Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised Salient Object Detection with Effective Confidence Estimation

The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-supervised salient object detection, with access to a small number of labeled samples and a large number of unlabeled samples. Specifically, we present a pseudo label based learn-ing framework with a Conditional Energy-based Model. We model the stochastic nature of human saliency labels using the stochastic latent variable of the Conditional Energy-based Model. It further enables generation of a high-quality pixel-wise uncertainty map, highlighting the reliability of corresponding pseudo label generated for the unlabeled sample. This minimises the contribution of low-certainty pseudo labels in optimising the model, preventing the error propagation. Experimental results show that the proposed strategy can effectively explore the contribution of unlabeled data. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models.
Unbiased Parameter Inference for a Class of Partially Observed Lévy-Process Models

We consider the problem of static Bayesian inference for partially observed Lévy-process models. We develop a methodology which allows one to infer static parameters and some states of the process, without a bias from the time-discretization of the afore-mentioned Lévy process. The unbiased method is exceptionally amenable to parallel implementation and can be computationally efficient relative to competing approaches. We implement the method on S & P 500 log-return daily data and compare it to some Markov chain Monte Carlo (MCMC) algorithm.
Syntactic Versus Semantic Similarity of Artificial and Real Faults in Mutation Testing Studies

Fault seeding is typically used in empirical studies to evaluate and compare test techniques. Central to these techniques lies the hypothesis that artificially seeded faults involve some form of realistic properties and thus provide realistic experimental results. In an attempt to strengthen realism, a recent line of research uses machine learning techniques, such as deep learning and Natural Language Processing, to seed faults that look like (syntactically) real ones, implying that fault realism is related to syntactic similarity. This raises the question of whether seeding syntactically similar faults indeed results in semantically similar faults and, more generally whether syntactically dissimilar faults are far away (semantically) from the real ones. We answer this question by employing 4 state-of-the-art fault-seeding techniques (PiTest - a popular mutation testing tool, IBIR - a tool with manually crafted fault patterns, DeepMutation - a learning-based fault seeded framework and <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="ojdanic-ieq1-3277564.gif"/></alternatives></inline-formula>BERT - a mutation testing tool based on the pre-trained language model CodeBERT) that operate in a fundamentally different way, and demonstrate that syntactic similarity does not reflect semantic similarity. We also show that 65.11%, 76.44%, 61.39% and 9.76% of the real faults of Defects4J V2 are semantically resembled by PiTest, IBIR, <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives><mml:math><mml:mi>μ</mml:mi></mml:math><inline-graphic xlink:href="ojdanic-ieq2-3277564.gif"/></alternatives></inline-formula>BERT and DeepMutation faults, respectively.
Robustness of antiferromagnetism in the Su-Schrieffer-Heeger Hubbard model

We recently unveiled that antiferromagnetism (AFM) order can be dominantly induced by the bond Su-Schrieﬀer-Heeger (SSH) electron-phonon coupling (EPC), which is beyond the conventional wisdom that AFM order is usually driven by strong Coulomb interactions. Nevertheless, many aspects of the interplay between EPC and strong electronic interactions on AFM ordering remains unexplored. Here, we investigate the Su-Schrieﬀer-Heeger-Hubbard (SSHH) model with bond SSH phonons and onsite Hubbard interactions by large-scale quantum Monte-Carlo (QMC) simulations and obtain its ground-state phase diagram for various Hubbard interactions and EPC strength at half ﬁlling. Our results show that Hubbard interactions further enhance EPC-induced AFM, especially for small phonon frequency or in adiabatic limit, the regime most relevant to various quantum materials. This could shed further light to understanding the cooperative eﬀect between EPC and electronic correlations in quantum materials.
AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent

Communication cost is the main bottleneck for the design of effective distributed learning algorithms. Recently, event-triggered techniques have been proposed to reduce the exchanged information among compute nodes and thus alleviate the communication cost. However, most existing event-triggered approaches only consider heuristic event-triggered thresholds. They also ignore the impact of computation and network delay, which play an important role on the training performance. In this paper, we propose an Asynchronous Event-triggered Stochastic Gradient Descent (SGD) framework, called AET-SGD, to i) reduce the communication cost among the compute nodes, and ii) mitigate the impact of the delay. Compared with baseline event-triggered methods, AET-SGD employs a linear increasing sample size event-triggered threshold, and can significantly reduce the communication cost while keeping good convergence performance. We implement AET-SGD and evaluate its performance on multiple representative data sets, including MNIST, FashionMNIST, KMNIST and CIFAR10. The experimental results validate the correctness of the design and show a significant communication cost reduction from 44x to 120x, compared to the state of the art. Our results also show that AET-SGD can resist large delay from the straggler nodes while obtaining a decent performance and a desired speedup ratio.
Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages

People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raising the awareness of gender stereotyping remain under-investigated. To this end, we collect a large greeting card message corpus covering three different occasions (birthday, Valentine’s Day and wedding) from three sources (exemplars from greeting message websites, real-life greetings from social media and language model generated ones). We uncover a wide range of gender stereotypes in this corpus via topic modeling, odds ratio and Word Embedding Association Test (WEAT). We further conduct a survey to understand people’s perception of gender roles in messages from this corpus and if gender stereotyping is a concern. The results show that people want to be aware of gender roles in the messages, but remain unconcerned unless the perceived gender roles conflict with the recipient’s true personality. In response, we developed GreetA, an interactive visualization and writing assistant tool to visualize fine-grained topics in greeting card messages drafted by the users and the associated gender perception scores, but without suggesting text changes as an intervention.
Mutation Testing in Evolving Systems: Studying the Relevance of Mutants to Code Evolution

Context: When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. However, while software evolves, its complexity is also evolving, introducing challenges to the testing process. To deal with this issue, testing techniques should be adapted to target the effect of the program changes instead of the entire program functionality. To this end, commit-aware mutation testing, a powerful testing technique, has been proposed. Unfortunately, commit-aware mutation testing is challenging due to the complex program semantics involved. Hence, it is pertinent to understand the characteristics, predictability, and potential of the technique. Objective: We conduct an exploratory study to investigate the properties of commit-relevant mutants, i.e., the test elements of commit-aware mutation testing, by proposing a general definition and an experimental approach to identify them. We thus aim at investigating the prevalence, location, and comparative advantages of commit-aware mutation testing over time (i.e., the program evolution). We also investigate the predictive power of several commit-related features in identifying and selecting commit-relevant mutants to understand the essential properties for its best-effort application case. Method: Our commit-relevant definition relies on the notion of observational slicing, approximated by higher-order mutation. Specifically, our approach utilizes the impact of mutants, effects of one mutant on another in capturing and analyzing the implicit interactions between the changed and unchanged code parts. The study analyses millions of mutants (over 10 million), 288 commits, five (5) different open-source software projects involving over 68,213 CPU days of computation and sets a ground truth where we perform our analysis. Results: Our analysis shows that commit-relevant mutants are located mainly outside of program commit change (81%), suggesting a limitation in previous work. We also note that effective selection of commit-relevant mutants has the potential of reducing the number of mutants by up to 93%. In addition, we demonstrate that commit relevant mutation testing is significantly more effective and efficient than state-of-the-art baselines, i.e., random mutant selection and analysis of only mutants within the program change. In our analysis of the predictive power of mutants and commit-related features (e.g., number of mutants within a change, mutant type, and commit size) in predicting commit-relevant mutants, we found that most proxy features do not reliably predict commit-relevant mutants. Conclusion: This empirical study highlights the properties of commit-relevant mutants and demonstrates the importance of identifying and selecting commit-relevant mutants when testing evolving software systems.
Hollow microstructural waveguides for propagation of infrared radiation

Theoretical results are presented on the propagation of infrared radiation in micron-sized parallel plane waveguides whose walls are gold-coated and separated by empty space. Propagation characteristics of the TE/sub 10/, TM/sub 10/, and TEM modes are described. The most loss-free propagation occurs for the TE/sub 10/ mode, due to the high reflectance for s polarization at the gold-coated surface. The results, when extrapolated to propagation in hollow micron-sized waveguides of rectangular cross section, suggest that propagation is possible over a significant number of wavelengths in spite of the large circumference of such guides in relation to their cross-sectional area. Theoretical indications are that the most loss-free structures would have losses on the order of 0.3 dB/cm. >
D ec 2 02 1 Preprint typeset in JHEP style Duality Defects in E 8

Ivan M. Burbano,1 Justin Kulp,2,3,4 Jonas Neuser5 Department of Physics, University of California, 366 Physics North, Berkeley, 94720-7300, CA, USA Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5, Canada Department of Physics & Astronomy, University of Waterloo, Waterloo, ON N2L 3G1, Canada Institute for Quantum Gravity, University of Erlangen-Nürnberg, Staudtstraße 7 / B2, 91058 Erlangen, Germany
A Framework for the Joint Optimization of Assignment and Pricing in Mobility-on-Demand Systems with Shared Rides

Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typically complemented with ridepooling options, which can reduce the negative externalities associated with ride-hailing services and increase the utilization of vehicles. Determining optimal policies for vehicle dispatching and pricing, two key components that enable MoD services, are challenging due to their massive scale and online nature. The challenge is amplified when the MoD platform offers exclusive (conventional ride-hailing) and shared services, and customers have the option to select between them. The pricing and dispatching problems are coupled because the realized demand depends on the quality of service (i.e., whom to share rides with) and the prices for each service type. We propose an integrated and computationally efficient method for solving the joint pricing and dispatching problem -- both when the problem is solved one request at a time or in batches (a common strategy in the industry). The main results of this research include showing that: (i) the sequential pricing problem has a closed-form solution under a multinomial logit (MNL) choice model, and (ii) the batched pricing problem is jointly concave in the expected demand distributions. To account for the spatial evolution of supply and demand, we introduce so-called retrospective costs to retain a tractable framework. Our numerical experiments demonstrate how this framework yields significant profit increases using taxicab data in Manhattan, New York City, compared to dynamic dispatching with static pricing policies.
Characteristic momentum of 
Hydro+
 and a bound on the enhancement of the speed of sound near the QCD critical point

Near the critical point in the QCD phase diagram, hydrodynamics breaks down at a momentum where the frequency of the fastest hydrodynamic mode becomes comparable with the decay rate of the slowest non-hydrodynamic mode. Hydro+ was developed as a framework which extends the range of validity of hydrodynamics beyond that momentum value. This was achieved through coupling the hydrodynamic modes to the slowest non-hydrodynamic mode. In this work, analyzing the spectrum of linear perturbations in Hydro+, we find that a slow mode falls out of equilibrium if its momentum is greater than a characteristic momentum value. That characteristic momentum turns out to be set by the branch points of the dispersion relations. These branch points occur at the critical momenta of so-called spectral curves and are related to the radius of convergence of the derivative expansion. The existence of such a characteristic momentum scale suggests that a particular class of slow modes has no remarkable effect on the flow of the plasma. Based on these results and previously derived relations to the stiffness of the equation of state, we find a temperature-dependent upper bound for the speed of sound near the critical point in the QCD phase diagram.
Projections of Orbital Measures and Quantum Marginal Problems

This paper studies projections of uniform random elements of (co)adjoint orbits of compact Lie groups. Such projections generalize several widely studied ensembles in random matrix theory, including the randomized Horn's problem, the randomized Schur's problem, and the orbital corners process. In this general setting, we prove integral formulae for the probability densities, establish some properties of the densities, and discuss connections to multiplicity problems in representation theory as well as to known results in the symplectic geometry literature. As applications, we show a number of results on marginal problems in quantum information theory and also prove an integral formula for restriction multiplicities.
Bayesian Compressive Channel Estimation for Hybrid Full-Dimensional MIMO Communications

Efficient channel estimation is challenging in full-dimensional multiple-input multiple-output communication systems, particularly in those with hybrid digital-analog architectures. Under a compressive sensing framework, this letter first designs a uniform dictionary based on a spherical Fibonacci grid to represent channels in a sparse domain, yielding smaller angular errors in three-dimensional beamspace than traditional dictionaries. Then, a Bayesian inference-aided greedy pursuit algorithm is developed to estimate channels in the frequency domain. Finally, simulation results demonstrate that both the designed dictionary and the proposed Bayesian channel estimation outperform the benchmark schemes and attain a lower normalized mean squared error of channel estimation.
Processing M.A. Castrén’s Materials: Multilingual Historical Typed and Handwritten Manuscripts

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castrén (1813–1852). The Finno-Ugrian Society is publishing Castrén’s manuscripts as new critical and digital editions, and at the same time different research groups have also paid attention to these materials. We discuss the workflows and technical infrastructure used, and consider how datasets that benefit different computational tasks could be created to further improve the usability of these materials, and also to aid the further processing of similar archived collections. We specifically focus on the parts of the collections that are processed in a way that improves their usability in more technical applications, complementing the earlier work on the cultural and linguistic aspects of these materials. Most of these datasets are openly available in Zenodo. The study points to specific areas where further research is needed, and provides benchmarks for text recognition tasks.
Body Composition Impacts Hematopoietic Stem Cell Transplant Outcomes in Both Autologous and Allogeneic Transplants: A Systematic Review

Abstract According to World Health Organization, over 50,000 hematopoietic stem cell transplants (HSCTs) are performed annually worldwide. Though HSCT can extend life-expectancy and improve disease-related health burdens, it is not without risks. Post-transplant overall survival is improving; therefore, it is imperative that factors contributing to or impeding further improvements are well understood. The purpose of this systematic review is to explore current data on body composition (specifically weight loss, BMI, obesity and sarcopenia) and the relation to HSCT outcomes. A literature search was conducted via PubMed and Web of Science databases. Key words included “body composition,” “sarcopenia,” “hematopoietic stem cell transplant,” “malnutrition,” “body mass index,” and “obesity. ” Results indicated that 16 out of 18 analyzed studies found a statistically significant relationship between body composition, in particular higher BMI and weight loss, and at least one survival-related outcome variable (eg., non-relapse mortality, overall survival and/or relapse). Based on the findings of this review, body composition, whether evaluated before or during HSCT, can impact a wide variety of post-transplant outcomes. This speaks to the importance of evaluating patients pre-transplant, identifying potential risk factors for worsened outcomes, and providing immediate interventions in order to optimize transplant outcomes.
Health of Convicted Persons in the Third Generation of the Longitudinal Cambridge Study in Delinquent Development

Research suggests that convicted persons are more likely than non-convicted persons to suffer poor health. However, few longitudinal studies have investigated associations between health and offending across generations. Using the Cambridge Study in Delinquent Development, this article prospectively investigates the relationship between health and offending across generations and between genders. At the average age of 25, third generation convicted males and females reported a higher incidence of serious drug use than non-convicted persons. Convicted males reported a higher incidence of mental illness and self-harm, whereas convicted females reported a lower incidence of physical illness, mental illness, self-harm and hospitalizations when compared to non-convicted females. Convicted males reported a higher incidence of industrial accidents, sports injuries and fight injuries, but a lower incidence of road accidents, whereas convicted females were more likely to report road accidents. Like their fathers, convicted males show worse health compared to non-convicted individuals.
Effectiveness of alectinib and osimertinib in a brain metastasized lung adenocarcinoma patient with concurrent EGFR mutations and DCTN1‐ALK fusion

The echinoderm microtubule associated protein‐like 4 gene (EML4) encodes the predominant anaplastic lymphoma kinase (ALK) fusion partner in non‐small‐cell lung cancer (NSCLC); however, the dynactin subunit 1 (DCTN1)‐ALK rearrangement is extremely rare. The co‐occurrence of primary epidermal growth factor receptor (EGFR) T790M mutation with EGFR exon 19 deletion (del) in patients with NSCLC is uncommon. Here we report a female lung adenocarcinoma patient with brain metastases and possible coexistence of primary EGFR T790M mutation/EGFR exon 19 del/DCTN1‐ALK translocation. The patient received multiline treatment including chemotherapy, antivascular, and targeted therapies. To overcome developed resistance to chemotherapy or targeted therapy to prolong overall survival, the patient's circulating tumor DNA (ctDNA) was dynamically monitored. The patient responded to successive osimertinib and alectinib treatment, and alectinib achieved a nearly complete response for lung and brain lesions after she acquired osimertinib resistance. Furthermore, we summarize 22 published cases of patients with lung adenocarcinoma with concurrent EGFR mutation and ALK rearrangement, including details of clinical characteristics, natural history, and pertinent therapy of this uncommon tumor subtype. This literature review shows that EGFR inhibition was an indispensable aspect of the treatment of patients with EGFR/ALK co‐alterations in the pre‐alectinib era and that ALK inhibition with crizotinib did not show more eye‐catching therapeutic results. Considering the effectiveness achieved by alectinib, this case study provides a new perspective for the treatment of lung cancer brain metastasis patients with concurrent EGFR/ALK mutations.
Outcomes of endoscopic submucosal dissection for colorectal neoplasms: Prospective, multicenter, cohort trial

Endoscopic mucosal resection (EMR) is the gold standard for the treatment of noninvasive large colorectal lesions, despite challenges associated with nonlifting lesions and a high rate of local recurrence. Endoscopic submucosal dissection (ESD) offers the possibility of overcoming these EMR limitations. However, a higher risk of complications and longer procedure time prevented its dissemination. As ESD now provides more stable results because of standardized techniques compared with those used earlier, this study aimed to quantify the rates of en bloc and curative resections, as well as ESD complications, in the present situation.
The Long-Term Impact of Childhood Disability on Mental Health Trajectories in Mid- to Late-Life

Objectives We draw from the life course and stress process frameworks to examine how experiencing disability in early life influences mental health in adulthood. Methods Data come from the Health and Retirement Study Cross-Wave Childhood Health and Family Aggregated Data file (2008-2018, n = 15,289). Childhood disability status is a retrospective self-report of whether respondents were disabled for six months or more because of a health problem before the age of 16 (n = 581). We used age-based growth curve models to construct trajectories of depressive symptoms by childhood disability status. Results Respondents who experienced childhood disability exhibit more depressive symptoms at age 50 compared to those who did not experience this stressor. However, there is no difference in the growth of depressive symptoms with age between these groups, suggesting maintained inequality over the late adulthood life course. Discussion Findings suggest that childhood disability has long-term implications for life course mental health.