Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling

We evaluate the impact of probabilistically-constructed digital identity data collected from Sep. to Dec. 2017 (approx.), in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed identities in marketing. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.Comment: Accepted by WSDM 201

Lion: Adversarial Distillation of Proprietary Large Language Models

The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention. Previous works have focused on a unidirectional knowledge distillation way by aligning the responses of the student model with those of the teacher model to a set of instructions. Nevertheless, they overlooked the possibility of incorporating any reciprocal "feedback"--identifying challenging instructions where the student model's performance falls short--to boost the student model's proficiency iteratively. To this end, we propose a novel adversarial distillation framework for a more efficient knowledge transfer. Leveraging the versatile role adaptability of LLMs, we prompt the teacher model to identify "hard" instructions and generate new "hard" instructions for the student model, creating a three-stage adversarial loop of imitation, discrimination, and generation. By applying this adversarial framework, we successfully transfer knowledge from ChatGPT to a student model (named Lion), using a mere 70k training data. Our results show that Lion-13B not only achieves comparable open-ended generation capabilities to ChatGPT but surpasses conventional state-of-the-art (SOTA) instruction-tuned models like Vicuna-13B by 55.4% in challenging zero-shot reasoning benchmarks such as BIG-Bench Hard (BBH) and 16.7% on AGIEval. Code and model can be found at https://github.com/YJiangcm/Lion.Comment: 21 pages, 5 figures, EMNLP 2023 main conferenc

Percutaneous Closure of Patent Foramen Ovale in a Patient with Mirror-Image Dextrocardia and Situs Inversus

A 26-year-old patient with mirror-image dextrocardia and situs inversus experienced a transient ischemic attack. We suspected that a patent foramen ovale was the reason. A Cardi-O-Fix occluder was used to close the patent foramen ovale with a mirror-reversed rotation of the radiologic views. During the 18-month follow-up, no symptoms of the transient ischemic attack appeared again

The Chain Flexibility Effects on the Self-assembly of Diblock Copolymer in Thin Film

We investigate the effects of chain flexibility on the self-assembly behavior of symmetric diblock copolymers (BCPs) when they are confined as a thin film between two surfaces. Employing worm-like chain (WLC) self-consistent field theory, we study the relative stability of parallel (L$_{\parallel}$) and perpendicular (L$_{\perp}$) orientations of BCP lamellar phases, ranging in chain flexibility from flexible Gaussian chains to semi-flexible and rigid chains. For flat and neutral bounding surfaces (no surface preference for one of the two BCP components), the stability of the L$_{\perp}$ lamellae increases with chain rigidity. When the top surface is flat and the bottom substrate is corrugated, increasing the surface roughness enhances the stability of the L$_{\perp}$ lamellae for flexible Gaussian chains. However, an opposite behavior is observed for rigid chains, where the L$_{\perp}$ stability decreases as the substrate roughness increases. We further show that as the substrate roughness increases, the critical value of the substrate preference, $u^{*}$, corresponding to an L$_{\perp}$-to-L$_{\parallel}$ transition, decreases for rigid chains, while it increases for flexible Gaussian chains. Our results highlight the physical mechanism of tailoring the orientation of lamellar phases in thin-film setups. This is of importance, in particular, for short (semi-flexible or rigid) chains that are in high demand in emerging nanolithography and other industrial applications

Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey

A human 3D avatar is one of the important elements in the metaverse, and the modeling effect directly affects people's visual experience. However, the human body has a complex topology and diverse details, so it is often expensive, time-consuming, and laborious to build a satisfactory model. Recent studies have proposed a novel method, implicit neural representation, which is a continuous representation method and can describe objects with arbitrary topology at arbitrary resolution. Researchers have applied implicit neural representation to human 3D avatar modeling and obtained more excellent results than traditional methods. This paper comprehensively reviews the application of implicit neural representation in human body modeling. First, we introduce three implicit representations of occupancy field, SDF, and NeRF, and make a classification of the literature investigated in this paper. Then the application of implicit modeling methods in the body, hand, and head are compared and analyzed respectively. Finally, we point out the shortcomings of current work and provide available suggestions for researchers.Comment: A Brief Surve

Bulk Density Adjustment of Resin-Based Equivalent Material for Geomechanical Model Test

An equivalent material is of significance to the simulation of prototype rock in geomechanical model test. Researchers attempt to ensure that the bulk density of equivalent material is equal to that of prototype rock. In this work, barite sand was used to increase the bulk density of a resin-based equivalent material. The variation law of the bulk density was revealed in the simulation of a prototype rock of a different bulk density. Over 300 specimens were made for uniaxial compression test. Test results indicated that the substitution of quartz sand by barite sand had no apparent influence on the uniaxial compressive strength and elastic modulus of the specimens but can increase the bulk density, according to the proportional coarse aggregate content. An ideal linearity was found in the relationship between the barite sand substitution ratio and the bulk density. The relationship between the bulk density and the usage of coarse aggregate and barite sand was also presented. The test results provided an insight into the bulk density adjustment of resin-based equivalent materials

Automated Evaluation of Personalized Text Generation using Large Language Models

Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context. While the research progress in this area has been rapid, evaluation still presents a challenge. Traditional automated metrics such as BLEU and ROUGE primarily measure lexical similarity to human-written references, and are not able to distinguish personalization from other subtle semantic aspects, thus falling short of capturing the nuances of personalized generated content quality. On the other hand, human judgments are costly to obtain, especially in the realm of personalized evaluation. Inspired by these challenges, we explore the use of large language models (LLMs) for evaluating personalized text generation, and examine their ability to understand nuanced user context. We present AuPEL, a novel evaluation method that distills three major semantic aspects of the generated text: personalization, quality and relevance, and automatically measures these aspects. To validate the effectiveness of AuPEL, we design carefully controlled experiments and compare the accuracy of the evaluation judgments made by LLMs versus that of judgements made by human annotators, and conduct rigorous analyses of the consistency and sensitivity of the proposed metric. We find that, compared to existing evaluation metrics, AuPEL not only distinguishes and ranks models based on their personalization abilities more accurately, but also presents commendable consistency and efficiency for this task. Our work suggests that using LLMs as the evaluators of personalized text generation is superior to traditional text similarity metrics, even though interesting new challenges still remain