Miracle-dz/UniFAR-FTUs huggingface dataset:
Young Stellar Object Candidates in IC 417

IC 417 is in the Galactic plane, and likely part of the Aur OB2 association; it is ∼2 kpc away. Stock 8 is one of the densest cluster constituents; off of it to the east, there is a “nebulous stream” (NS) that is dramatic in the infrared (IR). We have assembled a list of literature-identified young stellar objects (YSOs), new candidate YSOs from the NS, and new candidate YSOs from IR excesses. We vetted this list via inspection of the images, spectral energy distributions (SEDs), and color–color/color–magnitude diagrams. We placed the 710 surviving YSOs and candidate YSOs in ranked bins, nearly two-thirds of which have more than 20 points defining their SEDs. The lowest-ranked bins include stars that are confused, or likely carbon stars. There are 503 in the higher-ranked bins; half are SED Class III, and ∼40% are SED Class II. Our results agree with the literature in that we find that the NS and Stock 8 are at about the same distance from Earth (as are the rest of the YSOs), and that the NS is the youngest region, with Stock 8 being a little older. We do not find any evidence for an age spread within the NS, consistent with the idea that the star formation trigger came from the north. We do not find that the other literature-identified clusters here are as young as either the NS or Stock 8; at best, they are older than Stock 8, and they may not all be legitimate clusters.
End-to-end Differentiable Clustering with Associative Memories

Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural networks defining a recursive dynamical system, which have been integrated with various deep learning architectures. We uncover a novel connection between the AM dynamics and the inherent discrete assignment necessary in clustering to propose a novel unconstrained continuous relaxation of the discrete clustering problem, enabling end-to-end differentiable clustering with AM, dubbed ClAM. Leveraging the pattern completion ability of AMs, we further develop a novel self-supervised clustering loss. Our evaluations on varied datasets demonstrate that ClAM benefits from the self-supervision, and significantly improves upon both the traditional Lloyd's k-means algorithm, and more recent continuous clustering relaxations (by upto 60% in terms of the Silhouette Coefficient).
Under-Counted Tensor Completion with Neural Incorporation of Attributes

Systematic under-counting effects are observed in data collected across many disciplines, e.g., epidemiology and ecology. Under-counted tensor completion (UC-TC) is well-motivated for many data analytics tasks, e.g., inferring the case numbers of infectious diseases at unobserved locations from under-counted case numbers in neighboring regions. However, existing methods for similar problems often lack supports in theory, making it hard to understand the underlying principles and conditions beyond empirical successes. In this work, a low-rank Poisson tensor model with an expressive unknown nonlinear side information extractor is proposed for under-counted multi-aspect data. A joint low-rank tensor completion and neural network learning algorithm is designed to recover the model. Moreover, the UC-TC formulation is supported by theoretical analysis showing that the fully counted entries of the tensor and each entry's under-counting probability can be provably recovered from partial observations -- under reasonable conditions. To our best knowledge, the result is the first to offer theoretical supports for under-counted multi-aspect data completion. Simulations and real-data experiments corroborate the theoretical claims.
Mathematics Is Physical

The world of mathematics is often considered abstract, with its symbols, concepts, and topics appearing unrelated to physical objects. However, it is important to recognize that the development of mathematics is fundamentally influenced by a basic fact: mathematicians and computers are physical objects subject to the laws of physics. Through an analysis of the Turing machine, it becomes evident that Turing and his contemporaries overlooked a physical possibility: information carriers can be quantum systems. As a result, computing models like the Turing machine can only process classical information, limiting their computing power. Gödel's incompleteness theorem highlights the basic fact that mathematicians and computers are made up of finite numbers of atoms and molecules. They can only start with a finite number of axioms, use a finite number of symbols and deduction rules, and arrive at theorems with a finite number of steps. While the number of proofs may be infinite after including all future mathematicians and computers, they must still be enumerable. In contrast, the number of mathematical statements is uncountable, meaning that there will always be mathematical statements that cannot be proved true or false. Just as Landauer claimed that information is physical, mathematics is also physical, limited or empowered by the physical entities that carries it out or embodies it.
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences

We study the problem of optimizing biological sequences, e.g., proteins, DNA, and RNA, to maximize a black-box score function that is only evaluated in an offline dataset. We propose a novel solution, bootstrapped training of score-conditioned generator (BootGen) algorithm. Our algorithm repeats a two-stage process. In the first stage, our algorithm trains the biological sequence generator with rank-based weights to enhance the accuracy of sequence generation based on high scores. The subsequent stage involves bootstrapping, which augments the training dataset with self-generated data labeled by a proxy score function. Our key idea is to align the score-based generation with a proxy score function, which distills the knowledge of the proxy score function to the generator. After training, we aggregate samples from multiple bootstrapped generators and proxies to produce a diverse design. Extensive experiments show that our method outperforms competitive baselines on biological sequential design tasks. We provide reproducible source code: \href{https://github.com/kaist-silab/bootgen}{https://github.com/kaist-silab/bootgen}.
CoSiNES: Contrastive Siamese Network for Entity Standardization

Entity standardization maps noisy mentions from free-form text to standard entities in a knowledge base. The unique challenge of this task relative to other entity-related tasks is the lack of surrounding context and numerous variations in the surface form of the mentions, especially when it comes to generalization across domains where labeled data is scarce. Previous research mostly focuses on developing models either heavily relying on context, or dedicated solely to a specific domain. In contrast, we propose CoSiNES, a generic and adaptable framework with Contrastive Siamese Network for Entity Standardization that effectively adapts a pretrained language model to capture the syntax and semantics of the entities in a new domain. We construct a new dataset in the technology domain, which contains 640 technical stack entities and 6,412 mentions collected from industrial content management systems. We demonstrate that CoSiNES yields higher accuracy and faster runtime than baselines derived from leading methods in this domain. CoSiNES also achieves competitive performance in four standard datasets from the chemistry, medicine, and biomedical domains, demonstrating its cross-domain applicability. Code and data is available at https://github.com/konveyor/tackle-container-advisor/tree/main/entity_standardizer/cosines
H$_{2}$-Mapping: Real-Time Dense Mapping Using Hierarchical Hybrid Representation

Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this letter, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption.
On Newton polytopes of Lagrangian augmentations

This note explores the use of Newton polytopes in the study of Lagrangian fillings of Legendrian submanifolds. In particular, we show that Newton polytopes associated to augmented values of Reeb chords can distinguish infinitely many distinct Lagrangian fillings, both for Legendrian links and higher dimensional Legendrian spheres. The computations we perform work in finite characteristic, which significantly simplifies arguments and also allows us to show that there exist Legendrian links with infinitely many nonorientable exact Lagrangian fillings.
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera

Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4% and a Cohen’s kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.
Better Write Amplification for Streaming Data Processing

Many current applications have to perform data processing in a streaming fashion. Doing so at a large scale requires a parallel system that must be equipped to handle straggling workers and different kinds of failures. YT is the main driver behind distributed systems at Yandex, home to its distributed file system, lock service, key-value storage, and internal MapReduce platform. We implement a new component of this system designed for performing streaming MapReduce operations, utilizing different core YT solutions to achieve fault-tolerance and exactly-once semantics while maintaining efficiency and low write amplification factors.
Multimode Lasing in Supercell Plasmonic Nanoparticle Arrays

Multicolour light sources can be used in applications such as lighting and multiplexing signals. In photonic and plasmonic systems, one way to achieve multicolour light is via multi-mode lasing. To achieve this, plasmonic nanoparticle arrays are typically arranged in superlattices that lead to multiple dispersions of the single arrays coupled via the superlattice Bragg modes. Here, we show an alternative way to enable multi-mode lasing in plasmonic nanoparticle arrays. We design a supercell in a square lattice by leaving part of the lattice sites empty. This results in multiple dispersive branches caused by the supercell period and hence creates additional band edges that can support lasing. We experimentally demonstrate multi-mode lasing in such a supercell array. Furthermore, we identify the lasing modes by calculating the dispersion by combining the structure factor of the array design with an empty lattice approximation. We conclude that the lasing modes are the 74th $\Gamma$- and 106th $X$-point of the supercell. By tuning the square lattice period with respect to the gain emission we can control the modes that lase. Finally, we show that the lasing modes exhibit a combination of transverse electric and transverse magnetic mode characteristics in polarization resolved measurements.
An open patch generator based fingerprint presentation attack detection using generative adversarial network

The low-cost, user-friendly, and convenient nature of Automatic Fingerprint Recognition Systems (AFRS) makes them suitable for a wide range of applications. This spreading use of AFRS also makes them vulnerable to various security threats. Presentation Attack (PA) or spoofing is one of the threats which is caused by presenting a spoof of a genuine fingerprint to the sensor of AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure intended to protect AFRS against fake or spoof fingerprints created using various fabrication materials. In this paper, we have proposed a Convolutional Neural Network (CNN) based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG). This OPG is capable of generating realistic fingerprint samples which have no resemblance to the existing spoof fingerprint samples generated with other materials. The augmented dataset is fed to the DenseNet classifier which helps in increasing the performance of the Presentation Attack Detection (PAD) module for the various real-world attacks possible with unknown spoof materials. Experimental evaluations of the proposed approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and 2019 competition databases. An overall accuracy of 96.20%, 94.97%, and 92.90% has been achieved on the LivDet 2015, 2017, and 2019 databases, respectively under the LivDet protocol scenarios. The performance of the proposed PAD model is also validated in the cross-material and cross-sensor attack paradigm which further exhibits its capability to be used under real-world attack scenarios.
FAMO: Fast Adaptive Multitask Optimization

One of the grand enduring goals of AI is to create generalist agents that can learn multiple different tasks from diverse data via multitask learning (MTL). However, in practice, applying gradient descent (GD) on the average loss across all tasks may yield poor multitask performance due to severe under-optimization of certain tasks. Previous approaches that manipulate task gradients for a more balanced loss decrease require storing and computing all task gradients ($\mathcal{O}(k)$ space and time where $k$ is the number of tasks), limiting their use in large-scale scenarios. In this work, we introduce Fast Adaptive Multitask Optimization FAMO, a dynamic weighting method that decreases task losses in a balanced way using $\mathcal{O}(1)$ space and time. We conduct an extensive set of experiments covering multi-task supervised and reinforcement learning problems. Our results indicate that FAMO achieves comparable or superior performance to state-of-the-art gradient manipulation techniques while offering significant improvements in space and computational efficiency. Code is available at \url{https://github.com/Cranial-XIX/FAMO}.
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve [Formula: see text] AUC, [Formula: see text] accuracy, [Formula: see text] sensitivity, [Formula: see text] specificity, and [Formula: see text] F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.
CURVATURE PROPERTIES ON α -COSYMPLETIC MANIFOLDS WITH ∗ - η -RICCI-YAMABE SOLITONS

In this research article, we study ∗ - η -Ricci-Yamabe solitons on an α -Cosympletic manifold and show that it is η -Einstein manifold. In addition, we investigate that an α -Cosympletic manifold admitting ∗ - η -Ricci-Yamabe solitons satisﬁes some conditions. Lastly, we discuss the concircular, conformal, conharmonic, and W 2 -curvatures on the said manifold admitting ∗ - η -Ricci-Yamabe solitons.
Forward-Reflected-Backward and Shadow-Douglas--Rachford with partial inverse for Solving Monotone Inclusions

In this article, we study two methods for solving monotone inclusions in real Hilbert spaces involving the sum of a maximally monotone operator, a monotone-Lipschitzian operator, a cocoercive operator, and a normal cone to a vector subspace. Our algorithms split and exploits the intrinsic properties of each operator involved in the inclusion. We derive our methods by combining partial inverse techniques with the forward-reflected-backward algorithm and with the shadow-Douglas--Rachford algorithm, respectively. Our methods inherit the advantages of those methods, requiring only one activation of the Lipschitzian operator, one activation of the cocoercive operator, two projections onto the closed vector subspace, and one calculation of the resolvent of the maximally monotone operator. Furthermore, we develop methods for solving primal-dual inclusions involving a mixtureof sums, linear compositions, parallel sums, Lipschitzian operators, cocoercive operators, and normal cones. We apply our methods to constrained composite convex optimization problems as a specific example. Finally, in order to compare our methods with existing methods in the literature, we provide numerical experiments on constrained total variation least-squares optimization problems. We obtain promising numerical results.
TwistList: Resources and Baselines for Tongue Twister Generation

Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry. In this paper, we present work on the generation of tongue twisters - a form of language that is required to be phonetically conditioned to maximise sound overlap, whilst maintaining semantic consistency with an input topic, and still being grammatically correct. We present TwistList, a large annotated dataset of tongue twisters, consisting of 2.1K+ human-authored examples. We additionally present several benchmark systems (referred to as TwisterMisters) for the proposed task of tongue twister generation, including models that both do and do not require training on in-domain data. We present the results of automatic and human evaluation to demonstrate the performance ofexisting mainstream pre-trained models in this task with limited (or no) task specific training and data, and no explicit phonetic knowledge. We find that the task of tongue twister generation is challenging for models under these conditions, yet some models are still capable of generating acceptable examples of this language type.
Estimation with Valid and Invalid Instruments

We demonstrate analytically that for the widely used simultaneous equation model with one jointly endogenous variable and valid instruments, 2SLS has smaller MSE error, up to second order, than OLS unless the R², or the F statistic of the reduced form equation is extremely low. We also consider the relative bias of estimators when the instruments are invalid, i.e. the instruments are correlated with the stochastic disturbance. Here, both 2SLS and OLS are biased in finite samples and inconsistent. We investigate conditions under which the approximate finite sample bias or the MSE of 2SLS is smaller than the corresponding statistics for the OLS estimator. We again find that 2SLS does better than OLS under a wide range of conditions, which we characterize as functions of observable statistics and one unobservable statistic.
Determining soil water content of salt-affected soil using far-infrared spectra: laboratory experiment

Abstract. Rapid determination of soil water content is urgently needed for monitoring and modeling ecosystem processes and improving agricultural practices, especially in arid landscapes. Far-infrared band application in soil water measurement is still limited. Various samples were arranged to simulate complex field condition and emissivity was obtained from a Fourier transform infrared spectrometer. Four spectral forms (including raw spectra, logarithm of reciprocal spectra, first-order derivate, and second-order derivate) were employed to develop a partial least squares regression model. The results indicate that the model with first-order derivate spectral form was identified with the highest performance (R2=0.87 and root mean square error=1.88%) at the range of 8.309 to 10.771 μm. Judging from the contribution of the bands to each principal component, the band region from 8.27 to 9.112 μm holds a great promise for soil water content estimation. Several channels of ASTER and MODIS correspond to the involved band domain, which show the potential of predicting and mapping soil water content on large scales. However, there are still constraints due to the differences in spectral resolution between instrument and sensors and the influence of complex factors under field conditions, which are still challenges for forthcoming studies.