Scaling Up Probabilistic Circuits by Latent Variable Distillation

Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities). One key challenge is to scale PCs to model large and high-dimensional real-world datasets: we observe that as the number of parameters in PCs increases, their performance immediately plateaus. This phenomenon suggests that the existing optimizers fail to exploit the full expressive power of large PCs. We propose to overcome such bottleneck by latent variable distillation: we leverage the less tractable but more expressive deep generative models to provide extra supervision over the latent variables of PCs. Specifically, we extract information from Transformer-based generative models to assign values to latent variables of PCs, providing guidance to PC optimizers. Experiments on both image and language modeling benchmarks (e.g., ImageNet and WikiText-2) show that latent variable distillation substantially boosts the performance of large PCs compared to their counterparts without latent variable distillation. In particular, on the image modeling benchmarks, PCs achieve competitive performance against some of the widely-used deep generative models, including variational autoencoders and flow-based models, opening up new avenues for tractable generative modeling

iJTyper: An Iterative Type Inference Framework for Java by Integrating Constraint- and Statistically-based Methods

Inferring the types of API elements in incomplete code snippets (e.g., those on Q&A forums) is a prepositive step required to work with the code snippets. Existing type inference methods can be mainly categorized as constraint-based or statistically-based. The former imposes higher requirements on code syntax and often suffers from low recall due to the syntactic limitation of code snippets. The latter relies on the statistical regularities learned from a training corpus and does not take full advantage of the type constraints in code snippets, which may lead to low precision. In this paper, we propose an iterative type inference framework for Java, called iJTyper, by integrating the strengths of both constraint- and statistically-based methods. For a code snippet, iJTyper first applies a constraint-based method and augments the code context with the inferred types of API elements. iJTyper then applies a statistically-based method to the augmented code snippet. The predicted candidate types of API elements are further used to improve the constraint-based method by reducing its pre-built knowledge base. iJTyper iteratively executes both methods and performs code context augmentation and knowledge base reduction until a termination condition is satisfied. Finally, the final inference results are obtained by combining the results of both methods. We evaluated iJTyper on two open-source datasets. Results show that 1) iJTyper achieves high average precision/recall of 97.31% and 92.52% on both datasets; 2) iJTyper significantly improves the recall of two state-of-the-art baselines, SnR and MLMTyper, by at least 7.31% and 27.44%, respectively; and 3) iJTyper improves the average precision/recall of the popular language model, ChatGPT, by 3.25% and 0.51% on both datasets

GROOT: Learning to Follow Instructions by Watching Gameplay Videos

We study the problem of building a controller that can follow open-ended instructions in open-world environments. We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations. A new learning framework is derived to allow learning such instruction-following controllers from gameplay videos while producing a video instruction encoder that induces a structured goal space. We implement our agent GROOT in a simple yet effective encoder-decoder architecture based on causal transformers. We evaluate GROOT against open-world counterparts and human players on a proposed Minecraft SkillForge benchmark. The Elo ratings clearly show that GROOT is closing the human-machine gap as well as exhibiting a 70% winning rate over the best generalist agent baseline. Qualitative analysis of the induced goal space further demonstrates some interesting emergent properties, including the goal composition and complex gameplay behavior synthesis. The project page is available at https://craftjarvis-groot.github.io

Validation of QTL mapping and transcriptome profiling for identification of candidate genes associated with nitrogen stress tolerance in sorghum

Background: Quantitative trait loci (QTLs) detected in one mapping population may not be detected in other mapping populations at all the time. Therefore, before being used for marker assisted breeding, QTLs need to be validated in different environments and/or genetic backgrounds to rule out statistical anomalies. In this regard, we mapped the QTLs controlling various agronomic traits in a recombinant inbred line (RIL) population in response to Nitrogen (N) stress and validated these with the reported QTLs in our earlier study to find the stable and consistent QTLs across populations. Also, with Illumina RNA-sequencing we checked the differential expression of gene (DEG) transcripts between parents and pools of RILs with high and low nitrogen use efficiency (NUE) and overlaid these DEGs on to the common validated QTLs to find candidate genes associated with N-stress tolerance in sorghum. Results: An F7 RIL population derived from a cross between CK60 (N-stress sensitive) and San Chi San (N-stress tolerant) inbred sorghum lines was used to map QTLs for 11 agronomic traits tested under different N-levels. Composite interval mapping analysis detected a total of 32 QTLs for 11 agronomic traits. Validation of these QTLs revealed that of the detected, nine QTLs from this population were consistent with the reported QTLs in earlier study using CK60/China17 RIL population. The validated QTLs were located on chromosomes 1, 6, 7, 8, and 9. In addition, root transcriptomic profiling detected 55 and 20 differentially expressed gene (DEG) transcripts between parents and pools of RILs with high and low NUE respectively. Also, overlay of these DEG transcripts on to the validated QTLs found candidate genes transcripts for NUE and also showed the expected differential expression. For example, DEG transcripts encoding Lysine histidine transporter 1 (LHT1) had abundant expression in San Chi San and the tolerant RIL pool, whereas DEG transcripts encoding seed storage albumin, transcription factor IIIC (TFIIIC) and dwarfing gene (DW2) encoding multidrug resistance-associated protein-9 homolog showed abundant expression in CK60 parent, similar to earlier study. Conclusions: The validated QTLs among different mapping populations would be the most reliable and stable QTLs across germplasm. The DEG transcripts found in the validated QTL regions will serve as future candidate genes for enhancing NUE in sorghum using molecular approaches

ProAgent: Building Proactive Cooperative Agents with Large Language Models

Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learning-based methods, whose policy generalization depends heavily on the diversity of teammates they interact with during the training phase. Such reliance, however, constrains the agents' capacity for strategic adaptation when cooperating with unfamiliar teammates, which becomes a significant challenge in zero-shot coordination scenarios. To address this challenge, we propose ProAgent, a novel framework that harnesses large language models (LLMs) to create proactive agents capable of dynamically adapting their behavior to enhance cooperation with teammates. ProAgent can analyze the present state, and infer the intentions of teammates from observations. It then updates its beliefs in alignment with the teammates' subsequent actual behaviors. Moreover, ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various of coordination scenarios. Experimental evaluations conducted within the Overcooked-AI environment unveil the remarkable performance superiority of ProAgent, outperforming five methods based on self-play and population-based training when cooperating with AI agents. Furthermore, in partnered with human proxy models, its performance exhibits an average improvement exceeding 10% compared to the current state-of-the-art method. For more information about our project, please visit~\url{https://pku-proagent.github.io}.Comment: v3 is the AAAI'24 camera ready version, which polished abstract and introduction based on the reviewers' comments, and enriched related works. 7 pages of main content, 2 pages of references, 2 figures and 1 tabl

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. JARVIS-1 is the existing most general agent in Minecraft, capable of completing over 200 different tasks using control and observation space similar to humans. These tasks range from short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g., "obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in short-horizon tasks, achieving nearly perfect performance. In the classic long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the reliability of current state-of-the-art agents by 5 times and can successfully complete longer-horizon and more challenging tasks. The project page is available at https://craftjarvis.org/JARVIS-1Comment: update project pag

Production of tocotrienols in seeds of cotton (Gossypium hirsutum L.) enhances oxidative stability and offers nutraceutical potential

Upland cotton (Gossypium hirsutum L.) is an economically important multi-purpose crop cultivated globally for fibre, seed oil and protein. Cottonseed oil also is naturally rich in vitamin E components (collectively known as tocochromanols), with a- and c-tocopherols comprising nearly all of the vitamin E components. By contrast, cottonseeds have little or no tocotrienols, tocochromanols with a wide range of health benefits. Here, we generated transgenic cotton lines expressing the barley (Hordeum vulgare) homogentisate geranylgeranyl transferase coding sequence under the control of the Brassica napus seed-specific promoter, napin. Transgenic cottonseeds had ~twofold to threefold increases in the accumulation of total vitamin E (tocopherols + tocotrienols), with more than 60% c-tocotrienol. Matrix assisted laser desorption ionization-mass spectrometry imaging showed that c-tocotrienol was localized throughout the transgenic embryos. In contrast, the native tocopherols were distributed unequally in both transgenic and non-transgenic embryos. a- Tocopherol was restricted mostly to cotyledon tissues and c-tocopherol was more enriched in the embryonic axis tissues. Production of tocotrienols in cotton embryos had no negative impact on plant performance or yield of other important seed constituents including fibre, oil and protein. Advanced generations of two transgenic events were field grown, and extracts of transgenic seeds showed increased antioxidant activity relative to extracts from non-transgenic seeds. Furthermore, refined cottonseed oil from the two transgenic events showed 30% improvement in oxidative stability relative to the non-transgenic cottonseed oil. Taken together, these materials may provide new opportunities for cottonseed co-products with enhanced vitamin E profile for improved shelf life and nutrition

Unlocking the potential of weberite-type metal fluorides in electrochemical energy storage

Sodium-ion batteries (NIBs) are a front-runner among the alternative battery technologies suggested for substituting the state-of-the-art lithium-ion batteries (LIBs). The specific energy of Na-ion batteries is significantly lower than that of LIBs, which is mainly due to the lower operating potentials and higher molecular weight of sodium insertion cathode materials. To compete with the high energy density of LIBs, high voltage cathode materials are required for NIBs. Here we report a theoretical investigation on weberite-type sodium metal fluorides (SMFs), a new class of high voltage and high energy density materials which are so far unexplored as cathode materials for NIBs. The weberite structure type is highly favorable for sodium-containing transition metal fluorides, with a large variety of transition metal combinations (M, M’) adopting the corresponding Na2MM’F7 structure. A series of known and hypothetical compounds with weberite-type structure were computationally investigated to evaluate their potential as cathode materials for NIBs. Weberite-type SMFs show two-dimensional pathways for Na+ diffusion with surprisingly low activation barriers. The high energy density combined with low diffusion barriers for Na+ makes this type of compounds promising candidates for cathode materials in NIBs

Efficacy of Chuanxiong Ding Tong Herbal Formula Granule in the Treatment and Prophylactic of Migraine Patients: A Randomized, Double-Blind, Multicenter, Placebo-Controlled Trial

Objective. To evaluate the efficacy of traditional Chinese herbal ChuanXiong Ding Tong herbal formula granule (CXDT-HFG) for migraine patients with “the Syndrome of Liver Wind and Blood Stasis.” Methods. 150 migraine patients were recruited and assigned randomly in a double-blind, placebo-controlled study to receive CXDT-HFG (n=99) plus necessary analgesics, or placebo (n=51) plus necessary analgesics for 16 weeks (12 weeks’ intervention and 4 weeks’ follow up). Outcome measures included migraine days, frequency of migraine attacks, analgesics consumption for acute treatment, and the proportion of responders as well as the visual analogue scale (VAS) scores and intensity for pain. Results. Compared with the placebo group, the CXDT-HFG group showed significant reduction in migraine days and attacks frequency at week 12 and follow-up period (P0.05). Conclusion. CXDT-HFG was more effective than placebo in decreasing days of migraine attacks, frequency, VAS scores, and relieving pain intensity for migraine patients

A revised mechanistic model for sodium insertion in hard carbons

Hard carbons have shown considerable promise as anodes for emerging sodium-ion battery technologies. Current understanding of sodium-storage behaviour in hard carbons attributes capacity to filling of graphitic interlayers and pores, and adsorption at defects, although there is still considerable debate regarding the voltages at which these mechanisms occur. Here, ex situ23Na solid-state NMR and total scattering studies on a systematically tuned series of hard carbons revealed the formation of increasingly metallic sodium clusters in direct correlation to the growing pore size, occurring only in samples which exhibited a low voltage plateau. Combining experimental results with DFT calculations, we propose a revised mechanistic model in which sodium ions store first simultaneously and continuously at defects, within interlayers and on pore surfaces. Once these higher energy binding sites are filled, pore filling occurs during the plateau region, where the densely confined sodium takes on a greater degree of metallicity