Miracle-dz/UniFAR-FTUs huggingface dataset:
A Theory of Predictive Dissonance: Predictive Processing Presents a New Take on Cognitive Dissonance

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance. ” First, the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism.
Therapeutic Effect of Ecklonia cava Extract in Letrozole-Induced Polycystic Ovary Syndrome Rats

Polycystic ovary syndrome (PCOS) is an endocrinal disorder that afflicts mainly women of childbearing age. The symptoms of PCOS are irregular menstrual cycles, weight gain, subfertility and infertility. However, because the etiology is unclear, management and treatment methods for PCOS are not well established. Recently, natural substances have been used for PCOS therapy. Ecklonia cava (E. cava) is a well-known natural substance that attenuates the effects of inflammation, allergies, and cancer. In this study, we investigated the effects of E. cava extract in rats with PCOS. When rats with letrozole-induced PCOS were exposed to the E. cava extract, the regular estrus cycle was restored, similar to that in placebo rats. Hormone levels, including the levels of testosterone, estrogen, luteinizing hormone (LH), follicle stimulating hormone (FSH), and anti-Müllerian hormone (AMH), were restored to their normal states. Histological analysis revealed that the polycystic ovary symptoms were significantly decreased in the E. cava-treated rats and were comparable to those of normal ovaries. At the transcriptional and translational levels, Ar, and Esr2 levels were markedly increased in the E. cava-treated rats with PCOS compared with the rats with letrozole-induced PCOS. These results suggest that the E. cava extract inhibits the symptoms of PCOS by restoring imbalanced hormonal levels and irregular ovarian cycles in letrozole-induced female rats.
Pre-training Graph Neural Networks with Kernels

Many machine learning techniques have been proposed in the last few years to process data represented in graph-structured form. Graphs can be used to model several scenarios, from molecules and materials to RNA secondary structures. Several kernel functions have been defined on graphs that coupled with kernelized learning algorithms, have shown state-of-the-art performances on many tasks. Recently, several definitions of Neural Networks for Graph (GNNs) have been proposed, but their accuracy is not yet satisfying. In this paper, we propose a task-independent pre-training methodology that allows a GNN to learn the representation induced by state-of-the-art graph kernels. Then, the supervised learning phase will fine-tune this representation for the task at hand. The proposed technique is agnostic on the adopted GNN architecture and kernel function, and shows consistent improvements in the predictive performance of GNNs in our preliminary experimental results.
Evolutionary diversity optimization using multi-objective indicators

Evolutionary diversity optimization aims to compute a set of solutions that are diverse in the search space or instance feature space, and where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.
Phenotypic and Functional Profiles of Antigen-Specific CD4+ and CD8+ T Cells Associated With Infection Control in Patients With Cutaneous Leishmaniasis

The host immunological response is a key factor determining the pathogenesis of cutaneous leishmaniasis. It is known that a Th1 cellular response is associated with infection control and that antigen-specific memory T cells are necessary for the development of a rapid and strong protective cellular response. The present manuscript reports the analysis of the functional and phenotypic profiles of antigen-specific CD4+ and CD8+ T cells from patients cured of cutaneous leishmaniasis (CL), patients with an active process of cutaneous leishmaniasis, asymptomatic individuals with a positive Montenegro test and healthy donors (HD). Peripheral blood mononuclear cells (PBMCs) from the patients exhibited a lymphoproliferative capacity after stimulation with total soluble protein from either Leishmania panamensis (SLpA) or Leishmania infantum (SLiA) or with a recombinant paraflagellar rod protein-1 (rPFR1). Higher frequencies of antigen-specific TNAIVE cells, mainly following stimulation with rPFR1, were observed in asymptomatic and cured patients than in patients with active cutaneous leishmaniasis, while T cells from patients with active cutaneous leishmaniasis showed a higher percentage of effector memory T cells (TEM for CD4+ T cells and TEMRA for CD8+ T cells). The amount of antigen-specific CD57+/CD8+ TEMRA cells in patients with active cutaneous leishmaniasis was higher than that in cured patients and asymptomatic subjects. Regarding functionality, a more robust multifunctional CD8+ T cell response was detected in cured patients than in those with active cutaneous leishmaniasis. Moreover, cured patients showed a significant increase in the frequency of cells expressing a Th1-type cytotoxic production profile (IFN-γ+/granzyme-B/+perforin+). Patients with an active leishmaniosis process had a significantly higher frequency of CD8+ T cells expressing the inhibitory CD160 and 2B4 receptors than did cured patients. The expression profile observed in cured patients could be indicative of an imbalance toward a CD8+ Th1 response, which could be associated with infection control; consequently, the determination of this profile could be a useful tool for facilitating the clinical follow-up of patients with cutaneous leishmaniasis. The results also suggest a possible exhaustion process of CD8+ T cells associated with the evolution of Leishmania infection.
Longitudinal Associations Between Serum Cytokine Levels and Dementia

Background: The purpose of this study was to investigate whether long-term inflammation is related to the incidence of dementia in a prospective observational study. Methods: In total, 732 Korean community-dwelling elderly people >65 years were evaluated at baseline. Of the 625 without dementia, 518 (83%) were followed over a 2.4-years period, and the incidence of dementia was determined. Cytokine [interleukin (IL)-1α, IL-1β, IL-6, IL-8, and tumor necrosis factor (TNF)-α] levels were measured at baseline and follow-up. The individual and combined effects of cytokine levels on dementia were evaluated after adjusting for potential covariates (lifestyle factors, demographics, disability, cognitive function, and presence of the APOE e4 allele) and a Bonferroni correction. Results: Incident dementia was associated with increased serum cytokine levels after 2 years; the association remained significant for TNF-α, IL1-α, and IL-1β concentrations even after applying a Bonferroni correction. The analysis of the combined effects of the five cytokines showed independent associations between increases in the summed number of higher cytokine levels, between baseline and follow-up. However, incident dementia was not expected based on higher baseline pro-inflammatory cytokine levels. Conclusion: Our results suggest that dementia may precede changes in serum cytokine levels and inflammatory processes, rather than resulting from elevated pro-inflammatory cytokines.
Nerve Repair Using Decellularized Nerve Grafts in Rat Models. A Review of the Literature

Peripheral nerve regeneration after severe traumatic nerve injury is a relevant clinical problem. Several different strategies have been investigated to solve the problem of bridging the nerve gap. Among these, the use of decellularized nerve grafts has been proposed as an alternative to auto/isografts, which represent the current gold standard in the treatment of severe nerve injury. This study reports the results of a systematic review of the literature published between January 2007 and October 2017. The aim was to quantitatively analyze the effectiveness of decellularized nerve grafts in rat experimental models. The review included 33 studies in which eight different decellularization protocols were described. The decellularized nerve grafts were reported to be immunologically safe and able to support both functional and morphological regeneration after nerve injury. Chemical protocols were found to be superior to physical protocols. However, further research is needed to optimize preparation protocols, including recellularization, improve their effectiveness, and substitute the current gold standard, especially in the repair of long nerve defects.
DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules

We present a simple technique that allows capsule models to detect adversarial images. In addition to being trained to classify images, the capsule model is trained to reconstruct the images from the pose parameters and identity of the correct top-level capsule. Adversarial images do not look like a typical member of the predicted class and they have much larger reconstruction errors when the reconstruction is produced from the top-level capsule for that class. We show that setting a threshold on the $l2$ distance between the input image and its reconstruction from the winning capsule is very effective at detecting adversarial images for three different datasets. The same technique works quite well for CNNs that have been trained to reconstruct the image from all or part of the last hidden layer before the softmax. We then explore a stronger, white-box attack that takes the reconstruction error into account. This attack is able to fool our detection technique but in order to make the model change its prediction to another class, the attack must typically make the "adversarial" image resemble images of the other class.
Sequence Characterization of DSG3 Gene to Know Its Role in High-Altitude Hypoxia Adaptation in the Chinese Cashmere Goat

The Tibetan cashmere goat is one of the main goat breeds used by people living in the plateau. It exhibits the distinct phenotypic characteristics observed in lowland goats, allowing them to adapt to the challenging conditions at high altitudes. It provides an ideal model for understanding the genetic mechanisms underlying high-altitude adaptation and hypoxia-related diseases. Our previous exome sequencing of five Chinese cashmere breeds revealed a candidate gene, DSG3 (Desmoglein 3), responsible for the high-altitude adaptation of the Tibetan goat. However, the whole DSG3 gene (44 kbp) consisting of 16 exons in the goat genome was not entirely covered by the exome sequencing. In this study, we resequenced all the 16 exons of the DSG3 gene in ten Chinese native goat populations. Twenty-seven SNP variants were found between the lowland and highland goat populations. The genetic distance (FST) of significant SNPs between the lowland and highland populations ranged from 0.42 to 0.58. By using correlation coefficient analysis, linkage disequilibrium, and haplotype network construction, we found three non-synonymous SNPs (R597E, T595I, and G572S) in exon 5 and two synonymous SNPs in exons 8 and 16 in DSG3. These mutations significantly segregated high- and low-altitude goats in two clusters, indicating the contribution of DSG3 to the high-altitude hypoxia adaptation in the Tibetan goat.
Anomaly Detection Using Deep Learning Based Image Completion

Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region. The pixel-wise reconstruction error within the cut out region is an anomaly image which can be used for anomaly detection. Results on surface images of decorated plastic parts demonstrate that this approach is suitable for detection of visible anomalies and moreover surpasses all other tested methods.
Distribution of Transferable Antibiotic Resistance Genes in Laboratory-Reared Edible Mealworms (Tenebrio molitor L.)

In the present study, the distribution of antibiotic resistance genes in laboratory-reared fresh mealworm larvae (Tenebrio molitor L.), their feeding substrates (carrots and wheatmeal), and frass was assessed. Microbial counts on selective media added with antibiotics highlighted the presence of lactic acid bacteria resistant to ampicillin and vancomycin and, more specifically, enterococci resistant to the latter antibiotic. Moreover, staphylococci resistant to gentamicin, erythromycin, tetracycline, and vancomycin were detected. Enterobacteriaceae resistant to ampicillin and gentamicin were also found, together with Pseudomonadaceae resistant to gentamicin. Some of the genes coding for resistance to macrolide-lincosamide-streptogramin B (MLSB) [erm(A), erm(C)], vancomycin [vanA, vanB], tetracycline [tet(O)], and β-lactams [mecA and blaZ] were absent in all of the samples. For the feeding substrates, organic wheatmeal was positive for tet(S) and tet(K), whereas no AR genes were detected in organic carrots. The genes tet(M), tet(K), and tet(S) were detected in both mealworms and frass, whereas gene aac-aph, coding for resistance to amynoglicosides was exclusively detected in frass. No residues for any of the 64 antibiotics belonging to 10 different drug classes were found in either the organic wheatmeal or carrots. Based on the overall results, the contribution of feed to the occurrence of antibiotic resistance (AR) genes and/or antibiotic-resistant microorganisms in mealworm larvae was hypothesized together with vertical transmission via insect egg smearing.
Using Recurrences in Time and Frequency within U-net Architecture for Speech Enhancement

When designing fully-convolutional neural network, there is a trade-off between receptive field size, number of parameters and spatial resolution of features in deeper layers of the network. In this work we present a novel network design based on combination of many convolutional and recurrent layers that solves these dilemmas. We compare our solution with U-nets based models known from the literature and other baseline models on speech enhancement task. We test our solution on TIMIT speech utterances combined with noise segments extracted from NOISEX-92 database and show clear advantage of proposed solution in terms of SDR (signal-to-distortion ratio), SIR (signal-to-interference ratio) and STOI (spectro-temporal objective intelligibility) metrics compared to the current state-of-the-art.
Dimensional Complexity of the Resting Brain in Healthy Aging, Using a Normalized MPSE

Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.
Con: The Unclear Benefit of Albumin

Many studies have shown that 20% human albumin solution infusions improve circulatory function in patients with advanced liver disease, and this treatment is widely recommended and used by all hepatologists. However, it is more expensive than other crystalloids or colloids, and several countries suffer shortages of supply. This article examines whether other fluids might be considered for these patients.
Machine Decisions and Human Consequences

This chapter discusses how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or ‘classifiers’. It critically evaluates a real-world ‘classifier’, the Harm Assessment Risk Tool (HART)—an algorithmic decision-making tool employed by the Durham police force to inform custody decisions concerning individuals who have been arrested for suspected criminal offences. It evaluates the tool by reference to four normative benchmarks: prediction accuracy, fairness and equality before the law, transparency and accountability, and informational privacy and freedom of expression. It argues that systems which utilize decision-making (or decision-supporting) algorithms, and have the potential to detrimentally affect individual or collective human rights, deserve significantly greater regulatory scrutiny than those systems that use decision-making algorithms to process objects.
Increased Catalase Activity and Maintenance of Photosystem II Distinguishes High-Yield Mutants From Low-Yield Mutants of Rice var. Nagina22 Under Low-Phosphorus Stress

An upland rice variety, Nagina22 (N22) and its 137 ethyl methanesulfonate (EMS)-induced mutants, along with a sensitive variety, Jaya, was screened both in low phosphorus (P) field (Olsen P 1.8) and in normal field (Olsen P 24) during dry season. Based on the grain yield (YLD) of plants in normal field and plants in low P field, 27 gain of function (high-YLD represented as hy) and 9 loss of function (low-YLD represented as ly) mutants were selected and compared with N22 for physiological and genotyping studies. In low P field, hy mutants showed higher P concentration in roots, leaves, grains, and in the whole plant than in ly mutants at harvest. In low P conditions, Fv/Fm and qN were 24% higher in hy mutants than in ly mutants. In comparison with ly mutants, the superoxide dismutase (SOD) activity in the roots and leaves of hy mutants in low P fields was 9% and 41% higher at the vegetative stage, respectively, but 51% and 14% lower in the roots and leaves at the reproductive stage, respectively. However, in comparison with ly mutants, the catalase (CAT) activity in the roots and leaves of hy mutants in low P fields was 35% higher at the vegetative stage and 15% and 17% higher at the reproductive stage, respectively. Similarly, hy mutants in low P field showed 20% and 80% higher peroxidase (POD) activity in the roots and leaves at the vegetative stage, respectively, but showed 14% and 16% lower POD activity at the reproductive stage in the roots and leaves, respectively. Marker trait association analysis using 48 simple sequence repeat (SSR) markers and 10 Pup1 gene markers showed that RM3648 and RM451 in chromosome 4 were significantly associated with grain YLD, tiller number (TN), SOD, and POD activities in both the roots and leaves in low P conditions only. Similarly, RM3334 and RM6300 in chromosome 5 were associated with CAT activity in leaves in low P conditions. Notably, grain YLD was positively and significantly correlated with CAT activity in the roots and shoots, Fv/Fm and qN in low P conditions, and the shoots’ P concentration and qN in normal conditions. Furthermore, CAT activity in shoots was positively and significantly correlated with TN in both low P and normal conditions. Thus, chromosomal regions and physiological traits that have a role in imparting tolerance to low P in the field were identified.
Unconstrained submodular maximization with constant adaptive complexity

In this paper, we consider the unconstrained submodular maximization problem. We propose the first algorithm for this problem that achieves a tight (1/2−ε)-approximation guarantee using Õ(ε−1) adaptive rounds and a linear number of function evaluations. No previously known algorithm for this problem achieves an approximation ratio better than 1/3 using less than Ω(n) rounds of adaptivity, where n is the size of the ground set. Moreover, our algorithm easily extends to the maximization of a non-negative continuous DR-submodular function subject to a box constraint, and achieves a tight (1/2−ε)-approximation guarantee for this problem while keeping the same adaptive and query complexities.
AclNet: efficient end-to-end audio classification CNN

We propose an efficient end-to-end convolutional neural network architecture, AclNet, for audio classification. When trained with our data augmentation and regularization, we achieved state-of-the-art performance on the ESC-50 corpus with 85:65% accuracy. Our network allows configurations such that memory and compute requirements are drastically reduced, and a tradeoff analysis of accuracy and complexity is presented. The analysis shows high accuracy at significantly reduced computational complexity compared to existing solutions. For example, a configuration with only 155k parameters and 49:3 million multiply-adds per second is 81:75%, exceeding human accuracy of 81:3%. This improved efficiency can enable always-on inference in energy-efficient platforms.