Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies. In this work, we address these limitations by introducing novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure, size, question type, fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks. To foster further research in this area, we will open-source the datasets and evaluation framework used in our experiments.

Time series data are ubiquitous, and is one of the fastest growing and richest types of data. Recent advances in sensing technologies has resulted in a rapid growth in the size and complexity of time series archives. This demands development of new tools and solutions. The goals of this workshop are to: (1) highlight the significant challenges that underpin learning and mining from time series data (e.g. irregular sampling, spatiotemporal structure, uncertainty quantification), (2) discuss recent algorithmic, theoretical, statistical, or systems-based developments for tackling these problems, and (3) exploring new frontiers in time series analysis and their connections with important topics such as knowledge representation, reasoning, control, and business intelligence. In summary, our workshop will focus on both the theoretical and practical aspects of time series data analysis and will provide a platform for researchers and practitioners from both academia and industry to discuss potential research directions, key technical issues, and present solutions to tackle related issues in practical applications. We will invite researchers and practitioners from the related areas of AI, machine learning, data science, statistics, and many others to contribute to this workshop.

Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the difficulty in reproducing the data distribution hinder directly applying meta-learning techniques. Although the knowledge of governing partial differential equations (PDE) of the data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems. In this work, we propose a framework, physics-aware meta-learning with auxiliary tasks, whose spatial modules incorporate PDE-independent knowledge and temporal modules utilize the generalized features from the spatial modules to be adapted to the limited data, respectively. The framework is inspired by a local conservation law expressed mathematically as a continuity equation and does not require the exact form of governing equation to model the spatiotemporal observations. The proposed method mitigates the need for a large number of real-world tasks for meta-learning by leveraging spatial information in simulated data to meta-initialize the spatial modules. We apply the proposed framework to both synthetic and real-world spatiotemporal prediction tasks and demonstrate its superior performance with limited observations.

We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference. Deep Neural Networks with Controllable Rule Representations (DeepCTRL) incorporates a rule encoder into the model coupled with a rule-based objective, enabling a shared representation for decision making. DeepCTRL is agnostic to data type and model architecture. It can be applied to any kind of rule defined for inputs and outputs. The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio. In real-world domains where incorporating rules is critical -- such as Physics, Retail and Healthcare -- we show the effectiveness of DeepCTRL in teaching rules for deep learning. DeepCTRL improves the trust and reliability of the trained models by significantly increasing their rule verification ratio, while also providing accuracy gains at downstream tasks. Additionally, DeepCTRL enables novel use cases such as hypothesis testing of the rules on data samples, and unsupervised adaptation based on shared rules between datasets.

Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequence by explicitly learning/extracting the trend, seasonality, and event components. The trend is learned via a 1D and 2D temporal CNN and LSTM hierarchical neural net. The CNN-LSTM architecture can (i) seamlessly leverage the dependency among multiple correlated time series in a natural way, (ii) extract the weighted differencing feature for better trend learning, and (iii) memorize the long-term sequential pattern. The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets, such as forecasts of Amazon AWS Simple Storage Service (S3) and Elastic Compute Cloud (EC2) billings, and the closing prices for corporate stocks in the same category.

More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. These crimes are often further classified into different types based on the circumstances surrounding the crime (e.g., domestic violence, gang-related). Despite recent technological advances in AI and machine learning, these additional classification tasks are still done manually by specially trained police officers. In this paper, we provide the first attempt to develop a more automatic system for classifying crimes. In particular, we study the question of classifying whether a given violent crime is gang-related. We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014-2016, we experimentally show that our PGNN outperforms all other typically used classifiers for the problem of classifying gang-related violent crimes.

According to the Center for Disease Control and Prevention, hundreds of thousands initiate smoking each year, and millions live with smoking-related diseases in the United States. Many tobacco users discuss their opinions, habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to curb their tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets manually labeled as either pro-tobacco or not pro-tobacco. This model achieved a 90% accuracy rate on the training set and 74% on test data. Users posting protobacco tweets were matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, leveraging the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggested that our system would perform well if deployed.

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality.

Recently, many e-commerce websites have encouraged their users to rate shopping items and write review texts. This review information has been very useful for understanding user preferences and item properties, as well as enhancing the capability to make personalized recommendations of these websites. In this paper, we propose to model user preferences and item properties using convolutional neural networks (CNNs) with dual local and global attention, motivated by the superiority of CNNs to extract complex features. By using aggregated review texts from a user and aggregated review text for an item, our model can learn the unique features (embedding) of each user and each item. These features are then used to predict ratings. We train these user and item networks jointly which enable the interaction between users and items in a similar way as matrix factorization. The local attention provides us insight on a user's preferences or an item's properties. The global attention helps CNNs focus on the semantic meaning of the whole review text. Thus, the combined local and global attentions enable an interpretable and better-learned representation of users and items. We validate the proposed models by testing on popular review datasets in Yelp and Amazon and compare the results with matrix factorization (MF), the hidden factor and topical (HFT) model, and the recently proposed convolutional matrix factorization (ConvMF+). Our proposed CNNs with dual attention model outperforms HFT and ConvMF+ in terms of mean square errors (MSE). In addition, we compare the user/item embeddings learned from these models for classification and recommendation. These results also confirm the superior quality of user/item embeddings learned from our model.

Storage I/O performance remains a key factor that determines the overall user experience of a computer system. This is especially true for mobile systems as users commonly browse and navigate through many high-quality pictures and video clips stored in their device. The appetite for more appealing user interface has continuously pushed the mobile storage interface speed up; emerging UFS 2.0 standard provisions a maximum bandwidth of as high as 1,200 MB/s. In this work, we propose, design, and implement a mobile storage architecture that leverages the high-speed DRAM interface for communication, thus substantially expanding the storage performance headroom. In order to effectively turn the existing DRAM interface into a storage interface, we design a new storage protocol that runs on top of the DRAM interface. Our protocol builds on a small host interface buffer structure mapped to the system's memory space. Based on this protocol, we develop and fabricate a storage controller chip that natively supports the LPDDR3 interface. We also develop a host software stack (Linux device driver and boot loader) and a host platform board. Finally we show the feasibility of our proposal by constructing a full Android system running on the developed storage device and platform. Our detailed evaluation shows that the proposed storage architecture has very low protocol handling overheads and compares favorably to a UFS 2.0 device. The proposed architecture obviates the need for implementing a separate host-side storage controller on a mobile CPU chip.