Miracle-dz/UniFAR-FTUs huggingface dataset:
Male nurses adaptation experiences after turnover to community institutions in Korea

We aimed to develop a substantive theory according to the associations between adaptation experience-related factors identified in male nurses after turnover to community institutions. From April through August 2019, data were collected through direct observations and in-depth interviews of 22 male nurse participants who were recruited purposively and analyzed simultaneously with the method proposed by Strauss and Corbin. Furthermore, 29 subcategories were derived from 11 categories, including: (1) leaving the clinical sector and changing jobs, (2) shaking while settling, (3) characteristics of the new job, (4) personal disposition, (5) support system, (6) finding my place, (7) solidifying my place, (8) demonstrating my professional competence, (9) stable settlement in my place, (10) preparing for a better future, and (11) still confused. The core category was identified as "putting down roots in another place for myself. " The verification of this theory in this studys results indicates a need for research into the evaluation and development of professional development programs and related policies to provide support to male nurses who are pursuing opportunities in community institutions to maintain their nursing identity and further their efforts for developing a nursing specialty.
Solar Wind Magnetic Field Line Dispersal: Multispacecraft Analysis and Comparison with Theoretical Results

The dominance of the global over the relative, cross-field transport of magnetic field lines (MFLs), up to scales of a couple of hours in the solar wind (SW), has been predicted both theoretically and numerically, in the approximation of self-similar, single-level power spectra of magnetic fluctuations. Here the predictions for the MFL relative cross-field transport or dispersal are tested against the results of in situ data analysis, using 23 yr of field and flow measurements on board ACE and Wind. The theoretical results are confirmed/refined by nonlinear theoretical computations and corrected to include the effects of power-level variability or intermittency. Other than confirming earlier theoretical findings at the separation scales ρ between a few ×1010 cm and the large-separation limit, with a ballistic regime followed by decorrelation and transition to diffusion in the (lnρ,θ) -space, the new study reveals two new supradiffusive regimes in lnρ and clock angle θ for the MFL dispersal, distinct from a drift-induced supradiffusion found earlier, and distinct from each other. The first supradiffusion, of the nonlinear computations, is a simple nonlinear effect, which cannot be recovered by data analysis at two spacecraft of fixed separation on the analysis timescale. The second supradiffusion, of the data analysis, is much stronger for ρ < few × 1010 cm than the two previous supradiffusions, and it becomes stronger at shorter separations. It follows the ballistic regime without transition through diffusion and is recovered by multiscale, theoretical estimates. It is a Lévy-type supradiffusion, due to the intermittency observed in the SW.
A co-ideal based identity-summand graph of a commutative semiring

Let $I$ be a strong co-ideal of a commutative semiring $R$ with identity. Let $\Gamma_{I} (R)$ be a graph with the set of vertices $S_{I} (R) = \{x \in R\setminus I: x + y \in I$ for some $y \in R \setminus I\}$, where two distinct vertices $x$ and $y$ are adjacent if and only if $x + y \in I$. We look at the diameter and girth of this graph. Also we discuss when $\Gamma_{I} (R)$ is bipartite. Moreover, studies are done on the planarity, clique, and chromatic number of this graph. Examples illustrating the results are presented.
Robustness of pair structures for nuclear yrast states

In this study, we investigate the robustness of pair structures for nuclear yrast states, that is, whether the structures of relevant collective pairs as building blocks of different yrast states are the same. We focus on deformed and transitional nuclei and study the yrast states of Si, Cr, and Xe, whose experimental values are 2.60, 2.40, and 2.16, respectively, using the nucleon-pair approximation (NPA) and shell-model effective interactions. For each yrast state, we consider optimized pair structures to be those providing the energy minimum for this state. To find the minimum, many full NPA calculations are performed with varying pair structures, and the numerical optimization procedure of the conjugate gradient method is implemented. Our results suggest that optimized pair structures remain the same for all states within a rotational band of a deformed nucleus. Our results also suggest that after backbending, that is, changing of the intrinsic state, the structure of the S pair, which is essential to build the monopole pairing correlation, remains approximately unchanged, whereas the structures of the non-S pairs, which are essential to build the quadrupole correlation, change significantly.
Durability of immunity and clinical protection in nursing home residents following bivalent SARS-CoV-2 vaccination

Background: Vaccines have substantially mitigated the disproportional impact of SARS-CoV-2 on the high morbidity and mortality experienced by nursing home residents. However, variation in vaccine efficacy, immune senescence and waning immunity all undermine vaccine effectiveness over time. The introduction of the bivalent vaccine in September 2022 aimed to counter this increasing susceptibility and consequences of breakthrough infection, however data on the durability and protection of the vaccine are limited. We evaluated the durability of immunity and protection after the first bivalent vaccination to SARS-CoV-2 in nursing home residents. Methods: For the immunologic evaluation, community nursing home volunteers agreed to serial blood sampling before, at two weeks, three and six months after each vaccination for antibodies to spike protein and pseudovirus neutralization activity over time. Concurrent clinical outcomes were evaluated by reviewing electronic health record data from residents living in Veterans Administration managed nursing home units. Residents without recent infection but prior vaccination to SARS-CoV-2 were followed over time beginning with administration of the newly available bivalent vaccine using a target trial emulation (TTE) approach; TTE compared time to breakthrough infection, hospitalization and death between those who did and did not receive the bivalent vaccine. Results: We evaluated antibodies in 650 nursing home residents; 452 had data available following a first monovalent booster, 257 following a second monovalent booster and 321 following a bivalent vaccine. We found a rise in BA.5 neutralization activity from the first and second monovalent boosters through the bivalent vaccination regardless of prior SARS-CoV-2 history. Titers declined at three and six months after the bivalent vaccination but generally exceeded those at three months compared to either prior boost. BA.5 neutralization titers six months after the bivalent vaccination were diminished but had detectable levels in 80% of infection-naive and 100% of prior infected individuals. TTE evaluated 5903 unique subjects, of whom 2235 received the bivalent boost. TTE demonstrated 39% or greater reduction in risk of infection, hospitalization or death at four months following the bivalent boost. Conclusion: Immunologic results mirrored those of the TTE and suggest bivalent vaccination added substantial protection for up to six months after bivalent vaccination with notable exceptions. However, the level of protection declined over this period, and by six months may open a window of added vulnerability to infection before the next updated vaccine becomes available. We strongly agree with the CDC recommendation that those who have not received a bivalent vaccination receive that now and these results support a second bivalent booster for those at greatest risk which includes many nursing home residents.
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation

Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various fields. Several literatures show that DG can be arbitrarily hard without exploiting target domain information. To address this issue, test-time adaptive (TTA) methods are proposed. Existing TTA methods require offline target data or extra sophisticated optimization procedures during the inference stage. In this work, we adopt Non-Parametric Classifier to perform the test-time Adaptation (AdaNPC). In particular, we construct a memory that contains the feature and label pairs from training domains. During inference, given a test instance, AdaNPC first recalls K closed samples from the memory to vote for the prediction, and then the test feature and predicted label are added to the memory. In this way, the sample distribution in the memory can be gradually changed from the training distribution towards the test distribution with very little extra computation cost. We theoretically justify the rationality behind the proposed method. Besides, we test our model on extensive numerical experiments. AdaNPC significantly outperforms competitive baselines on various DG benchmarks. In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods. The code is available at https://github.com/yfzhang114/AdaNPC.
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach

A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (DTs), wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints by utilizing AI techniques such as causal reasoning. In this paper, a novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems. The CSC system is posed as an imitation learning (IL) problem, where the transmitter, with access to optimal network control policies using a DT, teaches the receiver using SC over a bandwidth-limited wireless channel how to improve its knowledge to perform optimal control actions. The causal structure in the transmitter’s data is extracted using novel approaches from the framework of deep end-to-end causal inference, thereby enabling the creation of a semantic representation that is causally invariant, which in turn helps generalize the learned knowledge of the system to new and unseen situations. The CSC decoder at the receiver is designed to extract and estimate semantic information while ensuring high semantic reliability. The receiver control policies, semantic decoder, and causal inference are formulated as a bi-level optimization problem within a variational inference framework. This problem is solved using a novel concept called network state models, inspired from world models in generative AI, that faithfully represents the environment dynamics leading to data generation. Furthermore, the proposed framework includes an analytical characterization of the performance gap that results from employing a suboptimal policy learned by the receiver that uses the transmitted semantic information to construct a model of the physical environment. The CSC system utilizes two concepts, namely the integrated information theory principle in the theory of consciousness and the abstract cell complex concept in topology, to precisely express the information content conveyed by the causal states and their relationships. Through this analysis, novel formulations of semantic information, semantic reliability, distortion, and similarity metrics are proposed, which extend beyond Shannon’s concept of uncertainty. Simulation results demonstrate that the proposed CSC system outperforms conventional wireless and state-of-the-art SC systems by achieving better semantic reliability with reduced bits and enabling better control policies over time thanks to the generative AI architecture.
Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings. The source code is available at: https://github.com/LoveSiameseCat/CFM.
Lessons Learned from a Citizen Science Project for Natural Language Processing

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and at- tract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.
Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction

Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds) on-device NeRF training remains a challenge. In this work, we first identify the inefficiency bottleneck: the need to interpolate NeRF embeddings up to 200,000 times from a 3D embedding grid during each training iteration. To alleviate this, we propose Instant-3D, an algorithm-hardware co-design acceleration framework that achieves instant on-device NeRF training. Our algorithm decomposes the embedding grid representation in terms of color and density, enabling computational redundancy to be squeezed out by adopting different (1) grid sizes and (2) update frequencies for the color and density branches. Our hardware accelerator further reduces the dominant memory accesses for embedding grid interpolation by (1) mapping multiple nearby points' memory read requests into one during the feed-forward process, (2) merging embedding grid updates from the same sliding time window during back-propagation, and (3) fusing different computation cores to support the different grid sizes needed by the color and density branches of Instant-3D algorithm. Extensive experiments validate the effectiveness of Instant-3D, achieving a large training time reduction of 41× - 248× while maintaining the same reconstruction quality. Excitingly, Instant-3D has enabled instant 3D reconstruction for AR/VR, requiring a reconstruction time of only 1.6 seconds per scene and meeting the AR/VR power consumption constraint of 1.9 W.
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation

Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimensions. In this paper, we focus on another important dimension that has received less attention: varying quantities/distributions of labeled and unlabeled data across devices. In order to leverage all data, we develop a decentralized federated domain adaptation methodology which considers the transfer of ML models from devices with high quality labeled data (called sources) to devices with low quality or unlabeled data (called targets). Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency. To obtain a concrete objective function, we derive a measurable generalization error bound that accounts for estimates of source-target hypothesis deviations and divergences between data distributions. The resulting optimization problem is a mixed-integer signomial program, a class of NP-hard problems, for which we develop an algorithm based on successive convex approximations to solve it tractably. Subsequent numerical evaluations of ST-LF demonstrate that it improves classification accuracy and energy efficiency over state-of-the-art baselines.
A Practical AoI Scheduler in IoT Networks with Relays

Internet of Things (IoT) networks have become ubiquitous as autonomous computing, communication and collaboration among devices become popular for accomplishing various tasks. The use of relays in IoT networks further makes it convenient to deploy IoT networks as relays provide a host of benefits, like increasing the communication range and minimizing power consumption. Existing literature on traditional AoI schedulers for such two-hop relayed IoT networks are limited because they are designed assuming constant/non-changing channel conditions and known (usually, generate-at-will) packet generation patterns. Deep reinforcement learning (DRL) algorithms have been investigated for AoI scheduling in two-hop IoT networks with relays, however, they are only applicable for small-scale IoT networks due to exponential rise in action space as the networks become large. These limitations discourage the practical utilization of AoI schedulers for IoT network deployments. This paper presents a practical AoI scheduler for two-hop IoT networks with relays that addresses the above limitations. The proposed scheduler utilizes a novel voting mechanism based proximal policy optimization (v-PPO) algorithm that maintains a linear action space, enabling it be scale well with larger IoT networks. The proposed v-PPO based AoI scheduler adapts well to changing network conditions and accounts for unknown traffic generation patterns, making it practical for real-world IoT deployments. Simulation results show that the proposed v-PPO based AoI scheduler outperforms both ML and traditional (non-ML) AoI schedulers, such as, Deep Q Network (DQN)-based AoI Scheduler, Maximal Age First-Maximal Age Difference (MAF-MAD), MAF (Maximal Age First) , and round-robin in all considered practical scenarios.
On Dynamic Program Decompositions of Static Risk Measures

Optimizing static risk-averse objectives in Markov decision processes is challenging because they do not readily admit dynamic programming decompositions. Prior work has proposed to use a dynamic decomposition of risk measures that help to formulate dynamic programs on an augmented state space. This paper shows that several existing decompositions are inherently inexact, contradicting several claims in the literature. In particular, we give examples that show that popular decompositions for CVaR and EVaR risk measures are strict overestimates of the true risk values. However, an exact decomposition is possible for VaR, and we give a simple proof that illustrates the fundamental diﬀerence between VaR and CVaR dynamic programming properties.
State Spaces Aren’t Enough: Machine Translation Needs Attention

Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e.g. vision, language modelling, and audio. Thanks to its mathematical formulation, it compresses its input to a single hidden state, and is able to capture long range dependencies while avoiding the need for an attention mechanism. In this work, we apply S4 to Machine Translation (MT), and evaluate several encoder-decoder variants on WMT’14 and WMT’16. In contrast with the success in language modeling, we find that S4 lags behind the Transformer by approximately 4 BLEU points, and that it counter-intuitively struggles with long sentences. Finally, we show that this gap is caused by S4’s inability to summarize the full source sentence in a single hidden state, and show that we can close the gap by introducing an attention mechanism.
C^1 self-maps on S^n, S^n S^m, C P^n and H P^n with all their periodic orbits hyperbolic

We study in its homological class the periodic structure of the C1 self–maps on the manifolds Sn (the n–dimensional sphere), Sn × Sm (the product space of the n–dimensional with the m–dimensional spheres), CPn (the n– dimensional complex projective space) and HPn (the n–dimensional quaternion projective space), having all their periodic orbits hyperbolic.
Stable and low-precision training for large-scale vision-language models

We introduce new methods for 1) accelerating and 2) stabilizing training for large language-vision models. 1) For acceleration, we introduce SwitchBack, a linear layer for int8 quantized training which provides a speed-up of 13-25% while matching the performance of bfloat16 training within 0.1 percentage points for the 1B parameter CLIP ViT-Huge -- the largest int8 training to date. Our main focus is int8 as GPU support for float8 is rare, though we also analyze float8 training through simulation. While SwitchBack proves effective for float8, we show that standard techniques are also successful if the network is trained and initialized so that large feature magnitudes are discouraged, which we accomplish via layer-scale initialized with zeros. 2) For stability, we analyze loss spikes and find they consistently occur 1-8 iterations after the squared gradients become under-estimated by their AdamW second moment estimator. As a result, we recommend an AdamW-Adafactor hybrid which avoids loss spikes when training a CLIP ViT-Huge model and outperforms gradient clipping at the scales we test.
What does BERT learn about prosody?

Language models have become nearly ubiquitous in natural language processing applications achieving state-of-the-art results in many tasks including prosody. As the model design does not define predetermined linguistic targets during training but rather aims at learning generalized representations of the language, analyzing and interpreting the representations that models implicitly capture is important in bridging the gap between interpretability and model performance. Several studies have explored the linguistic information that models capture providing some insights on their representational capacity. However, the current studies have not explored whether prosody is part of the structural information of the language that models learn. In this work, we perform a series of experiments on BERT probing the representations captured at different layers. Our results show that information about prosodic prominence spans across many layers but is mostly focused in middle layers suggesting that BERT relies mostly on syntactic and semantic information.