Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyrics Segmentation: Textual Macrostructure Detection using Convolutions

Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation – a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.
Fine-Grained Face Annotation Using Deep Multi-Task CNN

We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN’s parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations’ sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach.
Impact of hypertension on retinal capillary microvasculature using optical coherence tomographic angiography

Objective: Reduction in capillary density or rarefaction is a hallmark of essential hypertension. We measured the retinal capillary density using noninvasive optical coherence tomographic angiography (OCT-A) in adults with treated systemic hypertension and determined possible correlations with ambulatory blood pressure (BP) and renal parameters. Methods: This observational cross-sectional study consisted of 153 normal eyes from 77 nondiabetic hypertensive adults [mean (SD) age, 58 (9) years; 49% women; 23% poorly controlled BP]. Data on 24-h ambulatory BP monitoring, serum creatinine, and urine microalbumin/creatinine ratio (MCR) were collected. Estimated glomerular filtration rate (eGFR) was calculated based on CKD-EPI Creatinine Equation. Retinal capillary density measured with the OCT-A (AngioVue) at superficial (SVP) and deep vascular plexuses (DVP). Linear regression was used to investigate the association of risk factors with capillary density. Results: Retinal capillary density (percentage) at DVP was reduced in patients with poorly controlled BP (SBP = 148 ± 8 mmHg; 27.2 ± 13.0) compared with those with well controlled BP (SBP = 125 ± 9 mmHg; 34.7 ± 11.3). In the multivariable analysis, poorly controlled BP [&bgr; = −6.49, 95% confidence interval (CI), −12.39 to −0.59], higher SBP (&bgr; = −0.23, 95% CI −0.44 to −0.02) and lower eGFR (&bgr; = 6.42, 95% CI 1.25–11.60) were associated with sparser retinal capillary density. Systemic factors were not associated with capillary density at SVP (all P > 0.05). Conclusion: In adults with treated systemic hypertension, retinal capillary density reduced with higher BP and poorer eGFR. These findings highlight the potential role of OCT-A to study early microvascular changes because of systemic hypertension.
Multi-Locus Genome-Wide Association Studies of Fiber-Quality Related Traits in Chinese Early-Maturity Upland Cotton

Early-maturity varieties of upland cotton are becoming increasingly important for farmers to improve their economic benefits through double cropping practices and mechanical harvesting production in China. However, fiber qualities of early-maturing varieties are relatively poor compared with those of middle- and late- maturing ones. Therefore, it is crucial for researchers to elucidate the genetic bases controlling fiber-quality related traits in early-maturity cultivars, and to improve synergistically cotton earliness and fiber quality. Here, multi-locus genome-wide association studies (ML-GWAS) were conducted in a panel consisting of 160 early-maturing cotton accessions. Each accession was genotyped by 72,792 high-quality single nucleotide polymorphisms (SNPs) using specific-locus amplified fragment sequencing (SLAF-seq) approach, and fiber quality-related traits under four environmental conditions were measured. Applying at least three ML-GWAS methods, a total of 70 significant quantitative trait nucleotides (QTNs) were identified to be associated with five objective traits, including fiber length (FL), fiber strength (FS), fiber micronaire (FM), fiber uniformity (FU) and fiber elongation (FE). Among these QTNs, D11_21619830, A05_28352019 and D03_34920546 were found to be significantly associated with FL, FS, and FM, respectively, across at least two environments. Among 96 genes located in the three target genomic regions (A05: 27.95 28.75, D03: 34.52 35.32, and D11: 21.22 22.02 Mbp), six genes (Gh_A05G2325, Gh_A05G2329, Gh_A05G2334, Gh_D11G1853, Gh_D11G1876, and Gh_D11G1879) were detected to be highly expressed in fibers relative to other eight tissues by transcriptome sequencing method in 12 cotton tissues. Together, multiple favorable QTN alleles and six candidate key genes were characterized to regulate fiber development in early-maturity cotton. This will lay a solid foundation for breeding novel cotton varieties with earliness and excellent fiber-quality in the future.
In Silico Analysis of the Minor Histocompatibility Antigen Landscape Based on the 1000 Genomes Project

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is routinely used to treat hematopoietic malignancies. The eradication of residual tumor cells during engraftment is mediated by donor cytotoxic T lymphocytes reactive to alloantigens. In a HLA-matched transplantation context, alloantigens are encoded by various polymorphic genes situated outside the HLA locus, also called minor histocompatibility antigens (MiHAs). Recently, MiHAs have been recognized as promising targets for post-transplantation T-cell immunotherapy as they have several appealing advantages over tumor-associated antigens (TAAs) and neoantigens, i.e., they are more abundant than TAAs, which potentially facilitates multiple targeting; and unlike neoantigens, they are encoded by germline polymorphisms, some of which are common and thus, suitable for off-the-shelf therapy. The genetic sources of MiHAs are nonsynonymous polymorphisms that cause differences between the recipient and donor proteomes and subsequently, the immunopeptidomes. Systematic description of the alloantigen landscape in HLA-matched transplantation is still lacking as previous studies focused only on a few immunogenic and common MiHAs. Here, we perform a thorough in silico analysis of the public genomic data to classify genetic polymorphisms that lead to MiHA formation and estimate the number of potentially available MiHA mismatches. Our findings suggest that a donor/recipient pair is expected to have at least several dozen mismatched strong MHC-binding SNP-associated peptides per HLA allele (116 ± 26 and 65 ± 15 for non-related pairs and siblings respectively in European populations as predicted by two independent algorithms). Over 70% of them are encoded by relatively frequent polymorphisms (minor allele frequency > 0.1) and thus, may be targetable by off-the-shelf therapeutics. We showed that the most appealing targets (probability of mismatch over 20%) reside in the asymmetric allele frequency region, which spans from 0.15 to 0.47 and corresponds to an order of several hundred (213 ± 47) possible targets per HLA allele that can be considered for immunogenicity validation. Overall, these findings demonstrate the significant potential of MiHAs as targets for T-cell immunotherapy and emphasize the need for the systematic discovery of novel MiHAs.
The Effect of Educational Status on the Perception of Social and Spontaneous Smiles.

Objective The aim of the present study was to evaluate the effect of educational status on the perception of smile esthetics considering social and spontaneous smile patterns. Methods The present study was conducted on 110 subjects with differing educational status. Of 110 subjects with a mean age of 39.4 years, 30 were primary school graduates, 30 were high school graduates, and 50 had a university or higher educational level. Four different black and white natural social and spontaneous smiling images, captured from a video recording of a 25-year-old female, were prepared displaying the full-face and oral area. A 10-cm visual analog scale (VAS) was used to evaluate the smile esthetics. Results There were significant differences in the social and spontaneous smiles in a subjective assessment of the smile esthetics in full-face and oral views between participants having a different educational status (p>0.05). Compared with the other groups, the participants in the primary school graduate group gave higher esthetic scores in all the image groups. In terms of esthetic perception, this group was followed by high school graduates and participants with a university or higher level of education. In the four image groups, there was a significant difference between the primary school graduates and participants with a university or higher level of education (p<0.05). Conclusion Educational status is a sociodemographic factor that can affect the perception of smile esthetics. The esthetic scores both in social and spontaneous smile decrease with increasing education level, and the most remarkable difference exists between primary school graduates and those with university or higher level of education.
Detection of Treg/Th17 cells and related cytokines in peripheral blood of chronic hepatitis B patients combined with thrombocytopenia and the clinical significance

Changes of regulatory T (Treg) cells and Th17 cells, and related cytokines in peripheral blood of patients with chronic hepatitis B combined with thrombocytopenia were investigated to explore the relationship with treatment outcomes. A total of 45 chronic hepatitis B patients combined with thrombocytopenia were selected in Heilongjiang Provincial Hospital from June 2015 to December 2016. All patients were treated with prednisolone acetate + γ globulins for 60 days. Treg cells and Th17 cells in peripheral blood were detected by flow cytometry, and IL-10, TGF-β, IL-17, IL-21 and IL-22 in peripheral blood were detected by ELISA before and after treatment. No significant differences in the percentages of Treg and Th17, and levels of IL-10, TGF-β, IL-17, IL-21 and IL-22 were found in non-responders (n=17, platelets <100×109/l) before and after treatment (P>0.05). After treatment, percentage of Treg was significantly increased (higher than that of non-responders) and percentage of Th17 was significantly decreased (lower than that of non-responders) in responders (P<0.05). In addition, serum levels of IL-10 and TGF-β were significantly increased (higher than that of non-responders) and serum levels of IL-17, IL-21 and IL-22 were significantly decreased (lower than that of non-responders) in responders (P<0.05). The results showed that after treatment, the number of Treg cells was increased, the number of Th17 cells was decreased, the levels of anti-inflammatory factors IL-10 and TGF-β were increased, and levels of pro-inflammatory factors IL-17, IL-21 and IL-22 were decreased in chronic hepatitis B patients combined with thrombocytopenia, indicating the decreased autoimmune response and improved thrombocytopenia. The changes were closely related to the complete response.
Muscle Fatty Acid Content in Selected Freshwater Fish from Bukit Merah Reservoir, Perak, Malaysia

One of the oldest reservoirs in Peninsular Malaysia, Bukit Merah Reservoir, is a place in which locals participate in fishing activities. Inland fisheries are important to individuals, society and the environment; whereby they generate a source of income and food security. It is essential to gauge the nutrition value of fish caught in this location as food source, especially in terms of fatty acid composition, to better demonstrate its potential towards the betterment of human health and general well-being. From an initial list of 47 fish species available in Bukit Merah Reservoir, a total of seven edible freshwater fish species were identified, namely tinfoil barb (Barbonymus schwanenfeldii), Javanese barb (Barbonymus gonionotus), hampala barb (Hampala macrolepidota), beardless barb (Cyclocheilichthys apogon), glassfish (Oxygaster anomalura), striped snakehead (Channa striata) and horseface loach (Acantopsis dialuzona), and muscle fatty acid content was analysed to determine their nutritional value. Muscle of cyprinid fish contained substantial amount of omega-3 long-chain polyunsaturated fatty acids (LC-PUFA) compared to fish from Channidae and Cobitidae families. Javanese and tinfoil barbs muscle recorded the highest levels of combined eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) indicating the highest nutritional value comparatively. Unexpectedly, the striped snakehead, a predatory carnivore, contained lower levels of LC-PUFA compared to detrivorous/herbivorous fishes. This further justifies that the omega-3 polyunsaturated fatty acids (PUFA) content in fish muscle varies according to their feeding habits. Even though it has been recommended that marine fish be consumed to improve health to a certain extent, there still are benefits of consuming freshwater fish, as there are several species which contain considerable amounts of beneficial omega-3 PUFA.
Delay discounting of monetary gains and losses in adolescents with ADHD: Contribution of delay aversion to choice

ABSTRACT Adolescents with attention-deficit/hyperactivity disorder (ADHD) are known to have stronger preferences for smaller immediate rewards over larger delayed rewards in delay discounting tasks than their peers, which has been argued to reflect delay aversion. Here, participants performed a delay discounting task with gains and losses. In this latter condition, participants were asked whether they were willing to wait in order to lose less money. Following the core assumption of the delay aversion model that individuals with ADHD have a general aversion to delay, one would predict adolescents with ADHD to avoid waiting in both conditions. Adolescents (12–17 years) with ADHD (n = 29) and controls (n = 28) made choices between smaller immediate and larger delayed gains, and between larger immediate and smaller delayed losses. All delays (5–25 s) and gains/losses (2–10 cents) were experienced. In addition to an area under the curve approach, a mixed-model analysis was conducted to disentangle the contributions of delay duration and immediate gain/delayed loss amount to choice. The ADHD group chose the immediate option more often than controls in the gain condition, but not in the loss condition. The contribution of delay duration to immediate choices was stronger for the ADHD group than the control group in the gain condition only. In addition, the ADHD group scored higher on self-reported delay aversion, and delay aversion was associated with delay sensitivity in the gain condition, but not in the loss condition. In sum, we found no clear evidence for a general aversion to delay in adolescents with ADHD.
Generic refinement of expressive grammar formalisms with an application to discontinuous constituent parsing

We formulate a generalization of Petrov et al. (2006)’s split/merge algorithm for interpreted regular tree grammars (Koller and Kuhlmann, 2011), which capture a large class of grammar formalisms. We evaluate its effectiveness empirically on the task of discontinuous constituent parsing with two mildly context-sensitive grammar formalisms: linear context-free rewriting systems (Vijay-Shanker et al., 1987) as well as hybrid grammars (Nederhof and Vogler, 2014).
Modulating reconsolidation and extinction to regulate drug reward memory

Drug addiction is an aberrant memory that shares the same memory processes as other memories. Brief exposure to drug‐associated cues could result in reconsolidation, a hypothetical process during which original memory could be updated. In contrast, longer exposure times to drug‐associated cues could trigger extinction, a process that decreases the conditioned responding. In this review, we discuss the pharmacological and non‐pharmacological manipulations on the reconsolidation and extinction that could be used to interfere with drug reward memories. Pharmacological agents such as β‐adrenergic receptor antagonist propranolol can interfere with reconsolidation to disrupt drug reward memory. Pharmacological agents such as the NMDA receptor glycine site agonists d‐cycloserine and d‐serine can facilitate extinction and then attenuate the expression of drug reward memory. Besides pharmacological interventions, drug‐free behavioral approaches by utilizing the reconsolidation and extinction, such as ‘post‐retrieval extinction’ and ‘UCS‐retrieval extinction’, are also effective to erase or inhibit the recall of drug reward memory. Taken together, pharmacological modulation and non‐pharmacological modulation of reconsolidation and extinction are promising approaches to regulate drug reward memory and prevent relapse.
Cycles in the burnt pancake graphs

The pancake graph $P_n$ is the Cayley graph of the symmetric group $S_n$ on $n$ elements generated by prefix reversals. $P_n$ has been shown to have properties that makes it a useful network scheme for parallel processors. For example, it is $(n-1)$-regular, vertex-transitive, and one can embed cycles in it of length $\ell$ with $6\leq\ell\leq n! $. The burnt pancake graph $BP_n$, which is the Cayley graph of the group of signed permutations $B_n$ using prefix reversals as generators, has similar properties. Indeed, $BP_n$ is $n$-regular and vertex-transitive. In this paper, we show that $BP_n$ has every cycle of length $\ell$ with $8\leq\ell\leq 2^n n! $. The proof given is a constructive one that utilizes the recursive structure of $BP_n$. We also present a complete characterization of all the $8$-cycles in $BP_n$ for $n \geq 2$, which are the smallest cycles embeddable in $BP_n$, by presenting their canonical forms as products of the prefix reversal generators.
Argumentation Synthesis following Rhetorical Strategies

Persuasion is rarely achieved through a loose set of arguments alone. Rather, an effective delivery of arguments follows a rhetorical strategy, combining logical reasoning with appeals to ethics and emotion. We argue that such a strategy means to select, arrange, and phrase a set of argumentative discourse units. In this paper, we model rhetorical strategies for the computational synthesis of effective argumentation. In a study, we let 26 experts synthesize argumentative texts with different strategies for 10 topics. We find that the experts agree in the selection significantly more when following the same strategy. While the texts notably vary for different strategies, especially their arrangement remains stable. The results suggest that our model enables a strategical synthesis.
Neural Math Word Problem Solver with Reinforcement Learning

Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the “train-test discrepancy” issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection

Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework. In this paper, we propose a novel deep learning architecture for incorporating entities and their inherent attributes from various domains into a unified framework. Specifically, our model not only encodes the entities of reviewer, item, and review, but also their attributes such as location, date, price ranges. Furthermore, we present a domain classifier to adapt the knowledge from one domain to the other. With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings. Experimental results on two Yelp datasets prove that our proposed framework significantly outperforms the state-of-the-art methods.