Miracle-dz/UniFAR-FTUs huggingface dataset:
Knot invariants and M-theory: Proofs and derivations

We construct two distinct yet related M-theory models that provide suitable frameworks for the study of knot invariants. We then focus on the four-dimensional gauge theory that follows from appropriately compactifying one of these M-theory models. We show that this theory has indeed all required properties to host knots. Our analysis provides a unifying picture of the various recent works that attempt an understanding of knot invariants using techniques of four-dimensional physics. This is a companion paper to arXiv:1608.05128, covering all but section 3.3. It presents a detailed mathematical derivation of the main results there, as well as additional material. Among the new insights, those related to supersymmetry and the topological twist are highlighted. This paper offers an alternative, complementary formulation of the contents in~\cite{Dasgupta:2016rhc}, but is self-contained and can be read independently.
Strategies to increase childhood tuberculosis case detection at the primary health care level: Lessons from an active case finding study in Zambia

Introduction In high TB burden settings, it is estimated that 10–20% of total notifications should be children, however, currently only 6–8% of the total TB notifications in Zambia are children. We assessed whether the implementation of a multicomponent strategy, at primary healthcare facilities, that systematically targets barriers at each step of the childhood TB diagnostic cascade can increase childhood TB case detection. Methods We conducted a controlled, interrupted time series analysis to compare childhood TB case notifications before (January 2018—December 2019), and during implementation (January 2020—September 2021) in two intervention and two control Level 1 hospitals in Lusaka, Zambia. At each of the intervention facilities, we implemented a multicomponent strategy constituting: (1) capacity development on childhood TB and interpretation of chest x-ray, (2) TB awareness-raising and demand creation activities, (3) setting up fast track TB services, (4) strengthening of household contact tracing, and (5) improving access to digital chest X-ray for TB screening and Xpert MTB/Rif Ultra for TB diagnosis, through strengthening sample collection in children. Findings Among 5,150 children < 15 years screened at the two intervention facilities during the study period, 503 (9.8% yield) were diagnosed with TB. Of these, 433 (86.1%) were identified through facility-based activities (10.5% yield) and 70 (13.9%) were identified through household contact tracing (6.9% yield). Overall, 446 children (88.7%) children with TB were clinically diagnosed. Following implementation of the multicomponent strategy, the proportion children contributed to total TB notifications immediately changed by +1.5% (95%CI: -3.5, 6.6) and -4.4% (95%CI: -7.5, 1.4) at the intervention and control sites, respectively (difference 6.0% [95%CI: -0.7, 12.7]), p = 0.08); the proportion of childhood notifications increased 0.9% (95%CI: -0.7, 2.5%) each quarter at the intervention sites relative to pre-implementation trends, while declining 1.2% (-95%CI: -1.8, -0.6) at the control sites (difference 2.1% [95%CI: 0.1, 4.2] per quarter between, p = 0.046); this translated into 352 additional and 85 fewer childhood TB notifications at the intervention and control sites, respectively, compared to the pre-implementation period. Conclusion A standardized package of strategies to improve childhood TB detection at primary healthcare facilities was feasible to implement and was associated with a sustained improvement in childhood TB notifications.
Self-adaptive ergodic algorithm for equilibrium problems over the fixed point set

ABSTRACT We introduce a new ergodic algorithm for solving equilibrium problems over the fixed point set of a nonexpansive mapping. In contrast to the existing one in Kim [The Bruck's ergodic iteration method for the Ky Fan inequality over the fixed point set. Int. J. Comput. Math. 94 (2017), pp. 2466–2480], our algorithm uses self-adaptive step sizes. Thanks to that, the proposed algorithm converges under milder conditions. Moreover, at each step of our algorithm, instead of solving strongly convex problems, we only have to compute a subgradient of a convex function. Hence, our algorithm has lower computational cost.
Specific facial signals associate with categories of social actions conveyed through questions

The early recognition of fundamental social actions, like questions, is crucial for understanding the speaker’s intended message and planning a timely response in conversation. Questions themselves may express more than one social action category (e.g., an information request “What time is it? ”, an invitation “Will you come to my party? ” or a criticism “Are you crazy? ”). Although human language use occurs predominantly in a multimodal context, prior research on social actions has mainly focused on the verbal modality. This study breaks new ground by investigating how conversational facial signals may map onto the expression of different types of social actions conveyed through questions. The distribution, timing, and temporal organization of facial signals across social actions was analysed in a rich corpus of naturalistic, dyadic face-to-face Dutch conversations. These social actions were: Information Requests, Understanding Checks, Self-Directed questions, Stance or Sentiment questions, Other-Initiated Repairs, Active Participation questions, questions for Structuring, Initiating or Maintaining Conversation, and Plans and Actions questions. This is the first study to reveal differences in distribution and timing of facial signals across different types of social actions. The findings raise the possibility that facial signals may facilitate social action recognition during language processing in multimodal face-to-face interaction.
Spacecraft debris avoidance using positively invariant constraint admissible sets

To cope with the growing amount of debris in Earth orbit, spacecraft collision avoidance capabilities are necessary. In this paper, we propose an approach to debris avoidance maneuvering based on the use of safe positively invariant sets in order to steer the spacecraft, under closed-loop control, around a piece of debris. A connectivity graph of forced equilibria is computed based on the overlap of these invariant sets, and a graph search algorithm is then implmented in order to ﬁnd the shortest path around the debris. Fast growth distance computation is employed for on-board real-time applicability. Simulation results are presented that illustrate this approach.
“I am a Child!”: Public Perceptions of Black Girls and their Punitive Consequences

How do race and gender stereotypes affect public support for the punishment of Black girls? Across the USA, Black girls are suspended, expelled, arrested, and detained by educational and criminal justice institutions at disproportionate rates. Despite this, there is little research in political science investigating what drives this phenomenon. This paper uses original survey experimental data to examine how stereotypical public perceptions of Black girls shape support for their punishment and punitive policy more generally. We join previous research in finding race- and gender-based stereotypes intersect to produce a distinctive set of public perceptions rooted in the “adultification” of Black girls. In general, Black girls are seen as acting older than their age, more dangerous to others, and more experienced with sex than their peers. But, in an important extension, we also link these adultification stereotypes to support for harsher punishments for Black girls than for their peers and to support for punitive school policies more generally. Together, these findings point to the importance of understanding the intersectional nature of racialized and gendered public attitudes and help to draw the empirical link between the adultification of Black girls and support for their disproportionate punishment among the American public.
The Capital Market Effects of Introducing Private Rights of Action in Securities Regulation: Evidence from the United Kingdom

Securities class actions are among the most controversial topics in the corporate and securities law literature, but despite these actions’ significance, little is known about the impact of their introduction into the legal system. This paper contributes to filling this gap by examining the capital market effects of the adoption of private rights of action (PRAs) in the United Kingdom—a jurisdiction that provides a unique empirical setting for the analysis. The results indicate that the introduction of the rights was associated with enhanced market liquidity and value. Although this finding is most directly relevant to the cost-benefit analysis of private litigation in the United Kingdom, it also suggests that eliminating PRAs in jurisdictions that have adopted them (a reform that has been proposed in the United States) may lead to lower liquidity and market value.
Effect of Hanger Reflex on Detection Thresholds for Hand Redirection during Forearm Rotation

Hand redirection is a technique used in virtual reality (VR) to alter the virtual hand position from the real hand position, enabling extended interaction in VR. This technique capitalizes on the visual system’s dominance over proprioception, whereby discrepancies between the virtual hand mapping and the reality below a specific threshold are indiscernible to users. However, these detection thresholds are often minute. In this study, we explore the impact of haptic stimuli known as the ‘hanger reflex’ on the detection threshold of hand redirection, specifically concerning forearm pronation, which involves rotating the user’s palm downward. To achieve this, we conducted a user study that involved manipulating the degree of forearm rotation and measuring detection thresholds in three conditions: Neutral, where no force was applied; Inward, where haptic feedback was provided to match the direction of hand rotation through the hanger reflex; and Outward, where haptic feedback was provided in the opposite direction of hand rotation. The results demonstrate that, in the Neutral condition, the virtual degree of rotation can vary between 0.896 and 1.136 times the actual amount without being perceptible to users. However, under the Inward condition, this range increases to between 0.888 and 1.204 times the actual amount, representing a significant expansion of the detection threshold range. Consequently, our findings contribute to the field of upper limb rehabilitation.
The relationship between human milk, a functional nutrient, and microbiota

Abstract The intestinal microbiota begins to take shape in the mother’s womb, changes depending on many factors. It is known that the intestinal microbiota has an important role in the maturation of the immune system, also in the prevention of diseases that occur in newborn, childhood, adulthood. Nutrition is the main factor on the development of microbiota in infants after birth. The microbiota compositions of breastfed infants are different from formula-fed infants. Breast milk oligosaccharides play an important role in the development of infants’ microbiota. The higher number of Bifidobacterium species and lower α and β diversity in breastfed infants are considered protective. A dysbiosis occurring in the microbiota can cause adverse effects on health. Human milk oligosaccharides also have protective effects on the microbiota. These protective effects are to promote the growth of intestinal microbiota, prevent the adhesion of viruses to the colon, promote the growth of Bifidobacterium with its prebiotic effect. Short-chain fatty acids resulting from their digestion, also have protective effects. Another component that shapes the gut microbiota is HM glycoproteins. The aim of this study is to examine the effect of breast milk on the development of microbiota, to present the results by scanning the literature. Graphical Abtsract
Epirubicin: Biological Properties, Analytical Methods, and Drug Delivery Nanosystems

Abstract Epirubicin (EPI) is a chemotherapeutic agent belonging to the anthracycline drug class indicated for treating several tumors. It acts by suppressing the DNA and RNA synthesis by intercalating between their base pair. However, several side effects are associated with this therapy, including cardiotoxicity and myelosuppression. Therefore, EPI delivery in nanosystems has been an interesting strategy to overcome these limitations and improve the safety and efficacy of EPI. Thus, analytical methods have been used to understand and characterize these nanosystems, including spectrophotometric, spectrofluorimetric, and chromatography. Spectrophotometric and spectrofluorimetric methods have been used to quantify EPI in less complex matrices due to their efficiency, low cost, and green chemistry character. By contrast, high-performance liquid chromatography is a suitable method for detecting EPI in more complex matrices (e.g., plasm and urine) owing to its high sensitivity. This review summarizes physicochemical and pharmacokinetic properties of EPI, its application in drug delivery nanosystems, and the analytical methods employed in its quantification in different matrices, including blood, plasm, urine, and drug delivery nanosystems.
An ARL-unbiased modified chart for monitoring autoregressive counts with geometric marginal distributions

Abstract Geometrically distributed counts arise in the industry. Ideally, they should be monitored using a control chart whose average run length (ARL) function achieves a maximum when the process is in control; that is, the chart is ARL-unbiased. Moreover, its in-control ARL should coincide with a reasonably large and prespecified value. Because dependence among successive geometric counts is occasionally a more sensible assumption than independence, we assess the impact of using an ARL-unbiased chart specifically designed for monitoring independent geometric counts when, in fact, these counts are autocorrelated. We derive an ARL-unbiased modified chart for monitoring geometric first-order integer-valued autoregressive or GINAR(1) counts. We provide compelling illustrations of this chart and discuss its use to monitor other autoregressive counts with a geometric marginal distribution.
A Numerical and Experimental Study of Wall Pressure Caused by an Underwater Explosion Bubble

The bubble dynamics behaviors and the pressure in the wall center are investigated through experimental method and numerical study. In the experiment, the dynamics of an underwater explosion (UNDEX) bubble beneath a rigid wall are captured by high-speed camera and the wall pressure in the wall center is measured by pressure transducer. To reveal the process and mechanism of the pressure on a rigid wall during the first bubble collapse, numerical studies based on boundary element method (BIM) are applied. Numerical results with two different stand-off parameters (γ=0.38 and γ=0.90) show excellent agreement with experiment measurements and observations. According to the experimental and the numerical results, we can conclude that the first peak is caused by the reentrant jet impact and the following splashing effect enlarged the duration of the first jet impact. When γ=0.38, the splashing jet has a strong impact on the minimum volume bubble, a number of tiny bubbles, formed like bubble ring, are created and collapse more rapidly owing to the surrounding high pressure and emit multi shock waves. When γ=0.90, the pressure field around the bubble is low enough only a weak rebounding bubble peak occurs.
Biomimetic computer-to-brain communication restoring naturalistic touch sensations via peripheral nerve stimulation

Artificial communication with the brain through peripheral nerve stimulation recently showed promising results in people with sensorimotor deficits. However, these efforts fall short in delivering close-to-natural rich sensory experience, resulting in the necessity to propose novel venues for converting sensory information into neural stimulation patterns, which would possibly enable intuitive and natural sensations. To this aim, we designed and tested a biomimetic neurostimulation framework inspired by nature, able “to write” physiologically plausible information back into the residual healthy nervous system. Starting from the in-silico model of mechanoreceptors, we designed biomimetic policies of stimulation, emulating the activity of different afferent units. Then, we experimentally assessed these novel paradigms, alongside mechanical touch and commonly used, linear neuromodulations. We explored the somatosensory neuroaxis by stimulating the nerve while recording the neural responses at the dorsal root ganglion and spinal cord of decerebrated cats. Biomimetic stimulation resulted in a neural activity that travels consistently along the neuroaxis, producing the spatio-temporal neural dynamic more like the naturally evoked one. Finally, we then implemented these paradigms within the bionic device and tested it with patients. Biomimetic neurostimulations resulted in higher mobility and decreased mental effort compared to traditional approaches. The results of this neuroscience-driven technology inspired by the human body could be a model for the development of novel assistive neurotechnologies.
Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data

We use open source human gut microbiome data to learn a microbial “language” model by adapting techniques from Natural Language Processing (NLP). Our microbial “language” model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift. We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model’s predictions for IBD. Author summary Human microbiomes and their interactions with various body systems have been linked to a wide range of diseases and lifestyle variables. To understand these links, citizen science projects such as the American Gut Project (AGP) have provided large open-source datasets for microbiome investigation. In this work we leverage such open-source data and learn a “language” model for human gut microbiomes using techniques derived from natural language processing. We train the “language” model to capture the interactions among different microbial species and the common compositional patterns that shape gut microbiome communities. By considering the entirety of species within a sample and their interactions, our model produces a representation that enables contextualized interpretation of individual bacterial species within their microbial environment. We demonstrate that our sample representation enhances prediction performance compared to baseline methods across multiple microbiome tasks including prediction of Irritable Bowel Disease (IBD) and diet patterns. Furthermore, our learned representation yields a robust IBD prediction model that generalizes well to independent data collected from different populations. To gain insight into our model’s workings, we present interpretation results that showcase its ability to learn biologically meaningful representations.
The role of squid for food web structure and community-level metabolism

Squid differ from fish by their high growth rate, short life span and feeding behaviour. Their fast life strategy is thought to impose a high predation pressure on zooplankton, fish and other squid preys, and a rapid transfer of energy to upper trophic-levels of marine food webs. However, there is a lack of understanding of how squid’s fast life cycle affects the food-web structure, which is needed to project squid biomass across marine regions under shifting climatic conditions. Here, we examine the role of squid on community metabolism and biomass by collecting data on squid somatic growth and incorporating squid in a size- and trait-based fish community model. We show that squid have a 5 times higher average somatic growth rate than fish. Due to their high food demands, squid are constrained to regions of high pelagic secondary production. The presence of squid in these systems is associated with a reduction in total upper trophic level biomass. This decline is caused by an increase in community-level respiration losses associated with squid. Our results indicate that squid might have a large impact on ecosystem structure even at relatively low standing stock biomasses. Consequently, the recent proliferation of squid in ecosystems around the world is likely to have significant ecological and socio-economic impacts.
Multiweek Prediction Skill Assessment of Arctic Sea Ice Variability in the CFSv2

Skillful Arctic Sea ice prediction is becoming increasingly important because of its societal, industrial, and economic impacts over the polar regions and potential influence on lower-latitude weather and climate variability. In this work, we evaluate the multiweek forecast skill of Arctic sea ice using the Climate Forecast System, version 2 (CFSv2). To the authors’ knowledge, this is the first effort to diagnose and assess the skill of multiweek Arctic sea ice prediction from a coupled atmosphere–ocean model. Analysis of a suite of retrospective 45-day forecasts spanning 1999–2015 shows that CFSv2 captures general features of sea ice concentration (SIC) variability. Total SIC variability is dominated by interannual variability, which accounts for more than 60% of the total variance. Submonthly variability accounts for 29% of the total variance in December, 20% in March and June, and 12.5% in September. We assess the ability of CFSv2 to predict the pan-Arctic SIC, as well as regional SIC in nine Arctic regions. Results show that the SIC prediction skill is highly region dependent (e.g., higher prediction skill for Kara/Barents Seas and lower for the Canadian Archipelago). Overall, the maximum anomaly correlation coefficient (ACC) of SIC for both melt and freeze-up seasons is near the marginal zones, and their spatial distribution shows a relationship with the distribution of the variance. If the ACC of 0.5 is taken as the critical value for skillful prediction, the predictability of weekly SIC near the marginal zones is about 5–6 weeks. Prediction skill for Arctic sea ice extent is above 0.6 for the entire six target weeks and has a large contribution from interannual variability.