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Based on the Gaia Data Release 3 and APOGEE datasets, we investigate the kinematic differences between young stellar objects (YSOs) and their parent clouds in five nearby star-forming regions. Overall, the 1D velocity differences between Class II YSOs and their parent molecular cloud range from [0, 1.4] km/s. In feedback environments dominated by outflows, massive stars, and supernova feedback, the corresponding velocity differences range from [0, 1.4] km/s, [0.1, 0.4] km/s, and [0.1, 1] km/s, respectively. These results indicate that YSO kinematics are not significantly affected by these different types of feedback environment. Additionally, compared to the Class II YSOs, Class III YSOs have slightly larger velocities and dispersions., Comment: 19 pages, 17 figures, accepted by ApJS

Large language models (LLMs) have exhibited impressive zero-shot performance on inference tasks. However, LLMs may suffer from spurious correlations between input texts and output labels, which limits LLMs' ability to reason based purely on general language understanding. In other words, LLMs may make predictions primarily based on premise or hypothesis, rather than both components. To address this problem that may lead to unexpected performance degradation, we propose task calibration (TC), a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation. TC encourages LLMs to reason based on both premise and hypothesis, while mitigating the models' over-reliance on individual premise or hypothesis for inference. Experimental results show that TC achieves a substantial improvement on 13 inference tasks in the zero-shot setup. We further validate the effectiveness of TC in few-shot setups and various natural language understanding tasks. Further analysis indicates that TC is also robust to prompt templates and has the potential to be integrated with other calibration methods.

We calibrate the period--luminosity--metallicity (PLZ) relations of classical Cepheid (DCEP) in three near-infrared bands ($J$, $H$, and $K_S$) and four mid-infrared bands ($W1$, $W2$, $[3.6]$, and $[4.5]$). The PLZ relations of $W1$ and $W2$ bands are calibrated for the first time. The distance moduli of the Large Magellanic Cloud estimated by these PLZ relations are in good agreement with the most accurate published value measured by geometric methods. These seven homogenous PLZ relations can aid in obtaining more robust distances for DCEPs. Applying our PLZ relations to trace the metallicity gradient of the Galactic disc, we find that the gradient for sources with logarithmic age less than 7.67 is almost fixed: $-0.056 \,\pm\, 0.002 \,\textrm{dex}\, \textrm{kpc}^{-1}$; the gradient for sources with logarithmic age greater than 7.67 is period--dependent (i.e., age-dependent): $(-0.074 \,\pm\, 0.003)+(-0.022 \,\pm\, 0.002)\log P)\,\textrm{dex}\, \textrm{kpc}^{-1}$. In addition, we find that DCEPs in the $R_{GC} \gtrsim 14.5\,\textrm{kpc}$ region tend to migrate toward the Galactic center, while DCEPs in the $10.5\,\textrm{kpc} \lesssim R_{GC} \lesssim 14.5\,\textrm{kpc}$ region tend to migrate toward the anti-Galactic center, which may be the reason for the obvious break of the metallicity gradient at $R_{GC} \thickapprox 14.5\,\textrm{kpc}$. We conclude that the evolution of the metallicity gradient of DCEPs may be related to their radial migration., Comment: 12 pages, 3 figures, 4 tables, submitted to ApJ

Morphology is the starting point for understanding galaxies. Elmegreen et al. classified spiral galaxies into flocculent, multiple-arm, and grand-design galaxies based on the regularity of their spiral arm structure. With the release of a vast number of clear spiral galaxy images from the Sloan Digital Sky Survey, we conducted a morphological classification of 5093 blue spiral galaxies. A statistical analysis of this sample shows that the fractions of flocculent, multiple-arm, and grand-design galaxies are 38 $\pm$ 1%, 59 $\pm$ 1%, and 3 $\pm$ 1%, respectively. Redshift has no obvious influence on this classification. However, as the bulge size becomes larger, the fraction of multiple-arm galaxies increases, while that of flocculent galaxies decreases. In addition, we performed a statistical analysis of 3958 galaxies with a clear spiral arm structure, finding 82% of these galaxies have two arms in their inner regions. We also found that the majority (74%) of the barred spiral galaxies exhibit the characteristics of two inner spiral arms and multiple outer spiral arms, and there is no barred spiral galaxy in this work with four continuous spiral arms from the inner to the outer regions. These results highlight that the spiral arm structure of the Milky Way, according to the current mainstream view of a four-arm galaxy with continuous arms extending from the inner to outer regions, is quite unique. However, our findings align with the spiral morphology of the Milky Way proposed by Xu et al., in which case our Galaxy can be considered typical., Comment: 15 pages, 9 figures, 3 tables, accepted for publication in AJ

In many industrial settings, users wish to ask questions whose answers may be found in structured data sources such as a spreadsheets, databases, APIs, or combinations thereof. Often, the user doesn't know how to identify or access the right data source. This problem is compounded even further if multiple (and potentially siloed) data sources must be assembled to derive the answer. Recently, various Text-to-SQL applications that leverage Large Language Models (LLMs) have addressed some of these problems by enabling users to ask questions in natural language. However, these applications remain impractical in realistic industrial settings because they fail to cope with the data source heterogeneity that typifies such environments. In this paper, we address heterogeneity by introducing the siwarex platform, which enables seamless natural language access to both databases and APIs. To demonstrate the effectiveness of siwarex, we extend the popular Spider dataset and benchmark by replacing some of its tables by data retrieval APIs. We find that siwarex does a good job of coping with data source heterogeneity. Our modified Spider benchmark will soon be available to the research community

As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.

The Canis Major (CMa) region is known for its prominent arc-shaped morphology, visible at multiple wavelengths. This study integrates molecular gas data with high-precision astrometric parameters of young stellar objects (YSOs) from Gaia DR3 to provide the first three-dimensional (3D) insights into the dynamical evolution and star formation history of the CMa region. By utilizing the average distances and proper motions of the YSOs as proxies for those of the molecular clouds (MCs), we confirm the presence of a slowly expanding shell-like morphology in the CMa region, with the estimated radius of 47$\pm$11 pc and expansion velocity of 1.6$\pm$0.7 km/s. Further, the dynamical evolution of the shell supports its expansion, with an expansion timescale of $\sim$4.4 Myr obtained by the traceback analysis assuming constant velocities. Finally, a momentum estimate suggests that at least 2 supernova explosions (SNe) are needed to power the observed expanding shell, reinforcing the previous hypothesis of multiple SNe events. This study effectively combines the CO data with the astrometric data of YSOs from Gaia, offering significant support for the future studies on the 3D morphology and kinematics of MCs., Comment: 19 pages, 10 figures. Accepted for publication in AJ

As a key component in boosting online user growth, uplift modeling aims to measure individual user responses (e.g., whether to play the game) to various treatments, such as gaming bonuses, thereby enhancing business outcomes. However, previous research typically considers a single-task, single-treatment setting, where only one treatment exists and the overall treatment effect is measured by a single type of user response. In this paper, we propose a Multi-Treatment Multi-Task (MTMT) uplift network to estimate treatment effects in a multi-task scenario. We identify the multi-treatment problem as a causal inference problem with a tiered response, comprising a base effect (from offering a treatment) and an incremental effect (from offering a specific type of treatment), where the base effect can be numerically much larger than the incremental effect. Specifically, MTMT separately encodes user features and treatments. The user feature encoder uses a multi-gate mixture of experts (MMOE) network to encode relevant user features, explicitly learning inter-task relations. The resultant embeddings are used to measure natural responses per task. Furthermore, we introduce a treatment-user feature interaction module to model correlations between each treatment and user feature. Consequently, we separately measure the base and incremental treatment effect for each task based on the produced treatment-aware representations. Experimental results based on an offline public dataset and an online proprietary dataset demonstrate the effectiveness of MTMT in single/multi-treatment and single/multi-task settings. Additionally, MTMT has been deployed in our gaming platform to improve user experience.

Technology mapping involves mapping logical circuits to a library of cells. Traditionally, the full technology library is used, leading to a large search space and potential overhead. Motivated by randomly sampled technology mapping case studies, we propose MapTune framework that addresses this challenge by utilizing reinforcement learning to make design-specific choices during cell selection. By learning from the environment, MapTune refines the cell selection process, resulting in a reduced search space and potentially improved mapping quality. The effectiveness of MapTune is evaluated on a wide range of benchmarks, different technology libraries and technology mappers. The experimental results demonstrate that MapTune achieves higher mapping accuracy and reducing delay/area across diverse circuit designs, technology libraries and mappers. The paper also discusses the Pareto-Optimal exploration and confirms the perpetual delay-area trade-off. Conducted on benchmark suites ISCAS 85/89, ITC/ISCAS 99, VTR8.0 and EPFL benchmarks, the post-technology mapping and post-sizing quality-of-results (QoR) have been significantly improved, with average Area-Delay Product (ADP) improvement of 22.54\% among all different exploration settings in MapTune. The improvements are consistently remained for four different technologies (7nm, 45nm, 130nm, and 180 nm) and two different mappers., Comment: IEEE/ACM International Conference on Computer-Aided Design (ICCAD '24), October 27--31, 2024

It is beneficial to calibrate the period Wesenheit metallicity relation (PWZR) of Delta Cephei stars (DCEPs), i.e., classical Cepheids, using accurate parallaxes of associated open clusters (OCs) from Gaia data release 3 (DR3). To this aim, we obtain a total of 43 OC-DCEPs (including 33 fundamental mode, 9 first overtone mode, and 1 multimode DCEPs.) and calibrate the PWZR as $W_G=(-3.356 \,\pm\, 0.033) \,(\log{P-1})+(-5.947 \,\pm\, 0.025)+(-0.285 \,\pm\, 0.064)[\textrm{Fe/H}]$. The concurrently obtained residual parallax offset in OC, $zp = -4\pm5\,\mu\textrm{as}$, demonstrate the adequacy of the parallax corrections within the magnitude range of OC member stars. By comparing the field DCEPs' DR3 parallaxes with their photometric parallaxes derived by our PWZR, we estimated the residual parallax offset in field DCEPs as $zp = -15\pm3\,\mu\textrm{as}$. Using our PWZR, we estimate the distance modulus of the Large Magellanic Cloud to be $18.482 \,\pm\, 0.040$ mag, which aligns well with the most accurate published value obtained through geometric methods.

The multiplayer online battle arena (MOBA) genre has gained significant popularity and economic success, attracting considerable research interest within the Human-Computer Interaction community. Enhancing the gaming experience requires a deep understanding of player behavior, and a crucial aspect of MOBA games is matchmaking, which aims to assemble teams of comparable skill levels. However, existing matchmaking systems often neglect important factors such as players' position preferences and team assignment, resulting in imbalanced matches and reduced player satisfaction. To address these limitations, this paper proposes a novel framework called CUPID, which introduces a novel process called ``re-matchmaking'' to optimize team and position assignments to improve both fairness and player satisfaction. CUPID incorporates a pre-filtering step to ensure a minimum level of matchmaking quality, followed by a pre-match win-rate prediction model that evaluates the fairness of potential assignments. By simultaneously considering players' position satisfaction and game fairness, CUPID aims to provide an enhanced matchmaking experience. Extensive experiments were conducted on two large-scale, real-world MOBA datasets to validate the effectiveness of CUPID. The results surpass all existing state-of-the-art baselines, with an average relative improvement of 7.18% in terms of win prediction accuracy. Furthermore, CUPID has been successfully deployed in a popular online mobile MOBA game. The deployment resulted in significant improvements in match fairness and player satisfaction, as evidenced by critical Human-Computer Interaction (HCI) metrics covering usability, accessibility, and engagement, observed through A/B testing. To the best of our knowledge, CUPID is the first re-matchmaking system designed specifically for large-scale MOBA games., Comment: 38 pages, accepted by CSCW 24

Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of ReLU-based or quadratic objectives. Noting that an MIS in a graph is a Maximum Clique (MC) in its complement, we propose a new quadratic formulation for MIS by incorporating an MC term, improving convergence and exploration. We show that every maximal independent set corresponds to a local minimizer, derive conditions for the MIS size, and characterize stationary points. To solve our non-convex objective, we propose solving parallel multiple initializations using momentum-based gradient descent, complemented by an efficient MIS checking criterion derived from our theory. Therefore, we dub our method as parallelized Clique-Informed Quadratic Optimization for MIS (pCQO-MIS). Our experimental results demonstrate the effectiveness of the proposed method compared to exact, heuristic, sampling, and data-centric approaches. Notably, our method avoids the out-of-distribution tuning and reliance on (un)labeled data required by data-centric methods, while achieving superior MIS sizes and competitive runtime relative to their inference time. Additionally, a key advantage of pCQO-MIS is that, unlike exact and heuristic solvers, the runtime scales only with the number of nodes in the graph, not the number of edges.

This paper addresses the complex issue of resource-constrained scheduling, an NP-hard problem that spans critical areas including chip design and high-performance computing. Traditional scheduling methods often stumble over scalability and applicability challenges. We propose a novel approach using a differentiable combinatorial scheduling framework, utilizing Gumbel-Softmax differentiable sampling technique. This new technical allows for a fully differentiable formulation of linear programming (LP) based scheduling, extending its application to a broader range of LP formulations. To encode inequality constraints for scheduling tasks, we introduce \textit{constrained Gumbel Trick}, which adeptly encodes arbitrary inequality constraints. Consequently, our method facilitates an efficient and scalable scheduling via gradient descent without the need for training data. Comparative evaluations on both synthetic and real-world benchmarks highlight our capability to significantly improve the optimization efficiency of scheduling, surpassing state-of-the-art solutions offered by commercial and open-source solvers such as CPLEX, Gurobi, and CP-SAT in the majority of the designs., Comment: 13 pages; International Conference on Machine Learning (ICML'24)

The next generation of very long baseline interferometry (VLBI) is stepping into the era of microarcsecond ($\mu$as) astronomy, and pushing astronomy, especially astrometry, to new heights. VLBI with the Square Kilometre Array (SKA), SKA-VLBI, will increase current sensitivity by an order of magnitude, and reach astrometric precision routinely below 10 $\mu$as, even challenging 1 $\mu$as. This advancement allows precise parallax and proper motion measurements of various celestial objects. Such improvements can be used to study objects (including isolated objects, and binary or multiple systems) in different stellar stages (such as star formation, main-sequence stars, asymptotic giant branch stars, pulsars, black holes, white dwarfs, etc.), unveil the structure and evolution of complex systems (such as the Milky Way), benchmark the international celestial reference frame, and reveal cosmic expansion. Furthermore, the theory of general relativity can also be tested with SKA-VLBI using precise measurements of light deflection under the gravitational fields of different solar system objects and the perihelion precession of solar system objects., Comment: 41 pages, 12 figures, 4 tables. Accepted to RAA (Review)

Based on Pre-trained Language Models (PLMs), event coreference resolution (ECR) systems have demonstrated outstanding performance in clustering coreferential events across documents. However, the existing system exhibits an excessive reliance on the `triggers lexical matching' spurious pattern in the input mention pair text. We formalize the decision-making process of the baseline ECR system using a Structural Causal Model (SCM), aiming to identify spurious and causal associations (i.e., rationales) within the ECR task. Leveraging the debiasing capability of counterfactual data augmentation, we develop a rationale-centric counterfactual data augmentation method with LLM-in-the-loop. This method is specialized for pairwise input in the ECR system, where we conduct direct interventions on triggers and context to mitigate the spurious association while emphasizing the causation. Our approach achieves state-of-the-art performance on three popular cross-document ECR benchmarks and demonstrates robustness in out-of-domain scenarios., Comment: Accepted to NAACL-24 Main

Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language. As these models become more integral to research and applications, conducting comprehensive evaluations of their capabilities has grown increasingly important. However, most existing benchmarks fail to consider that, in certain situations, images need to be interpreted within a broader context. In this work, we introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. Further analysis confirms that these models struggle to effectively extract and utilize contextual information to improve their understanding of images. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner. View our project website at https://thunlp-mt.github.io/CODIS.

We identify a molecular bubble, and study the star formation and its feedback in the S Mon region, using multiple molecular lines, young stellar objects (YSOs), and infrared data. We revisit the distance to S Mon, ~722+/-9 pc, using Gaia Data Release 3 parallaxes of the associated Class II YSOs. The bubble may be mainly driven by a massive binary system (namely 15 Mon), the primary of which is an O7V-type star. An outflow is detected in the shell of the bubble, suggesting ongoing star formation activities in the vicinity of the bubble. The total wind energy of the massive binary star is three orders of magnitude higher than the sum of the observed turbulent energy in the molecular gas and the kinetic energy of the bubble, indicating that stellar winds help to maintain the turbulence in the S Mon region and drive the bubble. We conclude that the stellar winds of massive stars have an impact on their surrounding environment., Comment: 34 pages,19 figures, 5 tables, Accepted for publication in ApJ

Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow. Existing methods struggle to fully exploit optimization opportunities, and often suffer from an explosive search space and limited scalability efficiency. This work presents BoolGebra, a novel attributed graph-learning approach for Boolean algebraic manipulation that aims to improve fundamental logic synthesis. BoolGebra incorporates Graph Neural Networks (GNNs) and takes initial feature embeddings from both structural and functional information as inputs. A fully connected neural network is employed as the predictor for direct optimization result predictions, significantly reducing the search space and efficiently locating the optimization space. The experiments involve training the BoolGebra model w.r.t design-specific and cross-design inferences using the trained model, where BoolGebra demonstrates generalizability for cross-design inference and its potential to scale from small, simple training datasets to large, complex inference datasets. Finally, BoolGebra is integrated with existing synthesis tool ABC to perform end-to-end logic minimization evaluation w.r.t SOTA baselines., Comment: DATE 2024 extended version. arXiv admin note: text overlap with arXiv:2310.07846

The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems. In recent times, machine learning (ML) methodologies have emerged as potent instruments for assessing design quality-of-results at the Register-Transfer Level (RTL) or Boolean level, aiming to expedite design exploration of advanced RTL configurations. In this presentation, we introduce an innovative open-source framework that translates RTL designs into graph representation foundations, which can be seamlessly integrated with the PyTorch Geometric graph learning platform. Furthermore, the Verilog-to-PyG (V2PYG) framework is compatible with the open-source Electronic Design Automation (EDA) toolchain OpenROAD, facilitating the collection of labeled datasets in an utterly open-source manner. Additionally, we will present novel RTL data augmentation methods (incorporated in our framework) that enable functional equivalent design augmentation for the construction of an extensive graph-based RTL design database. Lastly, we will showcase several using cases of V2PYG with detailed scripting examples. V2PYG can be found at \url{https://yu-maryland.github.io/Verilog-to-PyG/}., Comment: 8 pages, International Conference on Computer-Aided Design (ICCAD'23)

The key methodologies of modern logic synthesis techniques are conducted on multi-level technology-independent representations such as And-Inverter-Graphs (AIGs) of the digital logic via directed-acyclic-graph (DAGs) traversal based structural rewriting, resubstitution, and refactoring. Existing state-of-the-art DAG-aware logic synthesis algorithms are all designed to perform stand-alone optimizations during a single DAG traversal. However, we empirically identify and demonstrate that these algorithms are limited in quality-of-results and runtime complexity due to this design concept. This work proposes Synthesis Orchestration, which orchestrates stand-alone operations within the single traversal of AIG. Thus, orchestration method explores more optimization opportunities and results in better performance. Our experimental results are comprehensively conducted on all 104 designs collected from ISCAS'85/89/99, VTR, and EPFL benchmark suites, with consistent logic minimization improvements over rewriting, resubstitution, refactoring, leading to an average of 4% more node reduction with improved runtime efficiency for the single optimization. Moreover, we evaluate orchestration as a plug-in algorithm in resyn and resyn3 flows in ABC, which demonstrates consistent logic minimization improvements (3.8% and 10.9% more node reduction on average). The runtime analysis demonstrates the orchestration outperforms stand-alone algorithms in both AIG minimization and runtime efficiency. Finally, we integrate the orchestration into OpenROAD for end-to-end performance evaluation. Our results demonstrate the advantages of the orchestration optimization technique, even after technology mapping and post-routing in the design flow have been conducted.

Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks. Most active learning techniques in classification rely on the model's uncertainty or disagreement to choose unlabeled data, suffering from the problem of over-confidence in superficial patterns and a lack of exploration. Inspired by the cognitive processes in which humans deduce and predict through causal information, we take an initial attempt towards integrating rationales into AL and propose a novel Explainable Active Learning framework (XAL) for low-resource text classification, which aims to encourage classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Specifically, besides using a pre-trained bi-directional encoder for classification, we employ a pre-trained uni-directional decoder to generate and score the explanation. We further facilitate the alignment of the model with human reasoning preference through a proposed ranking loss. During the selection of unlabeled data, the predicted uncertainty of the encoder and the explanation score of the decoder complement each other as the final metric to acquire informative data. Extensive experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines. Analysis indicates that the proposed method can generate corresponding explanations for its predictions., Comment: Accepted by NAACL 2024

Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. Previous research has demonstrated that contextual information is crucial for developing an effective ASE model. However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences. To tackle this challenge, we propose an Efficient Context-aware ASE model (ECASE) that fully exploits contextual information by enhancing modeling capacity and augmenting training data. Specifically, we introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Additionally, we augment the training data by randomly masking discourse markers and sentences, which reduces the model's reliance on specific words or less informative sentences. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance. Furthermore, ablation studies confirm the effectiveness of each module in our model., Comment: EMNLP 2023

Using young open clusters and O--B2-type stars in~\emph{Gaia}~DR3, we investigate the kinematics of the local spiral structure. In general, the young sources in the outer spiral arms may present larger peculiar motions than those in the inner spiral arms. The young open clusters appear to have smaller peculiar motions than the O--B2-type stars, and the sources in both the Perseus and Local Arms may show an inward motion toward the Galactic center and rotate slower than Galactic rotation. Meanwhile, the sources in the Carina Arm may move in the opposite direction from the Sun to the Galactic center and rotate marginally faster than Galactic rotation. In addition, using young open clusters and O--B2-type stars, we have improved the distance estimations of kinematic methods for several regions near the Sun., Comment: 13 pages, 10 figures, Accepted for publication in The Astrophysical Journal Supplement Series

We present the results of an unbiased $^{12}$CO/$^{13}$CO/C$^{18}$O ($J$ = 1-0) survey in a portion of the third Galactic quadrant (TGQ): $l$ = [219.75, 229.75]$^\circ$ and $b$ = [-5.25, 5.25]$^\circ$. The high-resolution and high-sensitivity data sets help to unravel the distributions and physical properties of the molecular clouds (MCs) in the mapped area. In the LSR velocity range from -1 to 85 km/s, the molecular material successfully traces the Local, Perseus, and Outer arms. In the TGQ, the Outer arm appears to be more prominent than that in the second Galactic quadrant (SGQ), but the Perseus arm is not as conspicuous as that in the SGQ. A total of 1,502 $^{12}$CO, 570 $^{13}$CO, and 53 C$^{18}$O molecular structures are identified, spanning over $\sim2$ and $\sim6$ orders of magnitude in size and mass, respectively. Tight mass-radius correlations and virial parameter-mass anticorrelations are observable. Yet, it seems that no clear correlations between velocity dispersion and effective radius can be found over the full dynamic range. The vertical distribution of the MCs renders evident pictures of the Galactic warp and flare., Comment: 22 pages, 13 figures, 7 tables (with machine-readable versions), published in ApJS

To lower the barrier to diffractive optical neural networks (DONNs) design, exploration, and deployment, we propose LightRidge, the first end-to-end optical ML compilation framework, which consists of (1) precise and differentiable optical physics kernels that enable complete explorations of DONNs architectures, (2) optical physics computation kernel acceleration that significantly reduces the runtime cost in training, emulation, and deployment of DONNs, and (3) versatile and flexible optical system modeling and user-friendly domain-specific-language (DSL). As a result, LightRidge framework enables efficient end-to-end design and deployment of DONNs, and significantly reduces the efforts for programming, hardware-software codesign, and chip integration. Our results are experimentally conducted with physical optical systems, where we demonstrate: (1) the optical physics kernels precisely correlated to low-level physics and systems, (2) significant speedups in runtime with physics-aware emulation workloads compared to the state-of-the-art commercial system, (3) effective architectural design space exploration verified by the hardware prototype and on-chip integration case study, and (4) novel DONN design principles including successful demonstrations of advanced image classification and image segmentation task using DONNs architecture and topology., Comment: 16 pages, 13 figures, Architectural Support for Programming Languages and Operating Systems (ASPLOS'24)

Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing, security screening, drug detection, and autonomous driving. However, due to the challenge of implementing reconfigurability, deploying multi-task learning (MTL) algorithms on ONNs requires re-building and duplicating the physical diffractive systems, which significantly degrades the energy and cost efficiency in practical application scenarios. This work presents a novel ONNs architecture, namely, \textit{RubikONNs}, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a \textit{Rubik's Cube}. To optimize MTL performance on RubikONNs, two domain-specific physics-aware training algorithms \textit{RotAgg} and \textit{RotSeq} are proposed. Our experimental results demonstrate more than 4$\times$ improvements in energy and cost efficiency with marginal accuracy degradation compared to the state-of-the-art approaches., Comment: To appear at 32nd International Joint Conference on Artificial Intelligence (IJCAI'23)

Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm, generating the optimal scheduling solution is a challenging NP-hard problem. Furthermore, the complexity of scheduling DNN computational graphs will further increase on pipelined multi-core systems considering memory communication cost, as well as the increasing size of DNNs. Using the synthetic graph for the training dataset, this work presents a reinforcement learning (RL) based scheduling framework RESPECT, which learns the behaviors of optimal optimization algorithms and generates near-optimal scheduling results with short solving runtime overhead. Our framework has demonstrated up to $\sim2.5\times$ real-world on-chip inference runtime speedups over the commercial compiler with ten popular ImageNet models deployed on the physical Coral Edge TPUs system. Moreover, compared to the exact optimization methods, the proposed RL scheduling improves the scheduling optimization runtime by up to 683$\times$ speedups compared to the commercial compiler and matches the exact optimal solutions with up to 930$\times$ speedups. Finally, we perform a comprehensive generalizability test, which demonstrates RESPECT successfully imitates optimal solving behaviors from small synthetic graphs to large real-world DNNs computational graphs., Comment: 6 pages, ACM/IEEE Design Automation Conference (DAC'23)

As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption. However, there is a mismatch, i.e., significant prediction accuracy loss, between the DONN numerical modelling and physical optical device deployment, because of the interpixel interaction within the diffractive layers. In this work, we propose a physics-aware diffractive optical neural network training framework to reduce the performance difference between numerical modeling and practical deployment. Specifically, we propose the roughness modeling regularization in the training process and integrate the physics-aware sparsification method to introduce sparsity to the phase masks to reduce sharp phase changes between adjacent pixels in diffractive layers. We further develop $2\pi$ periodic optimization to reduce the roughness of the phase masks to preserve the performance of DONN. Experiment results demonstrate that, compared to state-of-the-arts, our physics-aware optimization can provide $35.7\%$, $34.2\%$, $28.1\%$, and $27.3\%$ reduction in roughness with only accuracy loss on MNIST, FMNIST, KMNIST, and EMNIST, respectively., Comment: This paper is accepted by the Design Automation Conference (DAC), 2023

Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapath synthesis, malicious logic identification, etc. Mostly, conventional reasoning approaches leverage structural hashing and functional propagation, suffering from limited scalability and inefficient usage of modern computing power. In response, we propose a novel symbolic reasoning framework exploiting graph neural networks (GNNs) and GPU acceleration to reason high-level functional blocks from gate-level netlists, namely Gamora, which offers high reasoning performance w.r.t exact reasoning algorithms, strong scalability to BNs with over 33 million nodes, and generalization capability from simple to complex designs. To further demonstrate the capability of Gamora, we also evaluate its reasoning performance after various technology mapping options, since technology-dependent optimizations are known to make functional reasoning much more challenging. Experimental results show that (1) Gamora reaches almost 100% and over 97% reasoning accuracy for carry-save-array (CSA) and Booth-encoded multipliers, respectively, with up to six orders of magnitude speedups compared to the state-of-the-art implementation in the ABC framework; (2) Gamora maintains high reasoning accuracy (>92%) in finding functional modules after complex technology mapping, upon which we comprehensively analyze the impacts on Gamora reasoning from technology mapping., Comment: This work will appear at 60th Design Automation Conference (DAC'23)

Establishing the period--Wesenheit relation requires independent and accurate distance measurements of classical Cepheids (DCEPs). The precise distance provided by an associated open cluster independently calibrates the period--Wesenheit relation of DCEPs. 51 DCEPs associated with open clusters are compiled using the constraints of five-dimensional astrometric information. By directly using Gaia DR3 parallax, the period--Wesenheit relation in the Gaia $G$ band is calibrated as $W_G = (-3.06 \pm 0.11) \log P + (-2.96 \pm 0.10)$. Compared with the results derived by directly adopting the DCEP parallaxes or using distance moduli, the Wesenheit magnitudes based on the cluster-averaged parallaxes exhibit a tighter relation with the period. Besides, there is a systematic offset between the observed Wesenheit absolute magnitudes of distant OC-DCEPs and their fitted magnitudes. After considering the parallax zero-point correction, the systematic offset can be reduced, yielding a probably better PW relation of $W_G = (-2.94 \pm 0.12) \log P + (-2.93 \pm 0.11)$., Comment: 15 pages, 5 figures, accepted for publication in ApJ

Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms. However, inversely mapping algorithm-trained physical model parameters onto real-world optical devices with discrete values is a non-trivial task as existing optical devices have non-unified discrete levels and non-monotonic properties. This work proposes a novel device-to-system hardware-software codesign framework, which enables efficient physics-aware training of DONNs w.r.t arbitrary experimental measured optical devices across layers. Specifically, Gumbel-Softmax is employed to enable differentiable discrete mapping from real-world device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task. The results have demonstrated that our proposed framework offers significant advantages over conventional quantization-based methods, especially with low-precision optical devices. Finally, the proposed algorithm is fully verified with physical experimental optical systems in low-precision settings., Comment: International Conference on Computer-Aided Design (ICCAD'2022) To appear