Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms and Theory for Supervised Gradual Domain Adaptation

The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of supervised gradual domain adaptation, where labeled data from shifting distributions are available to the learner along the trajectory, and we aim to learn a classifier on a target data distribution of interest. Under this setting, we provide the first generalization upper bound on the learning error under mild assumptions. Our results are algorithm agnostic, general for a range of loss functions, and only depend linearly on the averaged learning error across the trajectory. This shows significant improvement compared to the previous upper bound for unsupervised gradual domain adaptation, where the learning error on the target domain depends exponentially on the initial error on the source domain. Compared with the offline setting of learning from multiple domains, our results also suggest the potential benefits of the temporal structure among different domains in adapting to the target one. Empirically, our theoretical results imply that learning proper representations across the domains will effectively mitigate the learning errors. Motivated by these theoretical insights, we propose a min-max learning objective to learn the representation and classifier simultaneously. Experimental results on both semi-synthetic and large-scale real datasets corroborate our findings and demonstrate the effectiveness of our objectives.
Attitudes towards and use of ebooks at the University of Canterbury

Research Problem: There is a contradiction surrounding ebooks in that they are becoming more and more common in academic libraries but evidence often suggests that users still prefer print books to ebooks. The purpose of the study is to examine how users at the University of Canterbury are using the Library’s ebooks and what their attitudes towards ebooks are. The study also looks at what role age, gender, academic status and college affiliation play in shaping attitudes towards and use of ebooks. Methodology: The study used an online survey to discover how ebooks were used and viewed by users at the University of Canterbury. The survey was largely quantitative but included several comments sections where users could give more qualitative answers. The population sampled was the academic staff and PhD students of the University of Canterbury. Results: The results show that the participants are mostly aware of and using ebooks. Opinion is still divided on ebooks with some user still preferring print and many users preferring access to both print and ebooks. Age, gender, academic status and college affiliation all have some effect on attitudes towards and use of ebooks. Implications: Academic libraries need to take note of the opinions their users have about ebooks so as to better meet their needs. Some of the problems around ebook use can be solved by increased user education but others are the result of restrictions placed on ebooks by publishers and vendors. Other problems are inherent to the ebook format and cannot be ignored. Academic libraries can best meet their users’ needs by providing both print and ebook collections were possible.
Tensorial tomographic differential phase-contrast microscopy

We report Tensorial Tomographic Differential Phase-Contrast microscopy (T2DPC), a quantitative label-free tomographic imaging method for simultaneous measurement of phase and anisotropy. T2DPC extends differential phase-contrast microscopy, a quantitative phase imaging technique, to highlight the vectorial nature of light. The method solves for permittivity tensor of anisotropic samples from intensity measurements acquired with a standard microscope equipped with an LED matrix, a circular polarizer, and a polarization-sensitive camera. We demonstrate accurate volumetric reconstructions of refractive index, birefringence, and orientation for various validation samples, and show that the reconstructed polarization structures of a biological specimen are predictive of pathology.
$\phi$ meson properties in nuclear matter from QCD sum rules with chirally separated four-quark condensates

The modiﬁcation of the φ meson spectrum in nuclear matter is studied in an updated QCD sum rule analysis, taking into account recent improvements in properly treating the chiral invariant and breaking components of four-quark condensates. Allowing both mass and decay width to change at ﬁnite density, the QCD sum rule analysis determines certain combinations of changes for these parameters that satisfy the sum rules equally well. A comprehensive error analysis, including uncertainties related to the behavior of various condensates at linear order in density, the employed renormalization scale and perturbative corrections of the Wilson coeﬃcients, is used to compute the allowed ranges of these parameter combinations. We ﬁnd that the φ meson mass shift in nuclear matter is especially sensitive to the strange sigma term σ sN , which determines the decrease of the strange quark condensate in nuclear matter. Speciﬁcally, we obtain a linear relation between the width Γ φ and mass shift ∆ m φ given as Γ φ = a ∆ m φ + bσ sN + c with a = (3 . 947 +0 . 139 − 0 . 130 ), b = (0 . 936 +0 . 180 − 0 . 177 ) and c = − (7 . 707 +4 . 791 − 5 . 679 )MeV.
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee

This paper is motivated by addressing open questions in distributionally robust chance-constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically, the computational techniques for those programs typically place restrictive assumptions on the constraint functions. The size of the Wasserstein ambiguity sets is often set using costly cross-validation (CV) procedures or conservative measure concentration bounds. In contrast, we propose a practical DRCCP algorithm using kernel maximum mean discrepancy (MMD) ambiguity sets, which we term MMD-DRCCP, to treat general nonlinear constraints without using ad-hoc reformulation techniques. MMD-DRCCP can handle general nonlinear and non-convex constraints with a proven finite-sample constraint satisfaction guarantee of a dimension-independent ${\mathcal{O}}\left( {\frac{1}{{\sqrt N }}} \right)$ rate, achievable by a practical algorithm. We further propose an efficient bootstrap scheme for constructing sharp MMD ambiguity sets in practice without resorting to CV. Our algorithm is validated numerically on a portfolio optimization problem and a tube-based distributionally robust model predictive control problem with non-convex constraints.
High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

Questing for learned lossy image coding (LIC) with superior compression performance and computation throughput is challenging. The vital factor behind it is how to intelligently explore Adaptive Neighborhood Information Aggregation (ANIA) in transform and entropy coding modules. To this end, Integrated Convolution and Self-Attention (ICSA) unit is first proposed to form a content-adaptive transform to characterize and embed neighborhood information dynamically of any input. Then a Multistage Context Model (MCM) is devised to progressively use available neighbors following a pre-arranged spatial-channel order for accurate probability estimation in parallel. ICSA and MCM are stacked under a Variational AutoEncoder (VAE) architecture to derive rate-distortion optimized compact representation of input image via end-to-end learning. Our method reports state-of-the-art compression performance surpassing the VVC Intra and other prevalent LIC approaches across Kodak, CLIC, and Tecnick datasets; More importantly, our method offers $>$60$\times$ decoding speedup using a comparable-size model when compared with the most popular LIC method. All materials are made publicly accessible at https://njuvision.github. io/TinyLIC for reproducible research.
Ab Initio Simulation of Amorphous Graphite.

An amorphous graphite material has been predicted from molecular dynamics simulation using ab initio methods. Carbon materials reveal a strong proclivity to convert into a sp^{2} network and then layer at temperatures near 3000 K within a density range of ca. 2.2-2.8 g/cm^{3}. Each layer of amorphous graphite is a monolayer of amorphous graphene including pentagons and heptagons in addition to hexagons, and the planes are separated by about 3.1 Å. The layering transition has been studied using various structural and dynamical analyses. The transition is unique as one of partial ordering (long range order of planes and galleries, but topological disorder in the planes). The planes are quite flat, even though monolayer amorphous graphene puckers near pentagonal sites. Interplane cohesion is due partly to non-Van der Waals interactions. The structural disorder has been studied closely, especially the consequences of disorder to electronic transport. It is expected that the transition elucidated here may be salient to other layered materials.
Multi-Agent Systems: Technical & Ethical Challenges of Functioning in a Mixed Group

Abstract In today's highly interconnected, open-networked computing world, artificial intelligence computer agents increasingly interact in groups with each other and with people both virtually and in the physical world. AI's current core challenges concern determining ways to build AI systems that function effectively and safely for people and the societies in which they live. To incorporate reasoning about people, research in multi-agent systems has engendered paradigmatic shifts in computer-agent design, models, and methods, as well as the development of new representations of information about agents and their environments. These changes have raised technical as well as ethical and societal challenges. This essay describes technical advances in computer-agent representations, decision-making, reasoning, and learning methods and highlights some paramount ethical challenges.
Shared N417-Dependent Epitope on the SARS-CoV-2 Omicron, Beta, and Delta Plus Variants

The evolution of SARS-CoV-2 has resulted in variants of concern (VOCs) with distinct spike mutations conferring various immune escape profiles. These variable mutations also influence the cross-reactivity of the antibody response mounted by individuals infected with each of these variants. ABSTRACT As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve, several variants of concern (VOCs) have arisen which are defined by multiple mutations in their spike proteins. These VOCs have shown variable escape from antibody responses and have been shown to trigger qualitatively different antibody responses during infection. By studying plasma from individuals infected with either the original D614G, Beta, or Delta variants, we showed that the Beta and Delta variants elicit antibody responses that are overall more cross-reactive than those triggered by D614G. Patterns of cross-reactivity varied, and the Beta and Delta variants did not elicit cross-reactive responses to each other. However, Beta-elicited plasma was highly cross-reactive against Delta Plus (Delta+), which differs from Delta by a single K417N mutation in the receptor binding domain, suggesting that the plasma response targets the N417 residue. To probe this further, we isolated monoclonal antibodies from a Beta-infected individual with plasma responses against Beta, Delta+, and Omicron, which all possess the N417 residue. We isolated an N417-dependent antibody, 084-7D, which showed similar neutralization breadth to the plasma. The 084-7D MAb utilized the IGHV3-23*01 germ line gene and had somatic hypermutations similar to those of previously described public antibodies which target the 417 residue. Thus, we have identified a novel antibody which targets a shared epitope found on three distinct VOCs, enabling their cross-neutralization. Understanding antibodies targeting escape mutations, such as K417N, which repeatedly emerge through convergent evolution in SARS-CoV-2 variants, may aid in the development of next-generation antibody therapeutics and vaccines. IMPORTANCE The evolution of SARS-CoV-2 has resulted in variants of concern (VOCs) with distinct spike mutations conferring various immune escape profiles. These variable mutations also influence the cross-reactivity of the antibody response mounted by individuals infected with each of these variants. This study sought to understand the antibody responses elicited by different SARS-CoV-2 variants and to define shared epitopes. We show that Beta and Delta infections resulted in antibody responses that were more cross-reactive than the original D614G variant, but they had differing patterns of cross-reactivity. We further isolated an antibody from Beta infection which targeted the N417 site, enabling cross-neutralization of Beta, Delta+, and Omicron, all of which possess this residue. The discovery of antibodies which target escape mutations common to multiple variants highlights conserved epitopes to target in future vaccines and therapeutics.
Monthly excess mortality across counties in the United States during the Covid-19 pandemic, March 2020 to February 2022

Official Covid-19 death tallies have under-estimated the total mortality impact of the Covid-19 pandemic. Excess mortality is a useful measure for assessing the total effects of the pandemic on mortality levels. While most studies of excess mortality in the United States have taken place at the state or national levels, some studies have produced county-level estimates. However, no estimates are currently available for the period from 2020 to 2021. In the present study, we estimate two hierarchical linear models to predict county-level death rates using mortality data by county of residence from January 2015 to December 2019. After fitting the models, we generate estimates of expected deaths for each month in the period from March 2020 to December 2021 and compare these estimates with the observed number of deaths to obtain counts of excess deaths. An estimated 936,911 excess deaths occurred during 2020 and 2021, of which 171,168 were not assigned to Covid-19 on death certificates. In the Far West, Great Lakes, Mideast, and New England, there was a substantial urban mortality disadvantage in 2020, which was reversed in 2021 to yield a rural mortality disadvantage. In the Southeast, Southwest, Rocky Mountain, and Plains regions, there was a rural mortality disadvantage in 2020, which was exacerbated in 2021. The proportion of excess deaths assigned to Covid-19 was lower in 2020 (76.3%) than in 2021 (87.0%), suggesting fewer Covid-19 deaths went unassigned later in the pandemic. However, in rural areas and in the Southeast and Southwest many excess deaths were still not assigned to Covid-19 during 2021.
AC/AC power conversion based on matrix converter topology with unidirectional switches

In this paper, a new type of AC/AC power converter is proposed. The proposed converter is capable of direct AC/AC power conversion and, except for a few small snubber elements, it does not require the use of any input inductors or a DC-link capacitor. In contrast to the matrix converter, which requires bidirectional switches, the proposed converter consists of only unidirectional switches such as insulated gate bipolar transistors. The converter has a unity input displacement power factor, and its input line current waveform is similar to that of a diode rectifier with a DC-link inductor. The proposed converter has been validated experimentally using an induction motor driven with field-oriented control.
The effect of matrix interferences on in situ boron isotope analysis by laser ablation MC-ICP-MS

Rationale: Boron isotope analysis of marine carbonates by laser ablation multicollector inductively coupled plasma mass spectrometry (LA-MC-ICP-MS) offers the potential for rapid sample throughput, and the means to examine micron scale variations in the δ 11 B signatures of fossil skeletons and shells/tests of marine organisms. Existing studies demonstrate an acceptable level of reproducibility is achievable, but also typically show a level of accuracy outside the limits required by most applications. Here we investigate matrix interference effects as a cause of inaccuracy and imprecision. Methods: Analyses were performed on a standard format Thermo Scientific Neptune Plus MC-ICP mass spectrometer coupled to a New Wave Research 193nm ArF laser ablation system. The effects of matrix interference on δ 11 B analysis were investigated through analyses of a set of reference materials with differing B/Ca ratios. Three approaches to correct for matrix-induced effects were Conclusions: LA-MC-ICP-MS can produce accurate and precise δ 11 B data to a 0.5‰ (2 σ ) level on <0.3 ng B after correction for Ca interference effects.
Knowledge Graph Enhanced Relation Extraction

Relation Extraction (RE) is the task of predicting a relation between a subject and object in a sentence, while knowledge graph link prediction (KGLP) infers a set of objects — O , given a subject and a relation from a knowledge graph. These two problems are closely intertwined: given a sentence consisting of a subject and object — o , a RE model estimates a relation that a KGLP model (together with the subject) may use to infer a set of objects — O — that contains o . In this paper, we leverage this insight by proposing a multi-task learning framework that enhances RE models by jointly training on both RE and KGLP tasks. We illustrate the generality of our approach by applying it on three existing RE methods and achieve consistent improvements across our benchmark datasets.
Traumatic Rupture of the Diaphragm: Retrospective Study of 27 Cases Operated in Three Hospitals in Yaound&#233 (Cameroon)

Background: Traumatic ruptures of the diaphragm (TRD) are serious lesions that are often part of polytrauma. They pose a real diagnostic and therapeutic challenge in a disadvantaged environment such as ours. Methods: We conducted a descriptive observational study covering a period of 11 years in 3 referral hospitals in the city of Yaoundé (Cameroon). All patients who had un-dergone surgery for a traumatic rupture of the diaphragm between January 1, 2011 and December 31, 2020, and whose outcome within 30 days of surgery was known were included. Results: A total of 27 patients were collected. Their mean age was 36.4 ± 19.7 years. There was a strong male predominance with 22 cases (81.5%). The TRD occurred mainly after an assault (n = 9, 33.3%), was mainly on the left side (n = 25, 92.6%) and was most often part of a polytrauma (n = 17, 62.9%). The lesions associated with TRD were mainly visceral (n =
A numerical approach to assess the capacity of rock-socketed piles subjected to axial compression in Mumbai region based on the pile load test data

The best way to analyse the load–settlement response of a rock-socketed pile is by conducting a load test. However, due to support reaction and time limitation, it is not possible to conduct these tests up to failure. In this paper, a numerical approach to assess the interpreted pile failure load is presented. The load–settlement behaviour of piles socketed in rocks of Mumbai region is studied for 160 cases, out of which, 50 cases are presented here. The load–settlement data obtained from the non-failed tests are extrapolated using Paikowsky and Tolosko’s method. The pile capacity is then estimated using different methods available from the literature. The suitability of the interpretation methods is also discussed.
Variance Risk Premia in Emerging Markets

Research on emerging market variance risk premia has been difficult due to the short length of data. Adopting the sample-extending methodology in Lynch and Wachter (2013), we construct country level variance risk premia for 9 important emerging markets, including China, India, Korea, Mexico, South Africa, Russia, Poland, Taiwan and Brazil, over 2000 to 2017. We find that both developed market and emerging market components of the variance risk premia predict emerging market returns, currency returns and capital inflows to emerging countries. This finding suggests that the variance risk premia is a global systematic predictor for various returns, even in the emerging markets. Interesting, the predictive power of the developed market variance risk premia differs from its component from emerging market, with the latter being more significant over longer horizons, which indicates different information content of these two components. JEL classification: G12, G13, G15.
Towards a green optical Internet

Increasing electricity consumption significantly affects all nations from environmental, social, and economic perspectives. Among the contributors to this problem is rising power consumption of the Internet’s backbone network due to expanding Internet traffic volumes. This thesis addresses the power consumption minimization problem of the Internet's backbone network. Existing solutions to reduce power consumption are identified and categorized into four approaches of network redesign, traffic engineering, power-aware networking, and load-adaptive operation. Network redesign solutions either modify core nodes by incorporating advanced devices or optimize the physical link topology. Traffic engineering solutions aggregate multiple traffic flows into fewer streams using wavelength/waveband grooming. Power-aware networking solutions vary the operating states of network devices according to traffic variability. Load-adaptive operation solutions use high-rate network devices with Mixed Line Rates (MLR) to serve traffic. To reduce power consumption of an Optical Circuit Switched (OCS) network, each chapter derives solutions from one or more approaches. First, network redesign and traffic engineering solutions are combined to develop a novel sparse grooming algorithm. The proposed algorithm selectively places core routers to reduce power consumption by 688 kW over a traditional network. Second, network redesign and power-aware networking solutions are integrated to develop two novel strategies that use different algorithms to reduce cost and power consumption of a survivable network. Cost is reduced by 40% and power consumption is reduced by 1.3 MWh in a day. Third, the effect of grouping policy and band size and configuration design parameters on power consumption of a waveband-groomed network is analyzed. Proposed algorithms show that power consumption of this Band Switched Network (BSN) can be reduced in different traffic environments by using specific design parameters. Fourth, traffic engineering and load-adaptive operation solutions are combined to develop three new schemes and two algorithms that assign line rates and wavelengths/wavebands in the network, respectively. Power consumption of this MLR-based BSN reduces by 80% and 17% over two Single Line Rate (SLR)-based traditional networks. Lastly, solutions from network redesign and power-aware networking are integrated to develop a novel burst assembly scheme that reduces power consumption of a sleep-mode-enabled Optical Burst Switched (OBS) network by approximately 0.5 kW than when traditional schemes are used.