Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding cyclic causal structures in acyclic space-times: no-go results for indefinite causality

Causality can be defined in terms of space-time or based on information-theoretic structures, which correspond to very different notions of causation. Yet, in physical experiments, these notions play together in a compatible manner. The process matrix framework is useful for modelling indefinite causal structures (ICS) in an information-theoretic sense, but there remain important open questions regarding the physicality of such processes. In particular, there are several experiments that claim to implement ICS processes in Minkowski space-time, which presents an apparent theoretical paradox: how can an indefinite information-theoretic causal structure be compatible with a definite space-time structure? To address this, we develop a general framework that disentangles the two causality notions and formalises their relations. The framework describes a composition of quantum operations through feedback loops, and the embedding of the resulting (possibly cyclic) information-theoretic structure in an acyclic space-time structure. Relativistic causality is formalised as an operational compatibility condition between the two structures. Reformulating the process matrix framework here, we establish no-go results which imply that it is impossible to physically realise ICS in a fixed space-time with space-time localised quantum systems. Further, we prove that physical realisations of any ICS process, even those involving space-time non-localised systems, will ultimately admit an explanation in terms of a definite (and acyclic) causal order process, at a more fine-grained level. These results fully resolve the apparent paradox and we discuss their implications for the interpretation of the above-mentioned experiments. Moreover, our work offers concrete insights on the operational meaning of indefinite causality, both within and beyond the context of a fixed space-time.
A hybrid 2-stage vision transformer for artiﬁcial intelligence-assisted 5 class pathologic diagnosis of gastric endoscopic biopsies: a diagnostic tool for guiding gastric cancer treatment

Objective Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. Although artiﬁcial intelligence (AI) has brought a great promise to assist pathologist to screen digitalized whole slide images, automatic classiﬁcation systems for guiding proper GC treatment based on clinical guideline are still lacking. We propose an AI system classifying 5 classes of GC histology, which can be perfectly matched to general GC treatment guidance. Design The AI system was designed to mimic the way pathologist understand slides through multi-scale self-attention mechanism using a 2-stage Vision Transformer network. The AI system performance was evaluated on 876 internal endoscopic slides and 336 external endoscopic slides from clinical cohort. We further evaluated practical usability of the AI system on observation of AI-assisted 6 pathologist performance. Results The AI system demonstrates clinical capability by achieving class-average diagnostic sensitivity of above 85% for both internal and external cohort analysis. Furthermore, AI-assisted pathologists showed signiﬁcantly improved diagnostic sensitivity by 10% within 18% saved screen-1
Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

ABSTRACT As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD’s prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence.
Research Situation and Application of Laser Welding Technology

The laser welding is one of important aspects OF laser processing material processing technology application. Many developed countries such as US, European Union, Japan attach great importance to the development and application of laser technology. They are all putting the laser technology into their national development plan, and invest a large amount of money. At present its application domain mainly includes manufacturing, powder metallurgy, automobile industry, electronics industry, biomedicine, aerospace industry, shipbuilding industry and so on.The general situation of laser welding technology, the research situation of domestic and foreign laser welding technology, application of laser welding technology and the development tend of laser welding technology are summaries in this paper. Through this paper we get a quite comprehensive understanding to the laser welding technology research situation and application.
Network meta-analysis: a statistical physics perspective

Network meta-analysis (NMA) is a technique used in medical statistics to combine evidence from multiple medical trials. NMA defines an inference and information processing problem on a network of treatment options and trials connecting the treatments. We believe that statistical physics can offer useful ideas and tools for this area, including from the theory of complex networks, stochastic modelling and simulation techniques. The lack of a unique source that would allow physicists to learn about NMA effectively is a barrier to this. In this article we aim to present the ‘NMA problem’ and existing approaches to it coherently and in a language accessible to statistical physicists. We also summarise existing points of contact between statistical physics and NMA, and describe our ideas of how physics might make a difference for NMA in the future. The overall goal of the article is to attract physicists to this interesting, timely and worthwhile field of research.
Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals

Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier work on gating in the primate prefrontal cortex, that capture the cost of interleaved training and allow the network to learn two tasks in sequence without forgetting. We augment standard stochastic gradient descent with two algorithmic motifs, so-called “sluggish” task units and a Hebbian training step that strengthens connections between task units and hidden units that encode task-relevant information. We found that the “sluggish” units introduce a switch-cost during training, which biases representations under interleaved training towards a joint representation that ignores the contextual cue, while the Hebbian step promotes the formation of a gating scheme from task units to the hidden layer that produces orthogonal representations which are perfectly guarded against interference. Validating the model on previously published human behavioural data revealed that it matches performance of participants who had been trained on blocked or interleaved curricula, and that these performance differences were driven by misestimation of the true category boundary.
Pointwise convergence in nilmanifolds along smooth functions of polynomial growth

We study the equidistribution of orbits of the form $b_1^{a_1(n)}\cdots b_k^{a_k(n)}\Gamma $ in a nilmanifold X, where the sequences $a_i(n)$ arise from smooth functions of polynomial growth belonging to a Hardy field. We show that under certain assumptions on the growth rates of the functions $a_1,\ldots ,a_k$ , these orbits are equidistributed on some subnilmanifold of the space X. As an application of these results and in combination with the Host–Kra structure theorem for measure-preserving systems, as well as some recent seminorm estimates of the author for ergodic averages concerning Hardy field functions, we deduce a norm convergence result for multiple ergodic averages. Our method mainly relies on an equidistribution result of Green and Tao on finite segments of polynomial orbits on a nilmanifold [The quantitative behaviour of polynomial orbits on nilmanifolds. Ann. of Math. (2)175 (2012), 465–540].
Higher-order generalized uncertainty principle applied to gravitational baryogenesis

The gravitational baryogenesis plays an important role in the study of baryon asymmetry. However, the original mechanism of gravitational baryogenesis in the radiation-dominated era leads to the asymmetry factor η equal to zero, which indicates this mechanism may not generate a sufficient baryon asymmetry in the early Universe. In this paper, we investigate the gravitational baryogenesis for the generation of baryon asymmetry in the early Universe by using a new higher-order generalized uncertainty principle (GUP). It is demonstrated that the entropy and the Friedman equation of the Universe deviate from the original cases due to the effect of the higher-order GUP. Those modifications break the thermal equilibrium of the Universe, and in turn produce a non-zero asymmetry factor η. In particular, our results satisfy all of Sakharov's conditions, which indicates that the scheme of explaining baryon asymmetry in the framework of higher-order GUP is feasible. In addition, combining our theoretical results with the observational data, we constraint the GUP parameter β 0, whose bound is between 8.4 × 1010 ∼ 1.1 × 1013.
GJ 229B: Solving the Puzzle of the First Known T Dwarf with the APOLLO Retrieval Code

GJ 229B was the first T dwarf to be discovered in 1995, and its spectrum has been more thoroughly observed than that of most other brown dwarfs. Yet a full spectroscopic analysis of its atmosphere has not been done with modern techniques. This spectrum has several peculiar features, and recent dynamical estimates of GJ 229B’s mass and orbit have disagreed widely, both of which warrant closer investigation. With a separation of tens of astronomical units from its host star, GJ 229B falls near the border of the planet and stellar population formation regimes, so its atmosphere could provide clues to formation processes for intermediate objects of this type. In an effort to resolve these questions, we performed retrievals on published spectra of GJ 229B over a wide range of wavelengths (0.5–5.1 μm) using the open-source APOLLO code. Based on these retrievals, we present a more precise mass estimate of 41.6 ± 3.3 M J and an effective temperature estimate of 869−7+5 K, which are more consistent with evolutionary models for brown dwarfs and suggest an older age for the system of >1.0 Gyr. We also present retrieved molecular abundances for the atmosphere, including replicating the previously observed high CO abundance, and discuss their implications for the formation and evolution of this object. This retrieval effort will give us insight into how to study other brown dwarfs and directly imaged planets, including those observed with JWST and other next-generation telescopes.
New penalized criteria for smooth non-negative tensor factorization with missing entries

Tensor factorization models are widely used in many applied fields such as chemometrics, psychometrics, computer vision or communication networks. Real life data collection is often subject to errors, resulting in missing data. Here we focus in understanding how this issue should be dealt with for nonnegative tensor factorization. We investigate several criteria used for non-negative tensor factorization in the case where some entries are missing. In particular we show how smoothness penalties can compensate the presence of missing values in order to ensure the existence of an optimum. This lead us to propose new criteria with efficient numerical optimization algorithms. Numerical experiments are conducted to support our claims.
Constraining the gravitational-wave spectrum from cosmological first-order phase transitions using data from LIGO-Virgo first three observing runs

We search for a first-order phase transition (PT) gravitational wave (GW) signal from Advanced LIGO and Advanced Virgo's first three observing runs. Due to the large theoretical uncertainties, four shapes of GW energy spectral from bubble and sound wave collisions widely adopted in literature are investigated, separately. Our results indicate that there is no evidence for the existence of such GW signals, and therefore we give the upper limits on the amplitude of GW energy spectrum Ωpt(f *) in the peak frequency range of f * ∈ [5,500] Hz for these four theoretical models, separately. We find that Ωpt(f * ≃ 40 Hz) < 1.3 × 10-8 at 95% credible level, and roughly H */β ≲ 0.1 and α ≲ 1 at 68% credible level in the peak frequency range of 20 ≲ f * ≲ 100 Hz corresponding to the most sensitive frequency band of Advanced LIGO and Advanced Virgo's first three observing runs, where H * is the Hubble parameter when PT happens, β is the bubble nucleation rate and α is the normalized latent heat.
ImageNet Challenging Classification with the Raspberry Pi: An Incremental Local Stochastic Gradient Descent Algorithm

With rising powerful, low-cost embedded devices, the edge computing has become an increasingly popular choice. In this paper, we propose a new incremental local stochastic gradient descent (SGD) tailored on the Raspberry Pi to deal with large ImageNet ILSVRC 2010 dataset having 1,261,405 images with 1,000 classes. The local SGD splits the data block into $k$ partitions using $k$means algorithm and then it learns in the parallel way SGD models in each data partition to classify the data locally. The incremental local SGD sequentially loads small data blocks of the training dataset to learn local SGD models. The numerical test results on Imagenet dataset show that our incremental local SGD algorithm with the Raspberry Pi 4 is faster and more accurate than the state-of-the-art linear SVM run on a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores.
Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-varying Covariates

We propose Bayesian nonparametric Weibull delegate racing (WDR) for survival analysis with competing events and achieve both model interpretability and flexibility. Utilizing a natural mechanism of surviving competing events, we assume a race among a potentially infinite number of sub-events. In doing this, WDR accommodates nonlinear covariate effects with no need of data transformation. Moreover, WDR is able to handle left truncation, time-varying covariates, different types of censoring, and missing event times or types. We develop an efficient MCMC algorithm based on Gibbs sampling for Bayesian inference and provide an \texttt{R} package. Synthetic data analysis and comparison with benchmark approaches demonstrate WDR's outstanding performance and parsimonious nonlinear modeling capacity. In addition, we analyze two real data sets and showcase advantages of WDR. Specifically, we study time to death of three types of lymphoma and show the potential of WDR in modeling nonlinear covariate effects and discovering new diseases. We also use WDR to investigate the age at onset of mild cognitive impairment and interpret the accelerating or decelerating effects of biomarkers on the progression of Alzheimer's disease.
Reliable Time Prediction in the Markov Stochastic Block Model

We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs' literature to design prediction methods that are robust to local clustering errors. We focus specifically on the link prediction and collaborative filtering problems and we introduce a new model selection procedure to infer the number of hidden clusters in the network. Our approaches for reliable prediction in MSBMs are not algorithm-dependent in the sense that they can be applied using your favourite clustering tool. In this paper, we use a recent SDP method to infer the hidden communities and we provide theoretical guarantees. In particular, we identify the relevant signal-to-noise ratio (SNR) in our framework and we prove that the misclassification error decays exponentially fast with respect to this SNR.
CFTR-mediated monocyte/macrophage dysfunction revealed by cystic fibrosis proband-parent comparisons

Cystic fibrosis (CF) is an inherited disorder caused by biallelic mutations of the CF transmembrane conductance regulator (CFTR) gene. Converging evidence suggests that CF carriers with only 1 defective CFTR copy are at increased risk for CF-related conditions and pulmonary infections, but the molecular mechanisms underpinning this effect remain unknown. We performed transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) of CF child-parent trios (proband, father, and mother) and healthy control (HC) PBMCs or THP-1 cells incubated with the plasma of these participants. Transcriptomic analyses revealed suppression of cytokine-enriched immune-related genes (IL-1β, CXCL8, CREM), implicating lipopolysaccharide tolerance in innate immune cells (monocytes) of CF probands and their parents. These data suggest that a homozygous as well as a heterozygous CFTR mutation can modulate the immune/inflammatory system. This conclusion is further supported by the finding of lower numbers of circulating monocytes in CF probands and their parents, compared with HCs, and the abundance of mononuclear phagocyte subsets, which correlated with Pseudomonas aeruginosa infection, lung disease severity, and CF progression in the probands. This study provides insight into demonstrated CFTR-related innate immune dysfunction in individuals with CF and carriers of a CFTR mutation that may serve as a target for personalized therapy.
A discrete choice experiment investigating HIV testing preferences in South Africa

Abstract Background South Africa (SA) has the world’s highest burden of HIV infection, with an estimated 13.7% of the population living with HIV (PLWH/Persons Living With HIV). The early identification of PLWH and rapid engagement of them in HIV treatment are indispensable tools in the fight against HIV transmission. Understanding client preferences for HIV testing may help improve uptake. This study aimed to elicit client preferences for key characteristics of HIV testing options. Methods A discrete-choice experiment (DCE) was conducted among individuals presenting for HIV testing at two public primary healthcare facilities in Cape Town, South Africa. Participants were asked to make nine choices between two unlabeled alternatives that differed in five attributes, in line with previous DCEs conducted in Tanzania and Colombia: testing availability, distance from the testing center, method for obtaining the sample, medication availability at testing centers, and confidentiality. Data were analyzed using a random parameter logit model. Results A total of 206 participants agreed to participate in the study, of whom 199 fully completed the choice tasks. The mean age of the participants was 33.6 years, and most participants were female (83%). Confidentiality was the most important attribute, followed by distance from the testing center and the method of obtaining a sample. Patients preferred finger prick to venipuncture as a method for obtaining the sample. Medication availability at the testing site was also preferred over a referral to an HIV treatment center for a positive HIV test. There were significant variations in preferences among respondents. Conclusion In addition to accentuating the importance of confidentiality, the method for obtaining the sample and the location of sites for collection of medication should be considered in the testing strategy. The variations in preferences within target populations should be considered in identifying optimal testing strategies.
Numerical method of free torsion constant for arbitrary complicated thin-walled cross section

An effective algorithm and model building method for free torsion constant calculation of thin-walled bars with arbitrary complicated thin-walled cross sections was presented.Series of lines with given width were used to build the model of thin-walled cross section,corresponding calculation program was developed based on theory of shear flow,and free torsion constant for arbitrary complicated thin-walled cross section was obtained.The calculation theory and method were applied to the world's largest cable-stayed bridge i.e.,Suzhou—Nantong Yangtze Road Bridge,and the results of this paper were compared with Midas.The results show that the model building method in this paper realizes the seamless connection with CAD,and is convenient to engineering application;the same results can be obtained by different given models of shear flow,this method overcomes the shortcomings of Midas,and the validity and stability of the method are verified.