Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Anomaly Detection & Diagnosis: A Stein Variational Gradient Descent Approach

Detecting and diagnosing anomalies in observational data plays a crucial role in various real-world applications, such as e-commerce applet maintenance. Unsupervised machine learning techniques are typically employed for anomaly detection and diagnosis due to their convenience and independence from labeled data. Density estimation (DE), as one of the most widely used unsupervised machine learning techniques for anomaly detection, can be categorized into kernel density estimation (KDE)-based methods and normalizing flow (NF)-based methods. While KDE-based methods offer fast computation speed, they often ignore the complex manifold structure present in observational data. On the other hand, NF-based methods address the manifold issue but suffer from longer computation times. In this study, we propose a novel DE-based anomaly detection & diagnosis method using Stein Variational Gradient Descent (SVGD), aiming to leverage the strengths of KDE and NF approaches. Firstly, we rigorously derive the DE capability of SVGD through mathematical analysis. Subsequently, we demonstrate the ability of the SVGD method to perform anomaly diagnosis based on input feature attribution. Finally, to validate the effectiveness of our approach, we conduct experiments using synthetic, benchmark, and industrial datasets. The results demonstrate the superior performance and practical applicability of our proposed method.
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning

The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r. t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify the noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.
The Landscape of Visual Information Communication and Interaction Research: Insights from Analyzing 14 Years of VINCI Conference Proceedings

A large body of work has sought to create effective ways of communicating and interacting with visual information. This paper presents insights into this research landscape based on the proceedings from the International Symposium on Visual Information Communication and Interaction (VINCI). We analyze several aspects of VINCI’s scholarship, including key contributors, emerging topics, and collaboration and citation patterns. Our findings reveal the dynamic character of VINCI and its advancements, highlighting its interdisciplinary nature with contributions from diverse institutions and countries, with the majority of authors engaging in both domestic and international collaborations. The insights from this study can inform future research directions and support more fruitful collaborations among VINCI researchers.
The µ-RA System for Recursive Path Queries over Graphs

We demonstrate a system for recursive query answering over graphs. The system is based on a complete implementation of the recursive relational algebra µ-RA, extended with parsers and compilers adapted for queries over knowledge and property graphs. Each component of the system comes with novelty for processing recursion. As a result, one can formulate, optimize and efficiently answer expressive queries that navigate recursively along paths in different types of graphs. We demonstrate the system on real datasets and show how it performs considering other state-of-the-art systems.
Optimizing for Member Value in an Edge Building Marketplace

Social networks are prosperous marketplaces where creators and consumers congregate to share and consume various content. In general, products that rank content for distribution (such as newsfeeds, stories, and notifications) and are related to edge recommendations (such as connect to members, follow celebrities or groups or hashtags) optimize the experience of active users. Typically, such users generate ample interaction data amenable to accurate model training and prediction. In contrast, we prioritize enhancing the experience of inactive members (IMs) who do not have a rich connection network. We formulate strategies for recommending superior edges to help members grow their connection network. Adapting the recommendations provides enormous value to the IMs and can significantly influence their future behaviour and engagement with the ecosystem. To that end, we propose a general and scalable multi-objective optimization (MOO) framework to provide more value to IMs as invitation recipients on LinkedIn, a professional network with over 900M members. To deal with the enormous scale, we formulate the problem as a massive constrained linear optimization involving billions of variables and millions of constraints and efficiently solve it using accelerated gradient descent,making this the largest deployment of LP-based recommender systems worldwide. Furthermore, the proposed MOO paradigm can solve the general problem of matching different types of entities in an m-sided marketplace. Finally, we discuss the challenges and benefits of implementing and ramping our method in production at scale at LinkedIn and report our findings about the core business metrics related to users' engagement and network health.
Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative Transfer

Identifying regional driving risks is important for real-world applications such as driving safety warning applications, public safety management, and insurance company premium pricing. Previous approaches are either based on traffic accident reports or vehicular sensor data. They either fail to identify potential risks, such as near-miss collisions, which would need other important measurements (e.g., hard break, acceleration, etc.), or fail to generalize to cities without vehicular sensor data, severely limiting their practicality. In this work, we address these two challenges and successfully identify regional driving risks in a target city without vehicular sensor data via cross-city transfer learning. Specifically, we design a novel framework RiskTrans by optimizing both the predictor and the relationship between cities to achieve transfer learning. We advance the existing works from two aspects: (i) we achieve it in a transductive manner without accessing labeled data in the target cities; (ii) we identify and address the problem of negative transfer in cross-city transfer learning, a prominent issue that is often (surprisingly) neglected in previous works. Finally, we conduct extensive experiments based on data collected from 175 thousand vehicles in six cities. The results show RiskTrans outperforms baselines by at least 50.2% and reduces negative transfer by 49.4%.
Ocean process tracers: nitrogen isotopes in the ocean

Introduction Nitrogen has two stable isotopes, N and N (atomic masses of 14 and 15, respectively). N is the more abundant of the two, comprising 99.63% of the nitrogen found in nature. Physical, chemical, and biological processes discriminate between the two isotopes, leading to subtle but measurable differences in the ratio of N to N among different forms of nitrogen found in the marine environment. Nitrogen is a central component of marine biomass and one of the major nutrients required by all phytoplankton. In this sense, biologically available (or ‘fixed’, i.e., non-N2) N is representative of the fundamental patterns of biogeochemical cycling in the ocean. However, N differs from other nutrients in that its oceanic sources and sinks are dominantly internal and biological, with marine N2 fixation supplying much of the fixed N in the ocean and marine denitrification removing it. The N isotopes provide a means of studying both the input/output budget of oceanic fixed N and its cycling within the ocean. In this overview, we outline the isotope systematics of N cycle processes and their impacts on the isotopic composition of the major N reservoirs in the ocean. This information provides a starting point for considering the wide range of questions in ocean sciences to which the N isotopes can be applied.
Leveraging Event Schema to Ask Clarifying Questions for Conversational Legal Case Retrieval

Legal case retrieval is a special IR task aiming to retrieve supporting cases for a given query case. Existing works have shown that conversational search paradigm can improve users' search experience in legal case retrieval. One of the keys to a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Recently, Large Language Models, such as ChatGPT and GPT-4, have shown superior ability in both open-domain QA and conversations with human. Thus it is natural to believe that they could be applied to legal conversational search as well. However, our preliminary study has shown that generating clarifying questions in legal conversational search with SOTA LLMs (e.g., GPT-4) often suffers from several problems such as duplication and low-utility contents. To address these problems, we propose LeClari, which leverages legal event schema as external knowledge to instruct LLMs to generate effective clarifying questions for legal conversational search. LeClari is constructed with a prompt module and a novel legal event selection module. The former defines a prompt with legal events for clarifying question generation and the latter selects potential event types by modeling the relationships of legal event types, conversational context, and candidate cases. We also propose ranking-oriented rewards and employ the reward augmented maximum likelihood (RAML) method to optimize LeClari directly based on the final retrieval performance of the conversational legal search system. Empirical results over two widely adopted legal case retrieval datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines.
Ubi-TOUCH: Ubiquitous Tangible Object Utilization through Consistent Hand-object interaction in Augmented Reality

Utilizing everyday objects as tangible proxies for Augmented Reality (AR) provides users with haptic feedback while interacting with virtual objects. Yet, existing methods focus on the attributes of the objects, constraining the possible proxies and yielding inconsistency in user experience. Therefore, we propose Ubi-TOUCH, an AR system that assists users in seeking a wider range of tangible proxies for AR applications based on the hand-object interaction (HOI) they desire. Given the target interaction with a virtual object, the system scans the users’ vicinity and recommends object proxies with similar interactions. Upon user selection, the system simultaneously tracks and maps users’ physical HOI to the virtual HOI, adaptively optimizing object 6 DoF and the hand gesture to provide consistency between the interactions. We showcase promising use cases of Ubi-TOUCH, such as remote tutorials, AR gaming, and Smart Home control. Finally, we evaluate the performance and usability of Ubi-TOUCH with a user study.
Competition for land and labour among individual farms and agricultural enterprises: Evidence from Kazakhstan's grain region [Der Wettbewerb zwischen Einzelbetrieben und Agrarunternehmen um Boden und Arbeit: Einsichten aus Kasachstans Getreideregion]

This article evaluates the recent evolution of farm structure in Kazakhstan's grain region against the reform objectives of the 1990s and the family farm theory that underpinned the latter. In the study region, super-large agroholdings, large-scale enterprises and smaller individual farms emerged side-by-side and now compete for resources in a homogenous production environment. Drawing on two survey rounds of farm-level data, we find that the agroholdings display the highest factor productivity and are the most competitive on land and labour markets among all farms. However, we also find constant technical returns to scale across farm types and a layer of smaller family farms that is highly competitive on land markets. It is thus too early to conclude that large corporate farms are economically superior to individual (family) farms. But the present analysis clearly calls into question that family farms are a per-se desirable or even the only viable way of organising agricultural production. A revision of the received family farm theory may thus be due.
Commonsense Temporal Action Knowledge (CoTAK) Dataset

This paper presents a publicly available, large-scale dataset resource, CoTAK (COmmonsense Temporal Action Knowledge) consisting of short descriptions of action-describing sentences manually annotated with temporal commonsense knowledge. The dataset consists of over 300K instructional sentences extracted from WikiHow, which are annotated with commonsense knowledge-based temporal labels indicating implicitly understood information about the actions described by the sentences, including approximately how long an action takes to perform and approximately how long its effects last for. For short duration actions labeled as taking seconds or minutes, which would be of relevance to automated task planning, e.g. in robotics applications, the dataset also provides scalar values to accurately label the temporal durations of how long actions take to perform. Experimental results are presented demonstrating that state-of-the-art machine learning techniques such as fine-tuning of large language models are effective in making predictions of commonsense temporal knowledge using the dataset, with up to 80% accuracy, showing the high utility and promising impact of the constructed resource and its applicability towards generating commonsense temporal knowledge relevant to various
Calibrate Graph Neural Networks under Out-of-Distribution Nodes via Deep Q-learning

Graph neural networks (GNNs) have achieved great success in dealing with graph-structured data that are prevalent in the real world. The core of graph neural networks is the message passing mechanism that aims to generate the embeddings of nodes by aggregating the neighboring node information. However, recent work suggests that GNNs also suffer the trustworthiness issues. Our empirical study shows that the calibration error of the in-distribution (ID) nodes would be exacerbated if a graph is mixed with out-of-distribution (OOD) nodes, and we assume that the noisy information from OOD nodes is the root for the worsened calibration error. Both previous study and our empirical study suggest that adjusting the weights of edges could be a promising way to reduce the adverse impact from the OOD nodes. However, how to precisely select the desired edges and modify the corresponding weights is not trivial, since the distribution of OOD nodes is unknown to us. To tackle this problem, we propose a Graph Edge Re-weighting via Deep Q-learning (GERDQ) framework to calibrate the graph neural networks. Our framework aims to explore the potential influence of the change of the edge weights on target ID nodes by sampling and traversing the edges in the graph, and we formulate this process as a Markov Decision Process (MDP). Many existing GNNs could be seamlessly incorporated into our framework. Experimental results show that when wrapped with our method, the existing GNN models can yield lower calibration error under OOD nodes as well as comparable accuracy compared to the original ones and other strong baselines. The source code is available at:
Surveying Emerging Network Approaches for Military Command and Control Systems

This survey paper examines emerging network approaches for military Command and Control (C2) systems. An extensive literature review is provided along the text on network-centric C2 systems. Also, it provides a comprehensive analysis of the paradigm based on C2 concepts, mapping the significant requirements of networked C2 systems to emerging approaches. Likewise, the paper explores ways to simulate networked battle scenarios realistically, leveraging the support of multi-agent systems and network simulations. The article offers a trend analysis of combining network approaches to design innovative solutions and a promising usage of multi-agent systems for realistic simulations. In conclusion, it discusses future implementations emphasizing advanced networking solutions to integrate different technologies and to drive technology boundaries to improve the effectiveness of networked military C2 systems.
T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates

Abstract Named Entity Recognition (NER) has so far evolved from the traditional flat NER to overlapped and discontinuous NER. They have mostly been solved separately, with only several exceptions that concurrently tackle three tasks with a single model. The current best-performing method formalizes the unified NER as word-word relation classification, which barely focuses on mention content learning and fails to detect entity mentions comprising a single word. In this paper, we propose a two-stage span-based framework with templates, namely, T2-NER, to resolve the unified NER task. The first stage is to extract entity spans, where flat and overlapped entities can be recognized. The second stage is to classify over all entity span pairs, where discontinuous entities can be recognized. Finally, multi-task learning is used to jointly train two stages. To improve the efficiency of span-based model, we design grouped templates and typed templates for two stages to realize batch computations. We also apply an adjacent packing strategy and a latter packing strategy to model discriminative boundary information and learn better span (pair) representation. Moreover, we introduce the syntax information to enhance our span representation. We perform extensive experiments on eight benchmark datasets for flat, overlapped, and discontinuous NER, where our model beats all the current competitive baselines, obtaining the best performance of unified NER.
The View from MARS: Empowering Game Stream Viewers with Metadata Augmented Real-time Streaming

We present MARS (Metadata Augmented Real-time Streaming), a system that enables game-aware streaming interfaces for Twitch. Current streaming interfaces provide a video stream of gameplay and a chat channel for conversation, but do not allow viewers to interact with game content independently from the steamer or other viewers. With MARS, a Unity game’s metadata is rendered in real-time onto a Twitch viewer’s interface. The metadata can then power viewer-side interfaces that are aware of the streamer’s game activity and provide new capacities for viewers. Use cases include providing contextual information (e.g. clicking on a unit to learn more), improving accessibility (e.g. slowing down text presentation speed), and supporting novel stream-based game designs (e.g. asymmetric designs where the viewers know more than the streamer). We share the details of MARS’ architecture and capabilities in this paper, and showcase a working prototype for each of our three proposed use cases.
The Price is Right: Removing A/B Test Bias in a Marketplace of Expirable Goods

Pricing Guidance tools at Airbnb aim to help hosts maximize the earning for each night of stay. For a given listing, the earning-maximization price point of a night can vary greatly with lead-day - the number of days from now until the night of stay. This introduces systematic bias in running marketplace A/B tests to compare the performances of two pricing strategies. Lead-day bias can cause the short-term experiment result to move in the opposite direction to the long-term impact, possibly leading to the suboptimal business decision and customer dissatisfaction. We propose an efficient experimentation approach that corrects for the bias, minimizes the possible negative impact of experimenting, and greatly accelerates the R&D cycle. This paper is the first of its kind to lays out the theoretical framework along with the real-world example that demonstrates the magnitude of the bias. It serves as a conversation starter for such insidious type of experimentation bias that is likely present in other marketplaces of expirable goods such as vacation nights, car rentals, and airline tickets, concert passes, or ride-hailings.
PRODIGY: Product Design Guidance at Scale

Growth of e-commerce has enabled the creation of thousands of small-scale brands. However, these brands lack information on a) what new products to develop and b) how to refine existing products to improve on business metrics. We present a comprehensive Product Design Insights and Guidance service (named PRODIGY) that mines product attributes data available on e-commerce platforms and surface insights on a) new product development and b) product refinement. Our core contribution is a novel demand forecasting model for product designs based on a notable extension of the recently proposed FTTransformer architecture combined with a self-supervised pre-training task, akin to Masked Language Modeling (MLM) objective. For the product refinement use-case, we present a novel algorithm by embedding the design search in a data-density approximator, namely Conditional Variational Autoencoder. We run a thorough and comprehensive set of experiments and establish that PRODIGY achieves significant improvement in demand prediction as compared to state-of-the-art alternatives. Finally, we present our findings from an online experiment where PRODIGY helps to launch new products with +20% lift in sales and +1.3% lift in product ratings.