How to Forget Clients in Federated Online Learning to Rank?

Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contributions made using their data to be removed from learned models. In this paper, we study how to remove the contributions made by a client participating in a Federated Online Learning to Rank (FOLTR) system. In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model. Local updates are learned in an online manner at a client-level using queries and implicit interactions that have occurred within that specific client. By doing so, each client's local data is not shared with other clients or with a centralised search service, while at the same time clients can benefit from an effective global ranking model learned from contributions of each client in the federation. In this paper, we study an effective and efficient unlearning method that can remove a client's contribution without compromising the overall ranker effectiveness and without needing to retrain the global ranker from scratch. A key challenge is how to measure whether the model has unlearned the contributions from the client $c^*$ that has requested removal. For this, we instruct $c^*$ to perform a poisoning attack (add noise to this client updates) and then we measure whether the impact of the attack is lessened when the unlearning process has taken place. Through experiments on four datasets, we demonstrate the effectiveness and efficiency of the unlearning strategy under different combinations of parameter settings.Comment: Accepted in ECIR 202

Development of Low-Temperature Photon Scanning Probe Microscopy and Nanoscale Characterization of Ultrathin ZnO Layers on Ag(111)

Abstract Photon scanning probe microscopy (photon-SPM) provides a promising route to study a lightmatter interaction at the nanometer scale and even down to the single-molecule level, which is an interesting topic not only for fundamental science, but also for a new evolution of nanotechnology. This thesis describes the development of a home-designed low-temperature (LT-) photon-SPM, which combines a parabolic mirror and a lens on the cold STM stage. We demonstrate that this instrument offers a precise beam alignment capability to attain highly reproducible experiments. Using the LT-photon-SPM, we first show a novel plasmon-assisted resonant electron transfer in an scanning tunneling microscope (STM) junction, where resonant electron transfer from a plasmonic tip to field emission resonances (FERs) over a Ag(111) surface is induced by visivble continuous-wave excitation. This process can serve as a simple and intriguing model to examine the interplay between localized surface plasmon excitation and resonant electron transfer in a plasmonic nanocavity. The resonant electron transfer is observed in FER spectroscopy and the plasmon-assisted process is manifested as a downshift of the FER peaks in the spectra. We also examined tip-enhanced Raman spectroscopy (TERS) for ultrathin ZnO layers epitaxially grown on a Ag(111) surface. The local geometric and electronic structure of ZnO/Ag(111) is investigated by combined experiments of STM, STS, and atomic force microscopy. With increasing thickness of the ZnO layers, the conduction band minimum was found to downshift as well as the work function was reduced. Strong TERS signals for 2-ML and 3-ML ZnO were obtained under the conditions where both chemical and physical enhancement mechanisms were satisfied. It is also revealed that the TERS intensity is sensitive to the local electronic structure leading to a high spatial resolution of TERS is below 1 nm.Kurzfassung Die Photon-Scanning-Probe-Mikroskopie (Photon-SPM) bietet eine vielversprechende Möglichkeit, die Wechselwirkung zwischen Licht und Materie im Nanometerbereich oder sogar bis auf die Ebene einzelner Moleküle zu untersuchen. Dies ist sowohl für die Grundlagenforschung als auch für neue technologische Anwendungen interessant. In dieser Arbeit wurde ein selbstgebautes Tieftemperatur (LT-) Photon-SPM entwickelt und dessen neuartigen Fähigkeiten demonstriert. In das LT-Photon-SPM wurde ein Parabolspiegel mit präziser Bewegungssteuerung integriert, der durch Piezoelemente auf dem kalten SPM-Tisch gesteuert werden und dadurch eine hochwertige und bequeme Ausrichtungsmöglichkeit für die Durchführung reproduzierbare Experimente bietet. Mit dem LT-Photon-STM wurde ein neuartiger resonanter Elektronentransfermechanismus in einer plasmonischen Nanokavität entdeckt, bei dem plasmonisch unterstütztes Elektronentunneln von einer plasmonischen Spitze zu Feldemissionsresonanzen über der Ag(111)-Oberfläche durch CW-Laseranregung im sichtbaren Bereich induziert wird. Korrelationen zwischen der laserinduzierten Änderung der FER-Spektren und den plasmonischen Eigenschaften des Übergangs wurden untersucht. Als Kennzeichen eines plasmonunterstützten resonanten Tunnelprozesses wurde ein Herabschieben des ersten Peaks in den FER-Spektren beobachtet, die der einfallenden Photonenenergie entspricht. Ebenfalls wurde die spitzenverstärkte Raman-Spektroskopie für ultradünne ZnOSchichten untersucht, die epitaktisch auf einer Ag(111)-Oberfläche gewachsen wurden. Die lokale geometrische und elektronische Struktur von ZnO/Ag(111) wurde durch kombinierte Experimente mit STM, STS und Rasterkraftmikroskopie untersucht. Mit zunehmender Dicke der ZnO-Schichten wurde festgestellt, dass sich die Position des Leitungsbandminimum energetisch verringerte, genauso wie die Austrittsarbeit. Starke TERS-Signale für 2-ML- und 3-ML-ZnO blieben unter Bedingungen erhalten, bei denen sowohl chemische als auch physikalische Verstärkungsmechanismen erfüllt sind. Es hat sich auch gezeigt, dass die TERSIntensität empfindlich gegenüber der lokalen elektronischen Struktur ist, was dazu führt, dass die hohe räumliche Auflösung von TERS unter 1 nm liegt

Do land markets improve land-use efficiency? evidence from Jiangsu, China

Inefficient use of scarce and fragmented land challenges the sustainability of agriculture. Land markets may improve land-use efficiency. In recent years, China has employed various instruments to promote land markets. This paper investigates whether land markets affect households' land-use efficiency, based on data from 1,202 farm households in Jiangsu Province. The measure of land-use efficiency was derived from a stochastic frontier production function, and a control function approach was employed to correct for selection bias. The results indicated that many households are using land inefficiently. While renting in land increases land-use efficiency, it is not affected by renting out land, implying that households are not giving up land for efficiency gains. We also provide suggestive evidence that the positive effect of renting in land results from abundant agricultural labour due to labour market failure

Illinois Fire Service Institute Library Initiatives During the COVID-19 Pandemic

The Illinois Fire Service Institute (IFSI) Library provides fire and emergency library and information assistance and services to the Institute’s instructional staff, students, Illinois fire departments and firefighters, and other fire/emergency-related users in the successful and effective performance of their jobs. In response to the COVID pandemic, IFSI Librarians have developed new services and resources to continue serving patrons. At the same time, new procedures and services were adopted. With the staff’s return to the library’s physical location, IFSI’s Learning Resource and Research Center building, new COVID-19-related safety measures have been instituted. IFSI Librarians worked with IFSI staff to create the COVID-19 Archives Collection to preserve important documents about the pandemic as it occurs. The Library received grant awards respectively from IMLS and ALA. During the pandemic, the IFSI International Programs continued to provide information resources and access to international users. The Library organized online academic activities via Zoom on a variety of workshops, lectures, and discussions to ensure that users were able to receive enough resources to continue their study and research

Note on off-shell relations in nonlinear sigma model

In this note, we investigate relations between tree-level off-shell currents in nonlinear sigma model. Under Cayley parametrization, all odd-point currents vanish. We propose and prove a generalized $U(1)$ identity for even-point currents. The off-shell $U(1)$ identity given in [1] is a special case of the generalized identity studied in this note. The on-shell limit of this identity is equivalent with the on-shell KK relation. Thus this relation provides the full off-shell correspondence of tree-level KK relation in nonlinear sigma model.Comment: 28 pages, 1 table, 11 figures, improved versio

API-Assisted Code Generation for Question Answering on Varied Table Structures

A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms. In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2) uses Python as a powerful querying language, and (3) uses few-shot prompting to translate NL questions into Python programs, which are executable on Pandas data frames. Furthermore, to answer complex relational questions with extended program functionality and external knowledge, our framework allows customized APIs that Python programs can call. We experiment with four TableQA datasets that involve tables of different structures -- relational, multi-table, and hierarchical matrix shapes -- and achieve prominent improvements over past state-of-the-art systems. In ablation studies, we (1) show benefits from our multi-index representation and APIs over baselines that use only an LLM, and (2) demonstrate that our approach is modular and can incorporate additional APIs.Comment: EMNLP 2023 camera ready, 13 pages, 11 figure

Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences

Cloth-Changing Person Re-Identification (CC-ReID) is a common and realistic problem since fashion constantly changes over time and people's aesthetic preferences are not set in stone. While most existing cloth-changing ReID methods focus on learning cloth-agnostic identity representations from coarse semantic cues (e.g. silhouettes and part segmentation maps), they neglect the continuous shape distributions at the pixel level. In this paper, we propose Continuous Surface Correspondence Learning (CSCL), a new shape embedding paradigm for cloth-changing ReID. CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings. We further extract fine-grained shape features from the learned surface embeddings and then integrate them with global RGB features via a carefully designed cross-modality fusion module. The shape embedding paradigm based on 2D-3D correspondences remarkably enhances the model's global understanding of human body shape. To promote the study of ReID under clothing change, we construct 3D Dense Persons (DP3D), which is the first large-scale cloth-changing ReID dataset that provides densely annotated 2D-3D correspondences and a precise 3D mesh for each person image, while containing diverse cloth-changing cases over all four seasons. Experiments on both cloth-changing and cloth-consistent ReID benchmarks validate the effectiveness of our method.Comment: Accepted by ACM MM 202

M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images

The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones for this task, which ignore the inherent multi-scale hierarchical data structure of digital pathology images. To address this limit, we propose M2ORT, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images through a decoupled multi-scale feature extractor. Different from traditional models that are trained with one-to-one image-label pairs, M2ORT accepts multiple pathology images of different magnifications at a time to jointly predict the gene expressions at their corresponding common ST spot, aiming at learning a many-to-one relationship through training. We have tested M2ORT on three public ST datasets and the experimental results show that M2ORT can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs). The code is available at: https://github.com/Dootmaan/M2ORT/