Miracle-dz/UniFAR-FTUs huggingface dataset:
UV Slopes at All Redshifts Are Consistent with H = 1 Stochastic Star Formation Histories

Multiple investigations support describing galaxy growth as a stochastic process with correlations over a range of timescales governed by a parameter, H, empirically and theoretically constrained to be near unity. Here, we show that the distribution of UV-slopes, β, derived from an ensemble of theoretical H = 1 star formation histories is consistent with data at all redshifts z ≤ 16. At z = 0, the median value 〈β H=1〉 = − 2.27 agrees well with the canonical β 0 = −2.23 for local starbursts. At 4 ≲ z ≲ 16, JWST data span the model distribution’s 2nd to 98th percentiles. Values of −2.8 ≤ β ≤ −2.5 should be common in early galaxies without reference to exotic stellar populations—arising solely from a null hypothesis of H = 1 for the underlying diversity of galaxy growth histories. Future data should be interpreted with this fact in mind.
Forcing Axioms and the Definabilty of the Nonstationary Ideal on $\omega_1$

We show that under BMM and “there exists a Woodin cardinal ” , the nonstationary ideal on ω 1 can not be deﬁned by a Σ 1 formula with parameter A ⊂ ω 1 . We show that the same conclusion holds under the assumption of Woodin’s ( ∗ ) -axiom. We further show that there are universes where BPFA holds and NS ω 1 is Σ 1 ( ω 1 ) -deﬁnable. Last we show that if the canonical inner model with one Woodin cardinal M 1 exists, there is a universe where NS ω 1 is saturated, Σ 1 ( ω 1 ) -deﬁnable and MA ω 1 holds.
Multi-Agent Learning for Resilient Distributed Control Systems

Resilience describes a system's ability to function under disturbances and threats. Many critical infrastructures, including smart grids and transportation networks, are large-scale complex systems consisting of many interdependent subsystems. Decentralized architecture becomes a key resilience design paradigm for large-scale systems. In this book chapter, we present a multi-agent system (MAS) framework for distributed large-scale control systems and discuss the role of MAS learning in resiliency. This chapter introduces the creation of an artificial intelligence (AI) stack in the MAS to provide computational intelligence for subsystems to detect, respond, and recover. We discuss the application of learning methods at the cyber and physical layers of the system. The discussions focus on distributed learning algorithms for subsystems to respond to each other, and game-theoretic learning for them to respond to disturbances and adversarial behaviors. The book chapter presents a case study of distributed renewable energy systems to elaborate on the MAS architecture and its interface with the AI stack.
Continuous Exposure for Extreme Low-Light Imaging

We consider the problem of enhancing an underexposed dark image captured in a very low-light environment where details cannot be detected. Existing methods learn to adjust the input image’s exposure to a predetermined value. In practice, however, the optimal enhanced exposure varies from one input image to another, and as a result, the enhanced images may contain visual artifacts such as low-contrast or dark areas. We address this limitation by introducing a deep learning model that allows the user to continuously adjust the enhanced exposure level during run-time in order to optimize the output based on his preferences. We present a dataset of 1500 raw images captured in both outdoor and indoor scenes in extreme low-light conditions, with ﬁve different exposure levels and various camera parameters, as a key contribution. We demonstrate that, when compared to previous methods, our method can sig-niﬁcantly improve the enhancement quality of images captured in extreme low-light conditions under a variety of conditions.
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech

Hypernasality is an abnormal resonance in human speech production, especially in patients with craniofacial anomalies such as cleft palate. In clinical application, hypernasality estimation is crucial in cleft palate diagnosis, as its results determine the subsequent surgery and additional speech therapy. Therefore, designing an automatic hypernasality assessment method will facilitate speech-language pathologists to make precise diagnoses. Existing methods for hypernasality estimation only conduct acoustic analysis based on low-resource cleft palate dataset, by using statistical or neural network-based features. In this paper, we propose a novel approach that uses automatic speech recognition model to improve hypernasality estimation. Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation. Benefiting from such design, our model for hypernasality estimation can enjoy the advantages of ASR model: 1) compared with low-resource cleft palate dataset, the ASR task usually includes large-scale speech data in the general domain, which enables better model generalization; 2) the text annotations in ASR dataset guide model to extract better acoustic features. Experimental results on two cleft palate datasets demonstrate that our method achieves superior performance compared with previous approaches.
Generating physically-consistent high-resolution climate data with hard-constrained neural networks

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often us-ing image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renor-malization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.
A Redshift 4.38 Mg II Absorber Toward BR 1202–0725

We have detected a strong (EWobs = 20 Å) redshift 4.38 Mg II absorber toward the redshift 4.68 quasar BR 1202-0725. The identification of this feature as Mg II λλ2796, 2803 is supported by the identification of a damped Lyα absorber and ultraviolet Si II λ1526 absorption at the same redshift. The strengths of Mg II λλ2796, 2803 and Si II λ1526 are consistent with those of strong low-redshift Mg II systems, damped Lyα absorbers, and diffuse clouds within the Milky Way. C IV λλ1548, 1550 is not detected, indicating a lower ionization state than is seen in lower redshift Mg II absorbers and within the interstellar medium of our Galaxy. The detection of a single strong Mg II absorber at z > 4.2, among the three quasars observed with sufficient signal-to-noise ratio to detect an absorber, is consistent with the statistics and evolution of lower redshift Mg II absorbers. Thus, despite being at nearly twice the redshift of any previously identified Mg II absorber, this system appears quite similar to lower redshift Mg II systems that are identified with normal field galaxies. Because of the saturated nature of the Si II and Mg II lines, the metallicity of the gas is uncertain, but it is clear that some enrichment of Si and Mg, probably amounting to more than 1% of solar, has occurred by a redshift of 4.38.
Biofilms as a microbial hazard in the food industry: A scoping review

Biofilms pose a serious public health hazard with a significant economic impact on the food industry. The present scoping review is designed to analyse the literature published during 2001–2020 on biofilm formation of microbes, their detection methods, and association with antimicrobial resistance (if any). The peer‐reviewed articles retrieved from 04 electronic databases were assessed using PRISMA‐ScR guidelines. From the 978 preliminary search results, a total of 88 publications were included in the study. On analysis, the commonly isolated pathogens were Listeria monocytogenes, Staphylococcus aureus, Salmonella spp. , Escherichia coli, Bacillus spp. , Vibrio spp. , Campylobacter jejuni and Clostridium perfringens. The biofilm‐forming ability of microbes was found to be influenced by various factors such as attachment surfaces, temperature, presence of other species, nutrient availability etc. A total of 18 studies characterized the biofilm‐forming genes, particularly for S. aureus, Salmonella spp. , and E. coli. In most studies, polystyrene plate and/or stainless‐steel coupons were used for biofilm formation, and the detection was carried out by crystal violet assays and/or by plate counting method. The strain‐specific significant differences in biofilm formation were observed in many studies, and few studies carried out analysis of multi‐species biofilms. The association between biofilm formation and antimicrobial resistance was not clearly defined. Further, viable but non‐culturable form of the foodborne pathogens is posing an unseen (by conventional cultivation techniques) but potent threat to the food safety. The present review recommends the need for carrying out systematic surveys and risk analysis of biofilms in food chain to highlight the evidence‐based public health concerns, especially in regions where microbiological food hazards are quite prevalent.
Re-evaluation of Mirror Visual Feedback Therapy for a Systematic Evaluation/Meta-analysis of Physical Function Re-education After Stroke

Objective The aim of the study is to re-evaluate the methodological quality and quality of evidence for a systematic evaluation/meta-analysis of the effect of mirror visual feedback therapy on physical function re-education after stroke. Methods Systematic evaluations/meta-analyses of mirror visual feedback therapy on physical function re-education after stroke were searched in the China Knowledge Network database, Wanfang database, Vipers database, China Biomedical Literature database, PubMed, Web of Science, and Embase using a computer, and the search time frame was up to January 2022. Methodological quality and quality of evidence ratings of the included studies were determined using the AMSTAR2 scale and GRADE classification by two authors. Results Seventeen publications were included. The evaluation with the AMSTAR2 scale showed that one study had an intermediate quality rating, five had a low-quality rating, and the remaining 11 were all very low quality. The GRADE scale showed 93 outcome indicators, of which 6 were intermediate, 23 were low grades, and the rest were very low grades, with low overall quality. Conclusions Mirror visual feedback therapy is efficacious for physical function re-education after stroke and promotes recovery from physical dysfunction, but the methodological quality and quality of evidence from the related systematic evaluations/meta-analyses are low.
Heterogeneity in RAS mutations: One size does not fit all

Although oncogenic driver mutations in RAS occur in 20% of cancers, heterogeneity in the biologic outputs of different RAS mutants has hampered efforts to develop effective treatments for RAS-mutated cancers. In this issue of Science Signaling, Huynh et al. show that even among KRASQ61 mutants, the specific amino acid that is substituted substantially affects mutant KRAS biologic activity and oncogenicity. Description The distinct biochemistry and biology of KRAS Gln61 mutants are revealed (Huynh et al., in 09 August 2022 issue).
A novel attention-getting vocalization in zoo-housed western gorillas

As a critical aspect of language, vocal learning is extremely rare in animals, having only been described in a few distantly related species. New evidence, however, extends vocal learning/innovation to the primate order, with zoo-housed chimpanzees and orangutans producing novel vocal signals to attract the attention of familiar human caregivers. If the ability to produce novel vocalizations as a means of navigating evolutionarily novel circumstances spans the Hominidae family, then we can expect to find evidence for it in the family’s third genus, Gorilla. To explore this possibility, we conduct an experiment with eight gorillas from Zoo Atlanta to examine whether they use species-atypical vocalizations to get the attention of humans across three different conditions: just a human, just food, or a human holding food. Additionally, we survey gorilla keepers from other AZA-member zoos to compile a list of common attention-getting signals used by the gorillas in their care. Our experiment results indicated that Zoo Atlanta gorillas vocalized most often during the human-food condition, with the most frequently used vocal signal being a species-atypical sound somewhere between a sneeze and a cough (n = 28). This previously undescribed sound is acoustically different from other calls commonly produced during feeding (i.e., single grunts and food-associated calls). Our survey and analyses of recordings from other zoos confirmed that this novel attention-getting sound is not unique to Zoo Atlanta, although further work should be done to better determine the extent and patterns of transmission and/or potential independent innovation of this sound across captive gorilla populations. These findings represent one of the few pieces of evidence of spontaneous novel vocal production in non-enculturated individuals of this species, supporting the inclusion of great apes as moderate vocal learners and perhaps demonstrating an evolutionary function to a flexible vocal repertoire.
A Practical Guide to Design and Assess a Phylogenomic Study

Abstract Over the last decade, molecular systematics has undergone a change of paradigm as high-throughput sequencing now makes it possible to reconstruct evolutionary relationships using genome-scale datasets. The advent of “big data” molecular phylogenetics provided a battery of new tools for biologists but simultaneously brought new methodological challenges. The increase in analytical complexity comes at the price of highly specific training in computational biology and molecular phylogenetics, resulting very often in a polarized accumulation of knowledge (technical on one side and biological on the other). Interpreting the robustness of genome-scale phylogenetic studies is not straightforward, particularly as new methodological developments have consistently shown that the general belief of “more genes, more robustness” often does not apply, and because there is a range of systematic errors that plague phylogenomic investigations. This is particularly problematic because phylogenomic studies are highly heterogeneous in their methodology, and best practices are often not clearly defined. The main aim of this article is to present what I consider as the ten most important points to take into consideration when planning a well-thought-out phylogenomic study and while evaluating the quality of published papers. The goal is to provide a practical step-by-step guide that can be easily followed by nonexperts and phylogenomic novices in order to assess the technical robustness of phylogenomic studies or improve the experimental design of a project.
A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems

Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.
Can cannabis kill? Characteristics of deaths following cannabis use in England (1998–2020)

Background: Cannabis is the most widely used illegal drug but is rarely considered a causal factor in death. Aims: This study aimed to understand trends in deaths in England where cannabinoids were detected at post-mortem, and to evaluate the clinical utility of post-mortem cannabinoid concentrations in coronial investigations. Methods: Deaths with cannabinoid detections reported to the National Programme on Substance Abuse Deaths (NPSAD) were extracted and analysed. Results: From 1998 to 2011, on average 7% of all cases reported to NPSAD had a cannabinoid detected (n = 110 deaths per year), rising to 18% in 2020 (n = 350). Death following cannabis use alone was rare (4% of cases, n = 136/3455). Traumatic injury was the prevalent underlying cause in these cases (62%, n = 84/136), with cannabis toxicity cited in a single case. Polydrug use was evident in most cases (96%, n = 3319/3455), with acute drug toxicity the prevalent underlying cause (74%, n = 2458/3319). Cardiac complications were the most cited physiological underlying cause of death (4%, n = 144/3455). The median average Δ9-tetrahydrocannabinol post-mortem blood concentrations were several magnitudes lower than previously reported median blood concentrations in living users (cannabis alone: 4.3 µg/L; cannabis in combination with other drugs: 3.5 µg/L). Conclusions: Risk of death due to cannabis toxicity is negligible. However, cannabis can prove fatal in circumstances with risk of traumatic physical injury, or in individuals with cardiac pathophysiologies. These indirect harms need careful consideration and further study to better elucidate the role cannabis plays in drug-related mortality. Furthermore, the relevance of cannabinoid quantifications in determining cause of death in coronial investigations is limited.
Adrenal Cortical Carcinoma and Additional Rare Pathologic Findings in Multi-Organs in a Birt-Hogg-Dubé Syndrome Patient: With an Emphasis on the Molecular Characteristics of Adrenal Cortical Carcinoma

Birt-Hogg-Dubé (BHD) syndrome is a rare autosomal dominant disorder caused by germline alterations in the FLCN gene. We report a 38-year-old man with BHD syndrome presenting with multiple rare pathologic findings involving various organs, including adrenal cortical carcinoma (ACC). Initially, he presented with severe cholestatic jaundice and was found to have a 25 cm left adrenal mass with radiologic evidence of lung metastases, which was diagnosed as ACC on resection. Concurrently, pigmented, bile-stained granular casts were present within the kidney and diffuse cholestasis of the liver consistent with Stauffer syndrome was identified. Subsequent staging workup detected a 1.2 cm tubulovillous adenoma in the distal ascending colon and an incidental 1.2 cm thyroid nodule. Germline genetic testing revealed a pathogenic FLCN c.1285dup. Targeted DNA next generation sequencing of ACC revealed FLCN c.1285dup, IDH2 c.5332C>T, PRKAR1A c.1074del, and PDGFRB c.3282C>A and concurrent transcriptomic analysis demonstrated VEGFA overexpression. Fourteen months after resection, follow-up computerized tomography (CT) identified the progression of lung metastases and chemotherapy with etoposide doxorubicin and cisplatin was initiated. Here, we report the first ACC with the molecular characteristics in a BHD syndrome patient, although 5 adrenal lesions, including ACC, adenomas or neoplasm with malignant potential due to higher Ki67 labelling index, have been reported in the literature and no somatic analysis in these tumors were performed. Despite the rarity, our case potentially expands the tumor spectrum of BHD patients, helps to solidify possible association with adrenal cortical tumors and reiterates the value of genetic counseling in patients with ACC.
Optimal condition monitoring of wind turbines using intelligent image processing and internet of things

The aim is to suggest a control scheme for the wind mills which convert wind energy to electrical energy. The functioning of the governing scheme is characterised by incorporating it to a doubly fed induction generator (DFIG). The stationary part of the DFIG is unswervingly linked to the electric network. The rotating part is allied to this electric network all the way through a back-to-back AC-DC-AC PWM converter. Fuzzy logic is used to acquire features using decision making logic which as human-like flexibility. The FLC provides a crisp and smooth control action. The governing process of the converter on rotating part is comprehended by stationary magnetic flux to adjust the performance of the fuzzy logic controller (FLC). The FLC is opted to have an intelligent speed control. To enable a level direct current voltage and to guarantee a pure sine wave for the current in the grid side a Grid Side Converter (GSC) is used which is controlled using FLC. The accuracy of the FLC used for the control of DFIG has a quick vibrant retort with almost unsteady error value once evaluated with the scheme using conformist proportional integral (PI) controller. Image processing algorithms are used to track the blade sweep and angular velocity. The entire monitoring is implemented using ATmega processor and incorporated in cloud service for online monitoring.
75As nuclear magnetic resonance study of antiferromagnetic fluctuations in the normal state of LiFeAs

We present a detailed study of $^{75}\text{A}\text{s}$ nuclear magnetic resonance Knight shift and spin-lattice relaxation rate in the normal state of stoichiometric polycrystalline LiFeAs. Our analysis of the Korringa relation suggests that LiFeAs exhibits strong antiferromagnetic fluctuations, if transferred hyperfine coupling is a dominant interaction between $^{75}\text{A}\text{s}$ nuclei and Fe electronic spins, whereas for an on-site hyperfine coupling scenario, these are weaker, but still present to account for our experimental observations. Density-functional calculations of electric field gradient correctly reproduce the experimental values for both $^{75}\text{A}\text{s}$ and $^{7}\text{L}\text{i}$ sites.
Improving the Robustness and Adaptability of sEMG-Based Pattern Recognition Using Deep Domain Adaptation

The pattern recognition (PR) based on surface electromyography (sEMG) could improve the quality of daily life of amputees. However, the lack of robustness and adaptability hinders its practical application. To realize the long-term reliability and user adaptability simultaneously, a novel multi-task dual-stream supervised domain adaptation (MDSDA) network based on convolutional neural network (CNN) was proposed. A long-term multi-subject sEMG signal acquisition was conducted to validate the performance of MDSDA, recruiting 12 able-bodied subjects. A total of thirty gestures were used for the acquisition, including one set of static gestures and two sets of dynamic gestures. The long-term multi-subject sEMG dataset is publicly available at the website. Four train-test estimations were designed to evaluate the robustness and adaptability of MDSDA. The results showed that MDSDA outperformed CNN and fune-tuning. Furthermore, we studied the divisibility between static and dynamic gestures that performed similar actions. The outcomes demonstrated that there existed high separability between them. This may be helpful to reduce the signal collection burden. Experimental results proved MDSDA has the potential to provide a robust and generalized PR system for the clinic applications.