Policy-Gradient Training of Language Models for Ranking

Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answering systems. Current state-of-the-art text retrieval models leverage pre-trained large language models (LLMs) to achieve competitive performance, but training LLM-based retrievers via typical contrastive losses requires intricate heuristics, including selecting hard negatives and using additional supervision as learning signals. This reliance on heuristics stems from the fact that the contrastive loss itself is heuristic and does not directly optimize the downstream metrics of decision quality at the end of the processing pipeline. To address this issue, we introduce Neural PG-RANK, a novel training algorithm that learns to rank by instantiating a LLM as a Plackett-Luce ranking policy. Neural PG-RANK provides a principled method for end-to-end training of retrieval models as part of larger decision systems via policy gradient, with little reliance on complex heuristics, and it effectively unifies the training objective with downstream decision-making quality. We conduct extensive experiments on various text retrieval benchmarks. The results demonstrate that when the training objective aligns with the evaluation setup, Neural PG-RANK yields remarkable in-domain performance improvement, with substantial out-of-domain generalization to some critical datasets employed in downstream question answering tasks

Differential effects of partial and complete loss of TREM2 on microglial injury response and tauopathy.

Alzheimer's disease (AD), the most common form of dementia, is characterized by the abnormal accumulation of amyloid plaques and hyperphosphorylated tau aggregates, as well as microgliosis. Hemizygous missense variants in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with elevated risk for developing late-onset AD. These variants are hypothesized to result in loss of function, mimicking TREM2 haploinsufficiency. However, the consequences of TREM2 haploinsufficiency on tau pathology and microglial function remain unknown. We report the effects of partial and complete loss of TREM2 on microglial function and tau-associated deficits. In vivo imaging revealed that microglia from aged TREM2-haploinsufficient mice show a greater impairment in their injury response compared with microglia from aged TREM2-KO mice. In transgenic mice expressing mutant human tau, TREM2 haploinsufficiency, but not complete loss of TREM2, increased tau pathology. In addition, whereas complete TREM2 deficiency protected against tau-mediated microglial activation and atrophy, TREM2 haploinsufficiency elevated expression of proinflammatory markers and exacerbated atrophy at a late stage of disease. The differential effects of partial and complete loss of TREM2 on microglial function and tau pathology provide important insights into the critical role of TREM2 in AD pathogenesis

Attribution and mitigation of heat wave-induced urban heat storage change

When the urban heat island (UHI) effect coincides with a heat wave (HW), thermal comfort conditions in cities are exacerbated. Understanding the surface energy balance (SEB) responses to HWs is critical for improving predictions of the synergies between UHIs and HWs. This study evaluates observed SEB characteristics in four cities (Beijing, Łódź, London and Swindon), along with their ambient meteorological conditions, for both HW and background summer climate (BC) scenarios. Using the Analytical Objective Hysteresis Model (AnOHM), particular emphasis is on the heat storage. The results demonstrate that in London and Swindon not only the amount of daytime heat storage but also its portion relative to the net all-wave radiation increase under HWs. Results further demonstrate that such increases are strongly tied to lower wind speeds. The effects of different UHI mitigation measures on heat storage are assessed using AnOHM. Results reveal that using reflective materials and maintaining higher soil moisture availability may offset the adverse effects of increased heat storage

From bibliometric analysis: 3D printing design strategies and battery applications with a focus on zinc-ion batteries

Three-dimensional (3D) printing has the potential to revolutionize the way energy storage devices are designed and manufactured. In this paper, we explore the use of 3D printing in the design and production of energy storage devices, especially zinc-ion batteries (ZIBs) and examine its potential advantages over traditional manufacturing methods. 3D printing could significantly improve the customization of ZIBs, making it a promising strategy for the future of energy storage. In particular, 3D printing allows for the creation of complex, customized geometries, and designs that can optimize the energy density, power density, and overall performance of batteries. Simultaneously, we discuss and compare the impact of 3D printing design strategies based on different configurations of film, interdigitation, and framework on energy storage devices with a focus on ZIBs. Additionally, 3D printing enables the rapid prototyping and production of batteries, reducing leading times and costs compared with traditional manufacturing methods. However, there are also challenges and limitations to consider, such as the need for further development of suitable 3D printing materials and processes for energy storage applications

Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction

We present a novel approach for modeling vegetation response to weather in Europe as measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus on photorealistic quality of the multispectral images, while derived vegetation dynamics have not yet received as much attention. We leverage both spatial and temporal context by extending state-of-the-art video prediction methods with weather guidance. We extend the EarthNet2021 dataset to be suitable for vegetation modeling by introducing a learned cloud mask and an appropriate evaluation scheme. Qualitative and quantitative experiments demonstrate superior performance of our approach over a wide variety of baseline methods, including leading approaches to satellite imagery forecasting. Additionally, we show how our modeled vegetation dynamics can be leveraged in a downstream task: inferring gross primary productivity for carbon monitoring. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predictive assessments of vegetation status.Comment: Source code available at https://github.com/earthnet2021/earthnet-models-pytorc

Retrieval of Precise Radial Velocities from Near-Infrared High Resolution Spectra of Low Mass Stars

Given that low-mass stars have intrinsically low luminosities at optical wavelengths and a propensity for stellar activity, it is advantageous for radial velocity (RV) surveys of these objects to use near-infrared (NIR) wavelengths. In this work we describe and test a novel RV extraction pipeline dedicated to retrieving RVs from low mass stars using NIR spectra taken by the CSHELL spectrograph at the NASA Infrared Telescope Facility, where a methane isotopologue gas cell is used for wavelength calibration. The pipeline minimizes the residuals between the observations and a spectral model composed of templates for the target star, the gas cell, and atmospheric telluric absorption; models of the line spread function, continuum curvature, and sinusoidal fringing; and a parameterization of the wavelength solution. The stellar template is derived iteratively from the science observations themselves without a need for separate observations dedicated to retrieving it. Despite limitations from CSHELL's narrow wavelength range and instrumental systematics, we are able to (1) obtain an RV precision of 35 m/s for the RV standard star GJ 15 A over a time baseline of 817 days, reaching the photon noise limit for our attained SNR, (2) achieve ~3 m/s RV precision for the M giant SV Peg over a baseline of several days and confirm its long-term RV trend due to stellar pulsations, as well as obtain nightly noise floors of ~2 - 6 m/s, and (3) show that our data are consistent with the known masses, periods, and orbital eccentricities of the two most massive planets orbiting GJ 876. Future applications of our pipeline to RV surveys using the next generation of NIR spectrographs, such as iSHELL, will enable the potential detection of Super-Earths and Mini-Neptunes in the habitable zones of M dwarfs.Comment: 64 pages, 28 figures, 5 tables. Accepted for publication in PAS

The PDF4LHC report on PDFs and LHC data: Results from Run I and preparation for Run II

The accurate determination of the Parton Distribution Functions (PDFs) of the proton is an essential ingredient of the Large Hadron Collider (LHC) program. PDF uncertainties impact a wide range of processes, from Higgs boson characterisation and precision Standard Model measurements to New Physics searches. A major recent development in modern PDF analyses has been to exploit the wealth of new information contained in precision measurements from the LHC Run I, as well as progress in tools and methods to include these data in PDF fits. In this report we summarise the information that PDF-sensitive measurements at the LHC have provided so far, and review the prospects for further constraining PDFs with data from the recently started Run II. This document aims to provide useful input to the LHC collaborations to prioritise their PDF-sensitive measurements at Run II, as well as a comprehensive reference for the PDF-fitting collaborations.Comment: 55 pages, 13 figure

Banning diesel vehicles in London: Is 2040 too Late?

Air pollution contributes to 9400 deaths annually in London and diesel vehicles are considered a major source of lethal air pollutants. Consequently, the UK government announced its intention to ban diesel vehicles by 2040 to achieve a sustainable zero-carbon road transport system. Since no empirical studies have used a bottom-up approach to seek Londoners’ views, it is therefore worth investigating the public opinion regarding this forthcoming ban. This paper aims to fill this research gap by taking London as a case study. A survey was designed, and fieldwork was conducted to distribute questionnaires to Londoners. Completed questionnaires were analysed using both quantitative and qualitative methods. The findings revealed that the majority of Londoners would be in favour of the ban if they were sufficiently exposed to the appropriate sources of information and were favourably disposed towards environmental protection measures. The results also showed that Londoners were more likely to switch to electric vehicles (EVs) if they were offered generous incentives and encouraged to use scrappage schemes. The present study makes a strong case for enforcing the ban well before 2040. The significance of this research is to provide clearer signals regarding the future of diesel vehicles, which in turn will strengthen the EV policy and uptake