Miracle-dz/UniFAR-FTUs huggingface dataset:
A super-resolution network using channel attention retention for pathology images

Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficiency by controlling the number of blocks. Meanwhile, a new linear loss was introduced to optimize the network. To evaluate the work and compare multiple SR works, a benchmark dataset bcSR was created, which forces a model training on wider and more critical regions. The results show that the proposed model outperforms state-of-the-art methods in both performance and efficiency, and the newly created dataset significantly improves the reconstruction quality of all compared models. Moreover, image classification experiments demonstrate that the suggested network improves the performance of downstream tasks in medical diagnosis scenarios. The proposed network and dataset provide effective priors for the SR task of pathology images, which significantly improves the diagnosis of relevant medical staff. The source code and the dataset are available on https://github.com/MoyangSensei/CARN-Pytorch.
The Relationship Between Child Sexual Abuse and Sexual Dysfunction in Adults: A Meta-Analysis

Child sexual abuse (CSA) has been recognized as a risk factor for sexual dysfunction and has attracted increasing attention. However, controversies remain regarding related research. The aim is to calculate the pooled effect size estimate for the correlation between CSA and sexual dysfunction in adults by meta-analysis. Five bibliographic databases (PubMed, Cochrane Library, Web of Science, Embase, and PsycINFO) were comprehensively searched to clarify the association between CSA and sexual dysfunction in adults. We used a fixed-effects model to determine the total pooled effect size estimate and reported odds ratios (ORs) and the corresponding 95% confidence intervals (CIs). Subgroup analysis, publication bias analysis, and sensitivity analysis were conducted. Adults who had a history of CSA experienced a higher proportion of sexual dysfunction than adults with no history of CSA (OR = 1.68, 95% CI [1.49, 1.87]). Subgroup analysis showed that women with a history of CSA reported a higher proportion of sexual dysfunction than men with a history of CSA (men: OR = 1.45, 95% CI [1.05, 1.84]; women: OR = 1.62, 95% CI [1.42, 1.83]). The estimates of the effect sizes differed substantially depending on the CSA and sexual dysfunction instruments that were used in each study and the region of each sample. This meta-analysis provides conclusive evidence of an association between CSA and sexual dysfunction in adults. Currently known interventions for the treatment of sexual dysfunction after CSA have only been evaluated in women, so specific interventions should be designed for men CSA survivors who experience sexual impairment.
Diversity of Phytophagous and Predatory Mites on Mangrove and Agri-Horticultural Crops in Sundarban Biosphere Reserve, West Bengal

Sundarban Biosphere Reserve (SBR) is very rich with at least 36 species of mangrove vegetation of the 55 species of mangrove plants that are known to occur in the world. Besides, during the past 2-3 decades, agricultural horticultural crops have been started growing in different re-claimed areas of SBR which have further added to the vegetational richness of this area. Expectedly, the plant mite fauna w.s. r. to those occurring on mangrove vegetation and diverse types of agrihorticultural crops are also likely to be diverse. Unfortunately, the knowledge about the plant mite faunal wealth of this region is very inadequately explored as appeared from the fact that only 6 species are so far known occurring on mangrove vegetation of that region (Gupta, 1992) and practically nothing is known about the phytophagous and predatory mite fauna occurring on agrihorticultural crops. Hence, with a view to exploring the plant mite faunal diversity of SBR and to study their bio-ecological aspects, this project was taken up and the present paper gives an idea about the Species Richness Index, Species Diversity Index, Index of Dominance, Relative Abundance, based on the observations made at Bamankhali, Sagar Island, during November, 2000 to October, 2001. This is the first such attempt to collect information of this kind on plant mite fauna from SBR and surprisingly such information is rather unavailable from most parts of India.
Multi-key binary search and the related performance

Binary Search is efficient due to it's logarithmic time complexity. It is used to identify the position of a key in a sorted list. Often, computer applications require searching for two to more different keys at the same execution. In this paper, a hybrid algorithm to perform the binary search with 2 to m different keys (m is an integer greater than or equal to 2) in a sorted list of elements is proposed. An m-key version of the proposed algorithm requires considering (2m + 1) different cases. Correctness proof of the algorithm is established using induction on the list size, n. Time complexity of the proposed algorithm is a function of 2 variables, namely, the number of keys, m and the list size, n, and is given as, O(mlog(n)) in both the worst and the average cases. The best case complexity is linear, which is O(m). Performance of 2 and 3-key versions is compared with the classical single key version. Possible key index combinations with the multi-key search strategies are also explored.
Automatic pain recognition techniques: a state-of- the-art review

Pain is a dynamic and subjective experience that can be difficult to measure. Automated clinical pain assessment method offers a lot of potential, and they're not widely employed in medical practice presently. There is now an a need for a comprehensive and precise method to identify acute pain among intensive care units in order to assist professionals in dispensing pain relievers at the proper dosage and on time. We review and discuss autonomous pain identification algorithms in this article also provide an introduction of pain processes and reactions, as well as a discussion of commonly used clinical pain assessment techniques and shared datasets.
Whether it is safe to start anticoagulation after intracranial hemorrhage within 2 weeks: A systematic review and meta‐analysis

Abstract Whether restarting anticoagulation (RA) treatment after intracranial hemorrhage (ICH) is still controversial. We performed a systematic review and meta‐analysis to summarize the relationship between anticoagulation after ICH with the recurrence of hemorrhagic events, ischemic events, and long‐term mortality. Medline, Embase, and the Cochrane Central Register of Controlled Trials, from inception to November 2020. We searched the published medical literature to ensure cohort studies involving ICH associated with anticoagulation in adults. Primary outcomes were long‐term mortality, hemorrhagic events, and ischemic events (myocardial infarction, pulmonary embolism, ischemic stroke, or systemic embolization). We concluded seven retrospective cohorts, including 1876 intracranial hemorrhage patients with indications of anticoagulation. The ratio of the anticoagulant restart was 35.3% (664n). RA was associated with a significantly lower incidence of recurrent ischemic events (pooled odds ratio [OR] 0.29, 95% confidence interval [CI] 0.19% to 0.45%, p = 0.97) and death events (pooled OR 0.56, 95% CI 0.40%–0.79%, p = 0.27). There is no evidence that early recovery of anticoagulation (within 2 weeks or 1 month) is associated with the occurrence of hemorrhagic events (within 2 weeks: pooled OR 0.80, 95% CI 0.3–2.12, p = 0.52 vs. within 1 month: pooled OR 1.14, 95% CI 0.77–1.68, p = 0.82). Based on these, recovery of anticoagulation after ICH is beneficial for long‐term mortality and recurrence of ischemic events. The meta‐analysis showed a resumption of oral anticoagulation within 2 weeks or 1 month in patients who had a cerebral hemorrhage was beneficial and did not increase the risk of hemorrhagic events and reduced the occurrence of ischemic and fatal endpoint events.
Application of Helicopter Time-domain EM for Mine Operations

Summary This paper presents examples of SkyTEM helicopter-borne time-domain (HTEM) systems recently applied to solve a variety of environmental and engineering problems. The mining industry has traditionally used airborne geophysical methods almost exclusively for mineral exploration. However, this paper provides examples of how HTEM data can inform decisions relevant for mine operations. Examples comprise delineation of aquifers that may (1) pose a risk to underground workings and (2) may be threatened by seepage from tailing dames, delineation of clay caps that can act as shallow gas traps endangering drilling operations, and mapping of faults and taliks the location of which must be considered when placing mine infrastructure.
Generalization in Accent Adaptation as Hierarchical Bayesian Inference

With adequate experience, listeners improve their ability to comprehend accented speech. Previous work demonstrates that listeners who adapt to one accented talker generalize that adaptation to other accented talkers - exposure to multiple talkers of the same accent facilitates comprehension of a novel talker of that accent (Bradlow & Bent, 2008) and exposure to multiple novel accents facilitates comprehension of yet another novel accent (Baese-Berk, Bradlow, & Wright, 2013). We propose a model of accent adaptation in which the task of accent adaptation is represented as a problem of hierarchical Bayesian inference, which assumes that listeners simultaneously learn about the distribution of talkers, accent groups, and accented speech more generally. We show that a hierarchical Bayesian model can predict qualitative ﬁndings of generalization of accent adaptation, but that a non-hierarchical model cannot. The hierarchical Bayesian model also provides better quantitative predictions about sentence-level performance at test. Taken together, these ﬁndings support a view of accent adaptation as hierarchical inference.
Finding All Impossible Differentials When Considering the DDT

Impossible diﬀerential (ID) cryptanalysis is one of the most important attacks on block ciphers. The Mixed Integer Linear Program-ming (MILP) model is a popular method to determine whether a speciﬁc diﬀerence pair is an ID. Unfortunately, due to the huge search space (ap-proximately 2 2 n for a cipher with a block size n bits), we cannot leverage this technique to exhaust all diﬀerence pairs, which is a well-known long-standing problem. In this paper, we propose a systematic method to ﬁnd all IDs for SPN block ciphers. The idea is to partition the whole diﬀerence pair space into lots of small disjoint sets, each of which has a representative diﬀerence pair. All diﬀerence pairs in one small set are possible if its representative pair is possible, and this can be conveniently checked by the MILP model. In this way, the overall search space is drastically reduced to a practical size by excluding the sets containing no IDs. We then examine the remaining diﬀerence pairs to identify all IDs (if some IDs exist). If our method cannot ﬁnd any ID, the target cipher is proved free of ID distinguishers. Our method works especially well for SPN ciphers with block size 64. We apply our method to SKINNY-64 and successfully ﬁnd all 432 and 12 truncated IDs (we ﬁnd all IDs but all of them can be assembled into certain truncated IDs) for 11 and 12 rounds, respectively. We also prove, for the ﬁrst time, that 13-round SKINNY-64 is free of ID distinguishers even achieve the same goal. For GIFT-128, we exhausted all diﬀerence patterns that have an active superbox in the plaintext and ciphertext and proved there is no ID of such patterns for 8 rounds. Although we have searched for a larger or even full space for IDs, no longer ID distinguishers have been found. This implies the reasonable-ness of the intuition that a small number (usually one or two) of active bits/words at the beginning and end of an ID will be the longest.
Oblivious Revocable Functions and Encrypted Indexing

Many online applications, such as online ﬁle backup services, support the sharing of indexed data between a set of devices. These systems may oﬀer client-side encryption of the data, so that the stored data is inaccessible to the online host. A potentially desirable goal in this setting would be to protect not just the contents of the backed-up ﬁles, but also their identiﬁers. However, as these identiﬁers are typically used for indexing, a deterministic consistent mapping across devices is necessary. Additionally, in a multi-device setting, it may be desirable to maintain an ability to revoke a device’s access—e.g. through rotating encryption keys for new data. We present a new primitive, called the Oblivious Revocable Function (ORF), which operates in the above setting and allows identiﬁers to be obliviously mapped to a consistent value across multiple devices, while enabling the server to permanently remove an individual device’s ability to map values. This permits a stronger threat model against metadata, in which metadata cannot be derived from identiﬁers by a revoked device colluding with the service provider, so long as the service provider was honest at the instant of revocation. We describe a simple Diﬃe-Hellman-based construction that achieves ORFs and provide a proof of security under the UC framework.
ROBUST KERNEL ESTIMATOR FOR DENSITIES OF UNKNOWN

Results on nonparametric kernel estimators of density differ according to the assumed degree of density smoothness; it is often assumed that the density function is at least twice differentiable. However, there are cases where non-smooth density functions may be of interest. We provide asymptotic results for kernel estimation of a continuous density for an arbitrary bandwidth/kernel pair. We also derive the limit joint distribution of kernel density estimators coresponding to different bandwidths and kernel functions. Using these reults, we construct an estimator that combines several estimators for different bandwidth/kernel pairs to protect against the negative consequences of errors in assumptions about order of smoothness. The results of a Monte Carlo experiment confirm the usefulness of the combined estimator. We demonstrate that while in the standard normal case the combined estimator has a relatively higher mean squared error than the standard kernel estimator, both estimators are highly accurate. On the other hand, for a non-smooth density where the MSE gets very large, the combined estimator provides uniformly better results than the standard estimator.
Answering Geosciences Research Questions at a Global Scale via a Hybrid Machine-Human Learning Approach: A Case Study of the Link between Climate and Volcanism

A common challenge in science is the human capability to evaluate the real impact of an observation and a data set. This is a complex task due to having only partial information and/or to the complexity of the problem, requiring different fields to be combined. In order to overcome these important limitations, we need to be able to review all the available data and interpretations. This would allow us to evaluate the global distribution of a specific process or phenomenon of interest. The increasing number of scientific publications prevents scientists from being able to keep up with all the available literature especially when scientific papers cross disciplines. These chal-lenges prevent us from evaluating the global impact of a certain process and are particularly relevant today given the impact of our scientific assessment on one of the most pressing issues of our time, which is climate change and its impact on society. We present here an application of artificial intelligence to geosciences: We conduct a systematic analysis of geoscience literature through a hybrid machine-human approach. Such applications are more common in other fields such as biomedicine and are in their infancy in the geosciences because of various difficulties the machines encounter in parsing geologic literature. We describe here some of these limitations and how we overcame them. We then use the following case study as an example to test our approach: We ask whether climate is influenced by volcanism in the geologic past. Our case study results show, as expected, that most analyzed literature in this experiment conclude that volcanism influ-ences climate change in deep time, but there is no complete consensus on this question. Similarly, any question of potential global significance, such as the impact of human activities on climate change, can be posed as an interrogating
Automatically Testing Solvers for String and Regular Expressions Constraints

SMT solvers are used to determine the satisfiablity of formulas expressed in first-order-logic. The solvers can support typical theories such as Integers and Booleans but this work focuses on strings and regular expressions constraints. SMT solvers have many applications, such as program verification and test case generation. All applications rely on the queries to be answered correctly. Nevertheless, as formulas are highly complex, ensuring correctness is hard. To detect incorrectly answered queries by the solvers we extend a technique, which automatically generates formulas that are either satisfiable or unsatisfiable by construction and then uses this ground truth to test the solvers. We extended the technique to also support formulas containing regular expressions, as well as formulas combining regular expressions and arrays/bitvectors. This thesis will show, that the for regular expressions extended technique, was able to detect soundness errors in state-of-the-art solver Z3. Furthermore we generated formulas, which expose incompleteness and performance issues in solvers.
A Neural Network

In this paper, we propose two neural network{based approaches, namely, One Speaker One Network and One Speaker Multiple Networks, for text-dependent speaker veri(cid:12)cation using suprasegmentalfeatures. The suprasegmental features used for this study are pitch accent and durational features. These features are extracted using properties of intonation patterns and duration. We have proposed an approach to combine evidence present at the segmental and suprasegmental levels to improve the performance of the veri(cid:12)cation sys-tem.
Space-Time-Resolved Simulation of Photon Vacuum Polarization and Scattering

We create a limited particle content model in one spatial dimension to study the interaction between single particle photon states, and electron-positron pair states. We use this model to simulate the phenomena of vacuum polarization in a propagating photon, and Bhabha Scattering. This approach supplements the S-matrix approach to Quantum Field Theory, which provides an effective approach for predicting the results of scattering experiments but gives little physical insight into the dynamics of the field during interactions. We find divergences arise in attempting to calculate the eigenvalues of particles states with interaction in out limited content model. This leads to areas requiring further study of further study, namely, how to implement renormalization within space-time-resolved frameworks.
Association between Blood N-3 Fatty Acid Levels and Risk for COVID-19 in the UK biobank

Background: The role of nutritional status and risk for contracting and/or suffering adverse outcomes from COVID-19 infection are unclear. Preliminary studies suggest that higher n-3 PUFA intakes may be protective. Objectives: The purpose of this study was to compare risk for three COVID-19 outcomes (testing positive, hospitalization, and death) as a function of baseline plasma DHA levels. Methods: DHA levels (% of total fatty acids) were measured by NMR. The three outcomes and relevant covariates were available for 110,688 subjects (hospitalization and death) and for 26,620 ever-tested subjects (positive COVID-19 PCR test result) via the UKBiobank prospective cohort study. Outcome data between January 1, 2020 and March 23, 2021 were included. Estimated Omega-3 Index (red blood cell EPA+DHA%) levels across DHA% quintiles were estimated. Multi-variable Cox-proportional hazards models were constructed and linear (per 1-SD) relations with risk for each outcome were computed. Results: In the fully adjusted models, subjects in quintile 5 of DHA% were 21% less likely to test positive than those in quintile 1 (p<0.001), and the risk for a positive test was 8% lower for each 1-SD increase in plasma DHA% (p<0.001). Quintile 5 subjects were also 27% less likely to be hospitalized than those in quintile 1 (P<0.05), and risk for hospitalization was 11% lower per 1-SD increase in DHA% (p<0.001). For death with COVID-19, risk was monotonically lower through quintile 4 (p<0.05 vs quintile 1), but in quintile 5, the risk reduction was partially attenuated and became non-significant. Estimated Omega-3 Index values across DHA quintiles ranged from 3.5% (quintile 1) to 8% (quintile 5). Conclusions: These findings suggest that nutritional strategies to increase circulating n-3 PUFA levels, such as increased consumption of oily fish and/or use of n-3 fatty acid supplements, may reduce risk for adverse COVID-19 outcomes.
Combining cash transfers and cognitive behavioral therapy to reduce antisocial behavior in young men: A mediation analysis of a randomized controlled trial in Liberia

Background Interventions that combine cognitive behavioral therapy (CBT) with unconditional cash transfers (UCT) reduce the risk of antisocial behavior (ASB), but the underlying mechanisms are unclear. In this paper, we test the role of psychological and cognitive mechanisms in explaining this effect. We assessed the mediating role of executive function, self-control, and time preferences. Methods We used data from the Sustainable Transformation of Youth in Liberia, a community-based randomized controlled trial of criminally engaged men. The men were randomized into: Group-1: control (n=237); and Group-2: CBT+UCT (n=207). ASB was measured 12-13 months after the interventions were completed, and the following mediators were assessed 2-5 weeks later: (i) self-control, (ii) time preferences and (iii) executive functions. We estimated the natural direct effect (NDE) and the natural indirect effect (NIE) of the intervention over ASB. Results Self-control, time preferences and a weighted index of all three mediators were associated with ASB scores, but the intervention had an effect on time preferences only [B= 0.09 95%CI (0.03; 0.15)]. There was no evidence that the effect of the intervention on ASB was mediated by self-control [B NIE = 0.007 95%CI (-0.01; 0.02)], time preferences [B NIE = -0.02 95%CI (-0.05; 0.01)], executive functions [B NIE = 0.002 95%CI (-0.002; 0.006)] or the weighted index of the mediators [B NIE = -0.0005 95%CI (-0.03; 0.02)]. Conclusions UCT and CBT lead to improvements in ASB, even in the absence of mediation via psychological and cognitive functions. Findings suggest that the causal mechanisms may involve non-psychological pathways.