m))$ is maximally concentrated on at most two explicit values for $t = \alpha(G(n,m))-2$. This behaviour stands in stark contrast to that of the normal chromatic number, which was recently shown not to be concentrated on any sequence of intervals of length $n^{1/2-o(1)}$. Moreover, when $t = \alpha(G_{n, 1/2})-1$ and if the expected number of independent sets of size $t$ is not too small, we determine an explicit interval of length $n^{0.99}$ that contains $\chi_t(G_{n,1/2})$ with high probability. Both results have profound consequences: the former is at the core of the intriguing Zigzag Conjecture on the distribution of $\chi(G_{n, 1/2})$ and justifies one of its main hypotheses, while the latter is an important ingredient in the proof of a non-concentration result for $\chi(G_{n,1/2})$ that is conjectured to be optimal. These two results are consequences of a more general statement. We consider a class of colourings that we call tame, and provide tight bounds for the probability of existence of such colourings via a delicate second moment argument. We then apply those bounds to the two aforementioned cases. As a further consequence of our main result, we prove two-point concentration of the equitable chromatic number of G(n,m).
Spectral gap-based deterministic tensor completion

Tensor completion is a core machine learning algorithm used in recommender systems and other domains with missing data. While the matrix case is well-understood, theoretical results for tensor problems are limited, particularly when the sampling patterns are deterministic. Here we bound the generalization error of the solutions of two tensor completion methods, Poisson loss and atomic norm minimization, providing tighter bounds in terms of the target tensor rank. If the ground-truth tensor is order t with CP-rank r, the dependence on r is improved from ${r^{2(t - 1)\left( {{t^2} - t - 1} \right)}}$ in [16] to ${r^{2(t - 1)(3t - 5)}}$. The error in our bounds is deterministically controlled by the spectral gap of the sampling sparsity pattern. We also prove several new properties for the atomic tensor norm, reducing the rank dependence from r3t–3 in [14] to r3t–5 under random sampling schemes. A limitation is that atomic norm minimization, while theoretically interesting, leads to inefficient algorithms. However, numerical experiments illustrate the dependence of the reconstruction error on the spectral gap for the practical max-quasinorm, ridge penalty, and Poisson loss minimization algorithms. This view through the spectral gap is a promising window for further study of tensor algorithms.
Transformer-based GAN for Terahertz Spatial-Temporal Channel Modeling and Generating

Terahertz (THz) communications are envisioned as a promising technology for 6G and beyond wireless systems, providing ultra-broad continuous bandwidth and thus Terabit-per-second (Tbps) data rates. However, as foundation of designing THz communications, channel modeling and characterization are fundamental to scrutinize the potential of the new spectrum. Relied on time-consuming and costly physical measurements, traditional statistical channel modeling methods suffer from the problem of low accuracy with the assumed certain distributions and empirical parameters. In this paper, a transformer-based generative adversarial network modeling method (T-GAN) is proposed in the THz band, which exploits the advantage of GAN in modeling the complex distribution, and the powerful expressive capability of transformer structure. Experimental results reveal that the distribution of channels generated by the proposed T-GAN method shows good agreement with the original channels in terms of the delay spread and angular spread. Moreover, T-GAN achieves good performance in modeling the power delay angular profile, with 2.18 dB root-mean-square error (RMSE).