Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines

In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data; (3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines.
Evolution of titanosaurid sauropods. I: phylogenetic analysis based on the postcranial evidence

The phylogenetic relationships of titanosaurid sauropods are examined by means of a clodistic parsimony analysis based on postcranel features. Eusauropoda tBarapasaurus tagorei + Neosauropoda) have, at least, two synapomorphies. The Camarasauromorpha (Camarasauridae + Titanosauriformes) have five synapomorphies. The Titanosauriformes [Brachiosaurus brancai + tChubunsaurus insignis + Titanosauria)] share six derived characters (e.g., presence of medial prespinallamina in posterior trunk vertebrae, neural arches positioned anteriorly in mid and posterior caudal centra, c1aw on manual digit 1reduced or absent). Chubutisaurus insignis Del Corro shares with Titanosauria one apomorphy. Titanosauria is rediagnosed based on five synapomorphies (e.g., eye-shape pleurocoels in trunk vertebrae, anterior caudals procoelous, pubis considerably longer than ischium). Titanosauridae comprises titanosaurids with: absence of hyposphene-hypantrum articulation in posterior trunk vertebrae, six sacral vertebrae, anterior caudals strongly procoelous having "ball and socket" articular faces, mid and posterior caudals strongly procoelous, semilunar sternal plates, claw on manual digit I absent, manual phalanges absent and preacetabular lobe of ilium nearly horizontal, outwardly projected. Titanosaurids more derived than Epachthosaurus sciuttoi Powell and Malawisaurus dixeyi Haughton have medial prespinal lamina formed down to the base of neural spine in posterior trunk vertebrae and quadrangular coracoids. Argentinosaurus huinculensis Bonaparte and Coria, Opisthocoelicaudia skarzynskii Borsuk-Bialynicka and Titanosauridae indet*(DGM "Serie B") form a monophyletic group characterized by presence of accesory spino-diapophyseallaminae in trunk vertebrae. Aeolosaurus Powell shares with Alamosaurus sanjuanensis Gilmore and the Saltasaurinae biconvex first caudal and presence of dorsal prominence on inner face of scapula. Alamosaurus sanjuanensis is the sister-taxon of the Saltasaurinae.
On Computationally Efficient Learning of Exponential Family Distributions

We consider the classical problem of learning, with arbitrary accuracy, the natural parameters of a $k$-parameter truncated \textit{minimal} exponential family from i.i. d. samples in a computationally and statistically efficient manner. We focus on the setting where the support as well as the natural parameters are appropriately bounded. While the traditional maximum likelihood estimator for this class of exponential family is consistent, asymptotically normal, and asymptotically efficient, evaluating it is computationally hard. In this work, we propose a novel loss function and a computationally efficient estimator that is consistent as well as asymptotically normal under mild conditions. We show that, at the population level, our method can be viewed as the maximum likelihood estimation of a re-parameterized distribution belonging to the same class of exponential family. Further, we show that our estimator can be interpreted as a solution to minimizing a particular Bregman score as well as an instance of minimizing the \textit{surrogate} likelihood. We also provide finite sample guarantees to achieve an error (in $\ell_2$-norm) of $\alpha$ in the parameter estimation with sample complexity $O({\sf poly}(k)/\alpha^2)$. Our method achives the order-optimal sample complexity of $O({\sf log}(k)/\alpha^2)$ when tailored for node-wise-sparse Markov random fields. Finally, we demonstrate the performance of our estimator via numerical experiments.
GLAD: Content-Aware Dynamic Graphs For Log Anomaly Detection

Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to detect anomalies in log sequences, they often overlook the significance of considering relations among system components, such as services and users, which can be identified from log contents. Understanding these relations is vital for detecting anomalies and their underlying causes. To address this issue, we introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect relational anomalies in system logs. GLAD incorporates log semantics, relational patterns, and sequential patterns into a unified framework for anomaly detection. Specifically, GLAD first introduces a field extraction module that utilizes prompt-based few-shot learning to identify essential fields from log contents. Then GLAD constructs dynamic log graphs for sliding windows by interconnecting extracted fields and log events parsed from the log parser. These graphs represent events and fields as nodes and their relations as edges. Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs. This model employs a Graph Neural Network (GNN)-based encoder enhanced with transformers to capture content, structural and temporal features. We evaluate our proposed method11Our code is available at https://github.com/yul091/GraphLogAD on three datasets, and the results demonstrate the effectiveness of GLAD in detecting anomalies indicated by varying relational patterns.
Exceptional point proximity-driven mode-locking in coupled microresonators

We show theoretically and numerically that mode-locking is feasible with a coupled-cavity system with gain and loss, notably, without any natural saturable absorber. We highlight that in the vicinity of the exceptional point, system $Q$ exhibits substantial modulation even with minor refractive index changes and a minimal Kerr effect contribution. Leveraging this unique behaviour, we propose an unprecedented approach wherein the lossy auxiliary cavity functions as an efficient artificial saturable absorber, thus facilitating mode-locking. This approach is not only novel, but also presents considerable advantages over conventional systems where both gain and saturable absorption are contained within a single microcavity. These benefits include reduced operational power and ease of post-adjustment, achievable through the manipulation of the coupling strength between the two microcavities.
Geometric semimetals

Topological semimetals, such as the Weyl and Dirac semimetals, represent one of the most active research fields in modern condensed matter physics. The peculiar physical properties of these systems mainly originate from their underlying symmetries, emergent relativistic dispersion, and band topology. In this paper, we present a novel class of $\textit{geometric semimetals}$ in three dimensions. These semimetals are protected by the generalized chiral and rotation symmetries, but are topologically trivial. Nevertheless, we show that their band geometry is nontrivial, as evidenced by the nonzero quantum metric trace with possible quantization. The possible realization in synthetic-matter experiments is also discussed.
Knowledge-based Refinement of Scientific Publication Knowledge Graphs

We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic logic model in the presence of human guidance (knowledge-based learning). Specifically, we learn relational regression trees using functional gradient boosting that outputs explainable rules. To incorporate human knowledge, advice in the form of first-order clauses is injected to refine the trees. We demonstrate the usefulness of human knowledge both quantitatively and qualitatively in seven authorship domains.
Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction

We present Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enables facial motion capture applications like virtual avatars. Our main contributions are: i) an annotation-free offline method for obtaining blendshape coefficients from real-world human scans, ii) a lightweight real-time model that predicts blendshape coefficients based on facial landmarks.
Computation of safe disturbance sets using implicit RPI sets

Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches as closely as possible the output constraint set, while being included in it. This problem finds application in several control design problems, such as the development of hierarchical control loops, decentralized control, supervisory control, robustness-verification, etc. We first characterize the set of disturbance sets satisfying the output constraint inclusion using corresponding minimal robust positive invariant (mRPI) sets, following which we formulate an optimization problem that minimizes the distance between the reachable output set and the output constraint set. We tackle the optimization problem using an implicit RPI set approach that provides a priori approximation error guarantees, and adopt a novel disturbance set parameterization that permits the encoding of the set of feasible disturbance sets as a polyhedron. Through extensive numerical examples, we demonstrate that the proposed approach computes disturbance sets with reduced conservativeness improved computational efficiency than state-of-the-art methods.
Lung Diseases Image Segmentation using Faster R-CNNs

Lung diseases are a leading cause of child mortality in the developing world, with India accounting for approximately half of global pneumonia deaths (370,000) in 2016. Timely diagnosis is crucial for reducing mortality rates. This paper introduces a low-density neural network structure to mitigate topological challenges in deep networks. The network incorporates parameters into a feature pyramid, enhancing data extraction and minimizing information loss. Soft Non-Maximal Suppression optimizes regional proposals generated by the Region Proposal Network. The study evaluates the model on chest X-ray images, computing a confusion matrix to determine accuracy, precision, sensitivity, and specificity. We analyze loss functions, highlighting their trends during training. The regional proposal loss and classification loss assess model performance during training and classification phases. This paper analysis lung disease detection and neural network structures.
Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle

It is difficult to perform 3D reconstruction from on-vehicle gathered video due to the large forward motion of the vehicle. Even object detection and human sensing models perform significantly worse on onboard videos when compared to standard benchmarks because objects often appear far away from the camera compared to the standard object detection benchmarks, image quality is often decreased by motion blur and occlusions occur often. This has led to the popularisation of traffic data-specific benchmarks. Recently Light Detection And Ranging (LiDAR) sensors have become popular to directly estimate depths without the need to perform 3D reconstructions. However, LiDAR-based methods still lack in articulated human detection at a distance when compared to image-based methods. We hypothesize that benchmarks targeted at articulated human sensing from LiDAR data could bring about increased research in human sensing and prediction in traffic and could lead to improved traffic safety for pedestrians.
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models

Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of the majority of recommender research -- and most practical recommenders of any import -- is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommenders could generate for its users. We argue that explicitly modeling the incentives and behaviors of all actors in the system -- and the interactions among them induced by the recommender's policy -- is strictly necessary if one is to maximize the value the system brings to these actors and improve overall ecosystem"health". Doing so requires: optimization over long horizons using techniques such as reinforcement learning; making inevitable tradeoffs in the utility that can be generated for different actors using the methods of social choice; reducing information asymmetry, while accounting for incentives and strategic behavior, using the tools of mechanism design; better modeling of both user and item-provider behaviors by incorporating notions from behavioral economics and psychology; and exploiting recent advances in generative and foundation models to make these mechanisms interpretable and actionable. We propose a conceptual framework that encompasses these elements, and articulate a number of research challenges that emerge at the intersection of these different disciplines.
Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration.

OBJECTIVE We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one EDR signal that is of higher quality. Methods: We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection. Results: The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (-score), optimal transport (OT) distance, and average frequency (AF) score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance. Conclusion: The sync-ensembled EDR provides robust respiratory information from electrocardiogram. Significance: Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.
Quantum algorithms for the simulation of perturbative QCD processes

Quantum computers are expected to give major speed-ups for the simulation of quantum systems. In these conference proceedings, we discuss quantum algorithms for the simulation of perturbative Quantum Chromodynamics (QCD) processes. In particular, we describe quantum circuits for simulating the colour part of the interactions of quarks and gluons. We implement our circuits on a simulated noiseless quantum computer and validate them by calculating colour factors for various examples of Feynman diagrams.
Saturn's Atmosphere in Northern Summer Revealed by JWST/MIRI

Saturn's northern summertime hemisphere was mapped by JWST/Mid‐Infrared Instrument (4.9–27.9 µm) in November 2022, tracing the seasonal evolution of temperatures, aerosols, and chemical species in the 5 years since the end of the Cassini mission. The spectral region between reflected sunlight and thermal emission (5.1–6.8 µm) is mapped for the first time, enabling retrievals of phosphine, ammonia, and water, alongside a system of two aerosol layers (an upper tropospheric haze p < 0.3 bars, and a deeper cloud layer at 1–2 bars). Ammonia displays substantial equatorial enrichment, suggesting similar dynamical processes to those found in Jupiter's equatorial zone. Saturn's North Polar Stratospheric Vortex has warmed since 2017, entrained by westward winds at p < 10 mbar, and exhibits localized enhancements in several hydrocarbons. The strongest latitudinal temperature gradients are co‐located with the peaks of the zonal winds, implying wind decay with altitude. Reflectivity contrasts at 5–6 µm compare favorably with albedo contrasts observed by Hubble, and several discrete vortices are observed. A warm equatorial stratospheric band in 2022 is not consistent with a 15‐year repeatability for the equatorial oscillation. A stacked system of windshear zones dominates Saturn's equatorial stratosphere, and implies a westward equatorial jet near 1–5 mbar at this epoch. Lower stratospheric temperatures, and local minima in the distributions of several hydrocarbons, imply low‐latitude upwelling and a reversal of Saturn's interhemispheric circulation since equinox. Latitudinal distributions of stratospheric ethylene, benzene, methyl, and carbon dioxide are presented for the first time, and we report the first detection of propane bands in the 8–11 µm region.
Hi Model, generating 'nice' instead of 'good' is not as bad as generating 'rice'! Towards Context and Semantic Infused Dialogue Generation Loss Function and Evaluation Metric

Over the past two decades, dialogue modeling has made significant strides, moving from simple rule-based responses to personalized and persuasive response generation. However, despite these advancements, the objective functions and evaluation metrics for dialogue generation have remained stagnant, i.e., cross-entropy and BLEU, respectively. These lexical-based metrics have the following key limitations: (a) word-to-word matching without semantic consideration: It assigns the same credit for failure to generate 'nice' and 'rice' for 'good'. (b) missing context attribute for evaluating the generated response: Even if a generated response is relevant to the ongoing dialogue context, it may still be penalized for not matching the gold utterance provided in the corpus. In this paper, we first investigate these limitations comprehensively and propose a new loss function called Semantic Infused Contextualized diaLogue (SemTextualLogue) loss function. Furthermore, we formulate a new evaluation metric called Dialuation, which incorporates both context relevance and semantic appropriateness while evaluating a generated response. We conducted experiments on two benchmark dialogue corpora, encompassing both task-oriented and open-domain scenarios. We found that the dialogue generation model trained with SemTextualLogue loss attained superior performance (in both quantitative and qualitative evaluation) compared to the traditional cross-entropy loss function across the datasets and evaluation metrics.
Unveiling the Potential of Large Language Models in Generating Semantic and Cross-Language Clones

Semantic and Cross-language code clone generation may be useful for code reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has potential in such clone generation as GPT is used for text generation. When developers copy/paste codes from Stack Overflow (SO) or within a system, there might be inconsistent changes leading to unexpected behaviours. Similarly, if someone possesses a code snippet in a particular programming language but seeks equivalent functionality in a different language, a semantic cross-language code clone generation approach could provide valuable assistance. In this study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3 model could help generate semantic and cross-language clone variants for a given fragment. We have comprised a diverse set of code fragments and assessed GPT-3's performance in generating code variants. Through extensive experimentation and analysis, where 9 judges spent 158 hours to validate, we investigate the model's ability to produce accurate and semantically correct variants. Our findings shed light on GPT-3's strengths in code generation, offering insights into the potential applications and challenges of using advanced language models in software development. Our quantitative analysis yields compelling results. In the realm of semantic clones, GPT-3 attains an impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot prompt engineering. Furthermore, the model shines in transcending linguistic confines, boasting an exceptional 91.25% accuracy in generating cross-language clones.
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation

Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model. In this paper, we explore a recent method called Rectified Flow, which, thus far, has only been applied to small datasets. The core of Rectified Flow lies in its \emph{reflow} procedure, which straightens the trajectories of probability flows, refines the coupling between noises and images, and facilitates the distillation process with student models. We propose a novel text-conditioned pipeline to turn Stable Diffusion (SD) into an ultra-fast one-step model, in which we find reflow plays a critical role in improving the assignment between noise and images. Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of $23.3$ on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin ($37.2$ $\rightarrow$ $23.3$ in FID). By utilizing an expanded network with 1.7B parameters, we further improve the FID to $22.4$. We call our one-step models \emph{InstaFlow}. On MS COCO 2014-30k, InstaFlow yields an FID of $13.1$ in just $0.09$ second, the best in $\leq 0.1$ second regime, outperforming the recent StyleGAN-T ($13.9$ in $0.1$ second). Notably, the training of InstaFlow only costs 199 A100 GPU days. Codes and pre-trained models are available at \url{github.com/gnobitab/InstaFlow}.