Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge

This work presents an enhanced approach to generating scene graphs by incorporating a relationship hierarchy and commonsense knowledge. Specifically, we propose a Bayesian classification head that exploits an informative hierarchical structure. It jointly predicts the super-category or type of relationship between the two objects, along with the detailed relationship under each super-category. We design a commonsense validation pipeline that uses a large language model to critique the results from the scene graph prediction system and then use that feedback to enhance the model performance. The system requires no external large language model assistance at test time, making it more convenient for practical applications. Experiments on the Visual Genome and the OpenImage V6 datasets demonstrate that harnessing hierarchical relationships enhances the model performance by a large margin. The proposed Bayesian head can also be incorporated as a portable module in existing scene graph generation algorithms to improve their results. In addition, the commonsense validation enables the model to generate an extensive set of reasonable predictions beyond dataset annotations

Discontinuous transition to shear flow turbulence

Depending on the type of flow the transition to turbulence can take one of two forms, either turbulence arises from a sequence of instabilities, or from the spatial proliferation of transiently chaotic domains, a process analogous to directed percolation. Both scenarios are inherently continuous and hence the transformation from ordered laminar to fully turbulent fluid motion is only accomplished gradually with flow speed. Here we show that these established transition types do not account for the more general setting of shear flows subject to body forces. By attenuating spatial coupling and energy transfer, spatio-temporal intermittency is suppressed and with forcing amplitude the transition becomes increasingly sharp and eventually discontinuous. We argue that the suppression of the continuous range and the approach towards a first order, discontinuous scenario applies to a wide range of situations where in addition to shear, flows are subject to e.g. gravitational, centrifugal or electromagnetic forces

Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction

Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modelling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill in this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.Comment: Accepted by the TKDE journa

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) researchers and engineers. Addressing this requires developers to partition a large model to distribute it across multiple GPUs or TPUs. This necessitates considerable coding and intricate configuration efforts with existing model parallel tools, such as Megatron-LM, DeepSpeed, and Alpa. These tools require users' expertise in machine learning systems (MLSys), creating a bottleneck in LLM development, particularly for developers without MLSys background. In this work, we present Redco, a lightweight and user-friendly tool crafted to automate distributed training and inference for LLMs, as well as to simplify ML pipeline development. The design of Redco emphasizes two key aspects. Firstly, to automate model parallism, our study identifies two straightforward rules to generate tensor parallel strategies for any given LLM. Integrating these rules into Redco facilitates effortless distributed LLM training and inference, eliminating the need of additional coding or complex configurations. We demonstrate the effectiveness by applying Redco on a set of LLM architectures, such as GPT-J, LLaMA, T5, and OPT, up to the size of 66B. Secondly, we propose a mechanism that allows for the customization of diverse ML pipelines through the definition of merely three functions, eliminating redundant and formulaic code like multi-host related processing. This mechanism proves adaptable across a spectrum of ML algorithms, from foundational language modeling to complex algorithms like meta-learning and reinforcement learning. Consequently, Redco implementations exhibit much fewer code lines compared to their official counterparts.Comment: Released under Apache License 2.0 at https://github.com/tanyuqian/redc

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit internal user information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields inside one event have proven to be strong indicators for fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in the risk management system of one of the world's largest e-commerce platforms, which utilize it to provide real-time transaction fraud detection. Experimental results on real industrial data from different regions in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.Comment: 11 pages, 4 figure

Evaluation of branched GDGTs and leaf wax n-alkane δ2H as (paleo) environmental proxies in East Africa

The role of mountain evolution on local climate is poorly understood and potentially underestimated in climate models. One prominent example is East Africa, which underwent major geodynamic changes with the onset of the East African Rift System (EARS) more than 250 Myr ago. This study explores, at the regional East African scale, a molecular approach for terrestrially-based paleo-climatic reconstructions that takes into account both changes in temperature and in altitude, potentially leading to an improved concept in paleo-climatic reconstructions. Using surface soils collected along pronounced altitudinal gradients in Mt. Rungwe (n=40; Southwest Tanzania) and Mt. Kenya (n=20; Central Kenya), we investigate the combination of 2 terrestrial proxies, leaf wax n-alkane δ2H (δ2Hwax) and branched glycerol dialkyl glycerol tetraether (br GDGT) membrane lipids, as (paleo) elevation and (paleo) temperature proxies, respectively. At the mountain scale, a weak link between δ2Hwax and altitude (R2 = 0.33) is observed at Mt. Kenya, but no relationship is observed at Mt. Rungwe. It is likely that additional parameters, such as decreasing relative humidity (RH) or vegetation changes with altitude, are outcompeting the expected 2H-depletion trend along Mt. Rungwe. In contrast, br GDGT-derived absolute mean annual air temperature (MAAT) and temperature lapse rate (0.65 °C/100 m) for both mountains are in good agreement with direct field measurements, further supporting the robustness of this molecular proxy for (paleo) temperature reconstructions. At the regional scale, estimated and observed δ2H data in precipitation along 3 mountains in East Africa (Mts. Rungwe, Kenya and Kilimanjaro) highlight a strong spatial heterogeneity, preventing the establishment of a regional based calibration of δ2Hwax for paeloaltitudinal reconstructions. Different from that, an improved regional soil calibration is developed between br GDGT distribution and MAAT by combining the data from this study (Mts. Rungwe and Kenya) with previous results from East African surface soils along Mts. Kilimanjaro (Tanzania) and Rwenzori (Uganda). This new regional calibration, based on 105 samples, improves both the R2 (0.77) and RMSE (root mean square error; 2.4 °C) of br GDGT-derived MAAT over the global soil calibrations previously established (R2 = 0.56; RMSE = 4.2 °C) and leads to more accurate (paleo) temperature reconstructions in the region

Diet of the endangered big-headed turtle \u3cem\u3ePlatysternon megacephalum\u3c/em\u3e

Populations of the big-headed turtle Platysternon megacephalum are declining at unprecedented rates across most of its distribution in Southeast Asia owing to unsustainable harvest for pet, food, and Chinese medicine markets. Research on Asian freshwater turtles becomes more challenging as populations decline and basic ecological information is needed to inform conservation efforts. We examined fecal samples collected from P. megacephalum in five streams in Hong Kong to quantify the diet, and we compared the germination success of ingested and uningested seeds. Fruits, primarily of Machilus spp., were most frequently consumed, followed by insects, plant matter, crabs and mollusks. The niche breadth of adults was wider than that of juveniles. Diet composition differed between sites, which may be attributable to the history of illegal trapping at some sites, which reduced the proportion of larger and older individuals. Digestion of Machilus spp. fruits by P. megacephalum enhanced germination success of seeds by about 30%. However, most digested seeds are likely defecated in water in this highly aquatic species, which limits the potential benefit to dispersal. The results of our study can be used by conservation-related captive breeding programs to ensure a more optimal diet is provided to captive P. megacephalum

Experimental and Numerical Analysis of High-Resolution Injection Technique for Capillary Electrophoresis Microchip

This study presents an experimental and numerical investigation on the use of high-resolution injection techniques to deliver sample plugs within a capillary electrophoresis (CE) microchip. The CE microfluidic device was integrated into a U-shaped injection system and an expansion chamber located at the inlet of the separation channel, which can miniize the sample leakage effect and deliver a high-quality sample plug into the separation channel so that the detection performance of the device is enhanced. The proposed 45° U-shaped injection system was investigated using a sample of Rhodamine B dye. Meanwhile, the analysis of the current CE microfluidic chip was studied by considering the separation of Hae III digested ϕx-174 DNA samples. The experimental and numerical results indicate that the included 45° U-shaped injector completely eliminates the sample leakage and an expansion separation channel with an expansion ratio of 2.5 delivers a sample plug with a perfect detection shape and highest concentration intensity, hence enabling an optimal injection and separation performance

A review into the use of ceramics in microbial fuel cells

© 2016 The Authors. Microbial fuel cells (MFCs) offer great promise as a technology that can produce electricity whilst at the same time treat wastewater. Although significant progress has been made in recent years, the requirement for cheaper materials has prevented the technology from wider, out-of-the-lab, implementation. Recently, researchers have started using ceramics with encouraging results, suggesting that this inexpensive material might be the solution for propelling MFC technology towards real world applications. Studies have demonstrated that ceramics can provide stability, improve power and treatment efficiencies, create a better environment for the electro-active bacteria and contribute towards resource recovery. This review discusses progress to date using ceramics as (i) the structural material, (ii) the medium for ion exchange and (iii) the electrode for MFCs