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Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of "thought" -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs' to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs' reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to "think" with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI's o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions., Comment: 36 pages, 5 figures

Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled network data, facilitating the learning process of the resilience predictor by illuminating the relationship between network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%. Furthermore, the framework still demonstrates a pronounced augmentation capability in extreme low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience. We have open-sourced our code in the following link, https://github.com/tsinghua-fib-lab/TDNetGen.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Early detection of dental disease is crucial to prevent adverse outcomes. Today, dental X-rays are currently the most accurate gold standard for dental disease detection. Unfortunately, regular X-ray exam is still a privilege for billions of people around the world. In this paper, we ask: "Can we develop a low-cost sensing system that enables dental self-examination in the comfort of one's home?" This paper presents ToMoBrush, a dental health sensing system that explores using off-the-shelf sonic toothbrushes for dental condition detection. Our solution leverages the fact that a sonic toothbrush produces rich acoustic signals when in contact with teeth, which contain important information about each tooth's status. ToMoBrush extracts tooth resonance signatures from the acoustic signals to characterize varied dental health conditions of the teeth. We evaluate ToMoBrush on 19 participants and dental-standard models for detecting common dental problems including caries, calculus, and food impaction, achieving a detection ROC-AUC of 0.90, 0.83, and 0.88 respectively. Interviews with dental experts validate ToMoBrush's potential in enhancing at-home dental healthcare.

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly aesthetic images. This creates the need for aesthetic alignment post pre-training. In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality. We pre-train a latent diffusion model on $1.1$ billion image-text pairs and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of $82.9\%$ compared with its pre-trained only counterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred $68.4\%$ and $71.3\%$ of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models. In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models.

The manipulation of two-dimensional (2D) magnetic order is of significant importance to facilitate future 2D magnets for low-power and high-speed spintronic devices. Van der Waals stacking engineering makes promises for controllable magnetism via interlayer magnetic coupling. However, directly examining the stacking order changes accompanying magnetic order transitions at the atomic scale and preparing device-ready 2D magnets with controllable magnetic orders remain elusive. Here, we demonstrate effective control of interlayer stacking in exfoliated CrBr$_3$ via thermally assisted strain engineering. The stable interlayer ferromagnetic (FM), antiferromagnetic (AFM), and FM-AFM coexistent ground states confirmed by the magnetic circular dichroism measurements are realized. Combined with the first-principles calculations, the atomically-resolved imaging technique reveals the correlation between magnetic order and interlay stacking order in the CrBr$_3$ flakes unambiguously. A tunable exchange bias effect is obtained in the mixed phase of FM and AFM states. This work will introduce new magnetic properties by controlling the stacking order, and sequence of 2D magnets, providing ample opportunities for their application in spintronic devices., Comment: 7 pages, 4 figures

Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets. We provide 641K part masks annotated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/facebookresearch/paco.

Ulta-wide bandgap semiconductors based on $\beta$-Ga$_2$O$_3$ offer the potential to achieve higher power switching performance, efficiency, and lower manufacturing cost than today's wide bandgap power semiconductors. However, the most critical challenge to the commercialization of Ga$_2$O$_3$ electronics is overheating, which impacts the device's performance and reliability. We fabricated a Ga$_2$O$_3$/4H-SiC composite wafer using a fusion-bonding method. A low temperature ($\le$ 600 $^{\circ}$C) epitaxy and device processing approach based on low-temperature (LT) metalorganic vapor phase epitaxy is developed to grow a Ga$_2$O$_3$ epitaxial channel layer on the composite wafer and subsequently fabricate into Ga$_2$O$_3$ power MOSFETs. This LT approach is essential to preserve the structural integrity of the composite wafer. These LT-grown epitaxial Ga$_2$O$_3$ MOSFETs deliver high thermal performance (56% reduction in channel temperature), high voltage blocking capabilities up to 2.45 kV, and power figures of merit of $\sim$ 300 MW/cm$^2$, which is a record high for any heterogeneously integrated Ga$_2$O$_3$ devices reported to date. This work is the first realization of multi-kilovolt homoepitaxial Ga$_2$O$_3$ power MOSFETs fabricated on a composite substrate with high heat transfer performance which delivers state-of-the-art power density values while running much cooler than those on native substrates. Thermal characterization and modeling results reveal that a Ga$_2$O$_3$/diamond composite wafer with a reduced Ga$_2$O$_3$ thickness ($\sim$ 1 $\mu$m) and thinner bonding interlayer ($<$ 10 nm) can reduce the device thermal impedance to a level lower than today's GaN-on-SiC power switches., Comment: 17 pages, 9 figures

We report on the demonstration of metalorganic vapor phase epitaxy-regrown (MOVPE) ohmic contacts in an all MOVPE-grown $\beta$-Ga$_2$O$_3$ metal-semiconductor field effect transistor (MESFET). The low-temperature (600$^{\circ}$C) heavy (n$^{+}$) Si-doped regrown layers exhibit extremely high conductivity with sheet resistance of 73 $\Omega$/$\square$ and record low metal/n$^{+}$-Ga$_2$O$_3$ contact resistance of 80 m$\Omega$.mm and specific contact resistivity of 8.3$\times$10$^{-7}$ $\Omega$.cm$^{2}$ were achieved. The fabricated MESFETs exhibit a maximum drain-to-source current of 130 mA/mm, a high I$_{ON}$/I$_{OFF}$ of $>$10$^{10}$ with a high power FOM of 25 MW/cm$^{2}$ were achieved without any field plates. Nanoparticle-assisted Raman thermometry, thermal modeling, and infrared thermography were performed to assess the device self-heating under the high current and power conditions. This demonstration shows the promise of MOVPE technique for the realization of high-performance lateral $\beta$-Ga$_2$O$_3$ devices and also highlights the need for device-level thermal management.

The label noise transition matrix, characterizing the probabilities of a training instance being wrongly annotated, is crucial to designing popular solutions to learning with noisy labels. Existing works heavily rely on finding "anchor points" or their approximates, defined as instances belonging to a particular class almost surely. Nonetheless, finding anchor points remains a non-trivial task, and the estimation accuracy is also often throttled by the number of available anchor points. In this paper, we propose an alternative option to the above task. Our main contribution is the discovery of an efficient estimation procedure based on a clusterability condition. We prove that with clusterable representations of features, using up to third-order consensuses of noisy labels among neighbor representations is sufficient to estimate a unique transition matrix. Compared with methods using anchor points, our approach uses substantially more instances and benefits from a much better sample complexity. We demonstrate the estimation accuracy and advantages of our estimates using both synthetic noisy labels (on CIFAR-10/100) and real human-level noisy labels (on Clothing1M and our self-collected human-annotated CIFAR-10). Our code and human-level noisy CIFAR-10 labels are available at https://github.com/UCSC-REAL/HOC., Comment: ICML 2021

As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles have received worldwide attention. For convenient centralized management, taxis are considered as the fleet with electrification priority. In this work, we focus on the study on electric taxis dispatching, with consideration of picking up customers and recharging, based on real world traffic data of a large number of taxis in Beijing. First, the assumed electric taxi charging stations are located using the K mean method. Second, based on the station locations and the order demands, which are in form of origin-destination pairs and extracted from the trajectory data, a dispatching strategy as well as the simulation framework is developed with consideration of reducing customer waiting time, mitigating electric taxi charging congestion, and balancing order number distribution among electric taxis. The proposed method models the electric taxi charging behaviors temporally discretely from the aspects of charging demands and availability of chargers, and further incorporates a centralized and intelligent fleet dispatching platform, which is capable of handling taxi service requests and arranging electric taxis' recharging in real time. The methodology in this paper is readily applicable to dispatching of different types of electric vehicle fleet with similar dataset available. Among the method, we use queueing theory to model the electric vehicle charging station waiting phenomena and include this factor into dispatching platform. Carbon emission is also surveyed and analyzed.