Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks

In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep face recognition. As done in popular training data augmentation schemes, we graphically render real and synthetic face images with randomly chosen or best-fitting 3D face models to generate novel views of the base identity. We compare deep features generated from these images to assess the perturbation these renderings introduce into the original identity. We perform this analysis at various degrees of facial yaw with the base identities varying in gender and ethnicity. Additionally, we investigate if adding some form of context and background pixels in these rendered images, when used as training data, further improves the downstream performance of a face recognition model. Our experiments demonstrate the significance of facial shape in accurate face matching and underpin the importance of contextual data for network training.
Slow Thermalization of Few-Body Dipole-Dipole Interactions

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-, and four-body dipole-dipole interactions in a one-dimensional array. Using a simplified model of a realistic experimental system, we study the initial state survival probability, the level spacing statistics, the spread of entanglement, and the properties of the energy eigenstates. By exploring a range of disorders and interaction strengths, we find regions in parameter space where the three- and four-body dynamics exhibit nonergodic behavior and either fail to reach thermodynamic equilibrium or do so slowly. The interplay between the hopping and field-tuned interactions gives rise to quantum many-body scar states, which play a critical role in slowing the dynamics of the three- and four-body interactions.
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment

—Human-centered AI considers human experiences with AI performance. While abundant research has been helping AI achieve superhuman performance either by fully automatic or weak supervision learning, fewer endeavors are experimenting with how AI can tailor to humans’ preferred skill level given ﬁne-grained input. In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process. To achieve this, we developed a portable, interactive platform that enables the user to interact with agents online via manipulating the task difﬁculty, observing performance, and providing curriculum feedback. Our system is highly parallelizable, making it possible for a human to train large-scale reinforcement learning applications that require millions of samples without a server. The result demonstrates the effectiveness of an interactive curriculum for reinforcement learning involving human-in-the-loop. It shows reinforcement learning performance can successfully adjust in sync with the human desired difﬁculty level. We believe this research will open new doors for achieving ﬂow and personalized adaptive difﬁculties. Our demo executable and videos are available at https://bit.ly/372vCNv.
M: Intent-based Recommendations to Support Dashboard Composition

Despite the ever-growing popularity of dashboards across a wide range of domains, their authoring still remains a tedious and complex process. Current tools offer considerable support for creating individual visualizations but provide limited support for discovering groups of visualizations that can be collectively useful for composing analytic dashboards. To address this problem, we present Medley, a mixed-initiative interface that assists in dashboard composition by recommending dashboard collections (i.e., a logically grouped set of views and filtering widgets) that map to specific analytical intents. Users can specify dashboard intents (namely, measure analysis, change analysis, category analysis, or distribution analysis) explicitly through an input panel in the interface or implicitly by selecting data attributes and views of interest. The system recommends collections based on these analytic intents, and views and widgets can be selected to compose a variety of dashboards. Medley also provides a lightweight direct manipulation interface to configure interactions between views in a dashboard. Based on a study with 13 participants performing both targeted and open-ended tasks, we discuss how Medley's recommendations guide dashboard composition and facilitate different user workflows. Observations from the study identify potential directions for future work, including combining manual view specification with dashboard recommendations and designing natural language interfaces for dashboard authoring.
PointConvFormer: Revenge of the Point-based Convolution

We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based attention. In PointConvFormer, attention computed from feature difference between points in the neighborhood is used to modify the convolutional weights at each point. Hence, we preserved the invariances from point convolution, whereas attention helps to select relevant points in the neighborhood for convolution. PointConvFormer is suitable for multiple tasks that require details at the point level, such as segmentation and scene flow estimation tasks. We experiment on both tasks with multiple datasets including ScanNet, SemanticKitti, FlyingThings3D and KITTI. Our results show that PointConvFormer offers a better accuracy-speed tradeoff than classic convolutions, regular transformers, and voxelized sparse convolution approaches. Visualizations show that PointConvFormer performs similarly to convolution on flat areas, whereas the neighborhood selection effect is stronger on object boundaries, showing that it has got the best of both worlds. The code will be available.
Modified generalized Brillouin zone theory with on-site disorder

We study the characterization of the non-Hermitian skin effect (NHSE) in non-Hermitian systems with on-site disorder. We extend the application of generalized-Brillouin-zone (GBZ) theory to these systems. By proposing a modified GBZ theory, we give a faithfully description of the NHSE. For applications, we obtain a unified $\beta$ for system with long-range hopping, and explain the conventional-GBZ irrelevance of the magnetic suppression of the NHSE in the previous study.
Photonic band gap formation in silicon carbide triangular cross-section photonic crystals

Triangular cross-section silicon carbide (SiC) color center photonics has recently emerged as a very promising platform for quantum information processing. Here, we investigate dispersion relations in 1D SiC photonic crystals in triangular geometry and discuss the formation of photonic band gaps (PBGs). We compare the PBG formation trends and evaluate the fabrication suitability. Finally, we propose three distinct photonic device designs, resulting from unique PBG conﬁgurations, which are vital components for improving the eﬃciency of quantum photonic circuits. 1D PhCs can play a signiﬁcant role for robust light conﬁnement in quantum photonic hardware. Our simulated results show that the nature of PBG conﬁgurations primarily depends on the etch-angle and varies intuitively with other
Dimensions and C*-algebras

Notations Nonintegral dimensions and indices in analysis and geometry Locally semisimple algebras and their Bratteli diagrams The characterization of the dimension groups Affine representations of simple dimension groups Finite difference equations and continued fractions Stationary systems, algebraic number theory, and Williams' problem Scales and dimensions for locally semisimple algebras The $K_0$ theory of $C^*$-algebras An application of the Bott Periodicity Theorem Further developments and problems References Index.
Stability of hairy black holes in shift-symmetric scalar-tensor theories via the effective field theory approach

Shift-symmetric Horndeski theories admit an interesting class of Schwarzschild-de Sitter black hole solutions exhibiting time-dependent scalar hair. The properties of these solutions may be studied via a bottom-up effective field theory (EFT) based on the background symmetries. This is in part possible by making use of a convenient coordinate choice — Lemaître-type coordinates — in which the profile of the Horndeski scalar field is linear in the relevant time coordinate. We construct this EFT, and use it to understand the stability of hairy black holes in shift-symmetric Horndeski theories, providing a set of constraints that the otherwise-free functions appearing in the Horndeski Lagrangian must satisfy in order to admit stable black hole solutions. The EFT is analyzed in the decoupling limit to understand potential sources of instability. We also perform a complete analysis of the EFT with odd-parity linear perturbations around general spherically symmetric space-time.
Few-shot Learning with Retrieval Augmented Language Models

Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question answering and fact checking, massive parameter counts to store knowledge seem to be needed. Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings. In this work we present Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples. We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index, showing that it can easily be updated. Notably, Atlas reaches over 42% accuracy on Natural Questions using only 64 examples, outperforming a 540B parameters model by 3% despite having 50x fewer parameters.
Beyond Diagonal Reconfigurable Intelligent Surfaces: From Transmitting and Reflecting Modes to Single-, Group-, and Fully-Connected Architectures

Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective RISs, mathematically characterized by diagonal phase shift matrices, while there is a lack of a comprehensive study for RISs unifying different modes/architectures. In this paper, we solve this issue by analyzing and proposing a general RIS-aided communication model. Specifically, we establish an RIS model not limited to diagonal phase shift matrices, a novel branch referred to as beyond diagonal RIS (BD-RIS), unifying modes and architectures. With the proposed model, we develop efficient algorithms to jointly design transmit precoder and BD-RIS matrix to maximize the sum-rate for RIS-aided systems. We also provide simulation results to compare the performance of BD-RISs with different modes/architectures. Simulation results show that under the same mode, fully- and group-connected RIS can effectively increase the sum-rate performance compared with single-connected RIS, and that hybrid RIS outperforms reflective/transmissive RIS with the same architecture.
Early surrogates of outcome after thrombectomy in posterior circulation stroke

Early surrogates for functional outcome in anterior circulation stroke have been described with the National Institute of Health Stroke Scale (NIHSS) at 24 h being reported as the most accurate metric. We compare discriminatory power of established definitions of early neurological improvement (ENI) and NIHSS scores at admission and 24 h to predict functional outcome at 90 days after thrombectomy in posterior circulation stroke (PCS).
The air mycobiome is decoupled from the soil mycobiome in the California San Joaquin Valley

Dispersal is a key force in the assembly of fungal communities and the air is the dominant route of dispersal for most fungi. Understanding the dynamics of airborne fungi is important for determining their source and for helping to prevent fungal disease. This understanding is important in the San Joaquin Valley of California, which is home to 4.2 million people and where the airborne fungus Coccidioides is responsible for the most important fungal disease of otherwise healthy humans, coccidioidomycosis. The San Joaquin Valley is the most productive agricultural region in the United States, with the principal crops grown therein susceptible to fungal pathogens. Here, we characterize the fungal community in soil and air on undeveloped and agricultural land in the San Joaquin Valley using metabarcoding of the internal transcribed spacer 2 variable region of fungal rDNA. Using 1,002 individual samples, we report one of the most extensive studies of fungi sampled simultaneously from air and soil using modern sequencing techniques. We find that the air mycobiome in the San Joaquin Valley is distinct from the soil mycobiome, and that the assemblages of airborne fungi from sites as far apart as 160 km are far more similar to one another than to the fungal communities in nearby soils. Additionally, we present evidence that airborne fungi in the San Joaquin Valley are subject to dispersal limitation and cyclical intra‐annual patterns of community composition. Our findings are broadly applicable to understanding the dispersal of airborne fungi and the taxonomic structure of airborne fungal assemblages.
Corticogenesis across species at single‐cell resolution

The neocortex (or pallium) consists of diverse cell types that are organized in a highly species‐specific manner under strict spatiotemporal control during development. Many of the cell types are present transiently throughout development but contribute to permanent species‐specific cortical features that are acquired through evolution. Therefore, capturing cell type‐specific biological information has always been an important quest in the field of neurodevelopment. The progress in achieving fine cellular resolution has been slow due to technical challenges. However, with recent advancements in single‐cell and multi‐omics technologies, many laboratories have begun to successfully interrogate cellular and molecular mechanisms driving corticogenesis at single‐cell resolution. In this review, we provide summarized results from many primary publications and several in‐depth review articles that utilize or address single‐cell genomics techniques to understand important topics, such as cellular and molecular mechanisms governing cortical progenitor proliferation, cell lineage progression, neuronal specification, and arealization, across multiple gyrencephalic (i.e., human and non‐human primates) and lissencephalic species (i.e., mouse, reptiles, and songbirds). We also examine findings from recent studies involving epigenomic and posttranscriptional regulation of corticogenesis. In the discussion section, we provide our insights on the challenges the field currently faces as well as promising future applications of single cell technologies.
Immunotherapeutic targets in non‐small cell lung cancer

Non‐small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and has a 5‐year survival rate of ~20%. Immunotherapies have shown promising results leading to durable responses, however, they are only effective for a subset of patients. To determine the best therapeutic approach, a thorough and in‐depth profiling of the tumour microenvironment (TME) is required. The TME is a complex network of cell types that form an interconnected network, promoting tumour cell initiation, growth and dissemination. The stroma, immune cells and endothelial cells that comprise the TME generate a plethora of cytotoxic or cytoprotective signalling pathways. In this review, we discuss immunotherapeutic targets in NSCLC tumours and how the TME may influence patients' response to immunotherapy.
Analysis of a national difficult airway database

Difficult airway management continues to adversely affect patient care and clinical outcomes and is poorly predicted. Previous difficult airway management is the most accurate predictor of future difficulty. The Difficult Airway Society initiated a national airway database to allow clinicians to access details of previous difficult airway episodes in patients issued with a difficult airway alert card. We aimed to analyse this database, reporting patient characteristics, airway management and patient outcomes. We included all living adult patients reported in the first 5 years of the database (n = 675). Clinical airway assessment was reported in 634 (94%) patients, with three or more parameters assessed in 488 (72%). A history of difficult airway was known in 136 (20%) patients and difficult airway management was anticipated in 391 (58%). In all, 75 (11%) patients had an airway‐related critical incident, with 1 in 29 being awoken from anaesthesia, 1 in 34 requiring unplanned or prolonged stay in the intensive care unit and 1 in 225 needing an emergency front‐of‐neck airway or had a cardiac arrest/peri‐arrest episode. Airway‐related critical incidents were associated with out‐of‐hours airway management, but no other associations were apparent. Our data report the first analysis of a national difficult airway database, finding that unanticipated difficult airway management continues to occur despite airway assessment, and the rate of critical incidents in this cohort of patients is high. This database has the potential to improve airway management for patients in the future.
Facilitating Type III investigations to meet the needs of high ability students with behavioral challenges

Using a multiple case study design, the author observed the facilitation of Type III investigations (Renzulli, 1977) with five students identified as having high academic ability and documented patterns of challenging behavior and underachievement. Research questions investigated the factors that lead to underachievement and behavioral challenges for this population as well as the impact that participation in Type III investigations have on academic performance and behavior patterns. Strategies and methods utilized by mentor teachers who facilitated these investigations were also identified. Findings indicated that participants’ underachievement and behavioral challenges were related to a lack of interest and engagement in the standardized curriculum, boredom with assignments they regard as irrelevant, poor social skills, anger management issues, and poor family relationships and/or family adversity. During the course of the study, positive academic and behavioral gains were identified through interview data, pre and posttest BASC-2 data, student grades and behavior reports, and observations from teachers. Mentor teachers reported that certain strategies such as: active listening, collaborative problem solving, and sharing personal experiences helped to establish rapport and build supportive relationships with the participants they collaborated with in this process. INDEX WORDS: Gifted, Applied Projects, High Ability, Behavior Problems, Mentoring, Case Study FACILITATING TYPE III INVESTIGATIONS TO MEET THE NEEDS OF HIGH ABILITY STUDENTS WITH BEHAVIORAL CHALLENGES
Differential immune response induced by two immunization schedules with an inactivated SARS-CoV-2 vaccine in a randomized phase 3 clinical trial

Background: The development of vaccines to control the COVID-19 pandemic progression is a worldwide priority. CoronaVac(R) is an inactivated SARS-CoV-2 vaccine approved for emergency use with robust efficacy and immunogenicity data reported in trials in China, Brazil, Indonesia, Turkey, and Chile. Methods: This study is a randomized, multicenter, and controlled phase 3 trial in healthy Chilean adults aged [≥]18 years. Volunteers received two doses of CoronaVac(R) separated by two (0-14 schedule) or four weeks (0-28 schedule). 2,302 volunteers were enrolled, 440 were part of the immunogenicity arm, and blood samples were obtained at different times. Samples from a single center are reported. Humoral immune responses were evaluated by measuring the neutralizing capacities of circulating antibodies. Cellular immune responses were assessed by ELISPOT and flow cytometry. Correlation matrixes were performed to evaluate correlations in the data measured. Results: Both schedules exhibited robust neutralizing capacities with the response induced by the 0-28 schedule being better. No differences were found in the concentration of antibodies against the virus and different variants of concern between schedules. Stimulation of PBMCs with MPs induced the secretion of IFN-g and the expression of activation induced markers for both schedules. Correlation matrixes showed strong correlations between neutralizing antibodies and IFN-g secretion. Conclusions: Immunization with CoronaVac(R) in Chilean adults promotes robust cellular and humoral immune responses. The 0-28 schedule induced a stronger humoral immune response than the 0-14 schedule. Funding: Ministry of Health, Government of Chile, Confederation of Production and Commerce & Millennium Institute on Immunology and Immunotherapy, Chile. Clinical trial number: NCT04651790.
Analysis of complex rectangular dielectric waveguides

In this paper, a rigorous method for the analysis of the propagation characteristics of various rectangular waveguides is proposed based on the recent development of the numerical mode matching method for multi-region, vertically stratified media. Unlike the conventional two-dimensional finite element method which is widely used for the analysis of dielectric waveguide problems, the numerical mode matching method proposed in this paper uses the propagation properties of the waves on the cross-section of the waveguides to convert the two-dimensional problem into a few one-dimensional problems. These one-dimensional problems are then solved with the one-dimensional finite element method, resulting in a great saving of the number of unknowns needed. A major difference between this method and the conventional mode matching method is that the eigenvalues and eigenmodes of the constituent layered media are found numerically by using a one-dimensional finite element method, which makes the method more versatile .
Load-settlement behaviour of model pile groups in sand under vertical load

AbstractModel pile load testing is effective to study the load-settlement behaviour of pile foundations given the controlled environment in which the testing is done. This paper reports a testing program in a large calibration chamber involving individual piles and pile groups installed in sand samples of three different densities. Tests on both nondisplacement and driven piles are evaluated to assess the influence of the pile installation process on pile load-settlement response. A method is proposed to predict the load-settlement response of a pile group based on the response of a single pile. The method is shown to produce estimates that are in good agreement with measurements. The influence of pile group configuration, pile spacing, soil density and method of pile installation is discussed.