Search for: All records

Causal inference from observational data has attracted considerable attention among researchers. One main obstacle is the handling of confounders. As direct measurement of confounders may not be feasible, recent methods seek to address the confounding bias via proxy variables, i.e., covariates postulated to be conducive to the inference of latent confounders. However, the selected proxies may scramble both confounders and post-treatment variables in practice, which risks biasing the estimation by controlling for variables affected by the treatment. In this paper, we systematically investigate the bias due to latent post-treatment variables, i.e., latent post-treatment bias, in causal effect estimation. Specifically, we first derive the bias when selected proxies scramble both latent confounders and post-treatment variables, which we demonstrate can be arbitrarily bad. We then propose a Confounder-identifiable VAE (CiVAE) to address the bias. Based on a mild assumption that the prior of latent variables that generate the proxy belongs to a general exponential family with at least one invertible sufficient statistic in the factorized part, CiVAE individually identifies latent confounders and latent post-treatment variables up to bijective transformations. We then prove that with individual identification, the intractable disentanglement problem of latent confounders and post-treatment variables can be transformed into a tractable independence test problem despite arbitrary dependence may exist among them. Finally, we prove that the true causal effects can be unbiasedly estimated with transformed confounders inferred by CiVAE. Experiments on both simulated and real-world datasets demonstrate significantly improved robustness of CiVAE. 

Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job seekers and employers, ultimately contributing to the greater good of the society. However, existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. To address the above challenges, we propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph, aiming to fully utilize the pretrained knowledge and reasoning ability to model member/job textual features as well as various member-job relations simultaneously. In the pretraining phase, we propose a heterogeneous ego-graph-based prompting strategy to model and aggregate member/job textual features based on the topological structure around the target member/job node, where entity type embeddings and graph positional embeddings are introduced accordingly to model different entities and their heterogeneous relations. Meanwhile, a proximity-aware attention alignment strategy is designed to dynamically adjust the attention of the PLM on ego-graph node tokens in the prompt, such that the attention can be better aligned with job marketplace semantics. Extensive experiments at LinkedIn demonstrate the effectiveness of PLM4Job. 

α -MnTe is an antiferromagnetic semiconductor with above room temperature TN = 310 K, which is promising for spintronic applications. Recently, it was reported to be an altermagnet, containing bands with momentum-dependent spin splitting; time-resolved experimental probes of MnTe are, therefore, important both for understanding novel magnetic properties and potential device applications. We investigate ultrafast spin dynamics in epitaxial MnTe(001)/InP(111) thin films using pump-probe magneto-optical measurements in the Kerr configuration. At room temperature, we observe an oscillation mode at 55 GHz that does not appear at zero magnetic field. Combining field and polarization dependence, we identify this mode as a magnon, likely originating from inverse stimulated Raman scattering. Magnetic field-dependent oscillations persist up to at least 335 K, which could reflect coupling to known short-range magnetic order in MnTe above TN. Additionally, we observe two optical phonons at 3.6 and 4.2 THz, which broaden and redshift with increasing temperature. 

Recently, there has been growing interest in developing the next-generation recommender systems (RSs) based on pretrained large language models (LLMs). However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously correlated user/item descriptors, ineffective language modeling on user/item data, inefficient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RSs, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics. Accordingly, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text consisting of homogeneous item tokens or vocab tokens to facilitate stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage CLLM4Rec to capture recommendation-related information from noisy user/item content. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on soft+hard prompts established from masked user-item interaction history, where recommendations of multiple items can be generated efficiently without hallucination. 

Ocean sciences in the U.S. remains a field with one of the lowest rates of diversity, having disproportionately low representation from marginalized groups, including Black, Asian, LatinX, Indigenous, and other people of color; LGBTQIA+ individuals; disabled persons; women; those with neurological differences; and those from low-income groups. With equity and inclusion in mind, recent efforts have been made to increase the number of ocean science professionals from marginalized groups through multiple entry points, including internships. However, there still exists a large gap between the diversity found in the general population and the diversity within ocean sciences. Perhaps one reason why this field continues to have lower diversity owes to the unique component of many oceanographic careers, which continues to present an especially high barrier for marginalized groups: participating in sea-going research expeditions. Herein, we have synthesized possible ways to prioritize the physical and emotional safety of marginalized ocean science professionals participating in a research expedition, including guidance on preparation, implementation, and providing support post-cruise. These suggestions are intended to be useful for the broader oceanographic research community to consider the safety and well-being of individuals from marginalized groups at sea, since the field of ocean sciences - like all fields - would greatly benefit from increased representation and diversity.