Miracle-dz/UniFAR-FTUs huggingface dataset:
A comprehensive protocol for manual segmentation of the human claustrum and its sub-regions using high-resolution MRI

The claustrum (Cl) is a thin grey matter structure located in the center of each brain hemisphere. Cl has been hypothesized as a central hub of the brain for multisensory/sensorimotor integration, consciousness, and attention. Accumulating evidence has suggested that Cl might be important in the development of severe neurological and psychiatric symptoms including epileptic seizures and psychosis. However, the specifics of the roles of Cl in human epilepsy and psychosis are largely unknown, primarily due to methodological limitations related to the thin morphology of Cl that is challenging to delineate accurately using conventional methods. The goal of this work is to develop noninvasive multimodal neuroimaging methods to delineate Cl anatomy by utilizing a large healthy adult high resolution (0.7mm3) T1-weighted MRI collected as part of the Washington University-Minnesota Consortium Human Connectome Project (WU-Minn HCP). We developed a comprehensive manual segmentation protocol to delineate Cl based on a cellular level brain atlas. The protocol involves detailed guidelines to delineate the three subregions of Cl, including the dorsal, ventral, and temporal Cl that can be parcellated based on a geometric method. As demonstrated in a representative result, Cl is large in its anterior-posterior, and the dorsal-ventral extent. Also, the volume is comparable to that of the amygdala. It is required to assess the reliability of the protocol so that it can be used for future anatomical studies of neuropsychiatric disorders, including epilepsy and schizophrenia.
Revisiting BFloat16 Training

State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision, creating the folklore that 16-bit precision alone is not enough to maximize model accuracy. As a result, deep learning accelerators are forced to support both 16-bit and 32-bit compute units which is more costly than only using 16-bit units for hardware design. We ask can we do pure 16-bit training which requires only 16-bit compute units, while still matching the model accuracy attained by 32-bit training. Towards this end, we study pure 16-bit training algorithms on the widely adopted BFloat16 compute unit. While these units conventionally use nearest rounding to cast output to 16-bit precision, we show that nearest rounding for model weight updates can often cancel small updates, which degrades the convergence and model accuracy. Motivated by this, we identify two simple existing techniques, stochastic rounding and Kahan summation, to remedy the model accuracy degradation in pure 16-bit training. We empirically show that these two techniques can enable up to 7% absolute validation accuracy gain in pure 16-bit training. This leads to 0.1% lower to 0.2% higher matching validation accuracy compared to 32-bit precision training across seven deep learning applications.
Hindsight Experience Replay with Kronecker Product Approximate Curvature

Hindsight Experience Replay (HER) is one of the efficient algorithm to solve Reinforcement Learning tasks related to sparse rewarded environments.But due to its reduced sample efficiency and slower convergence HER fails to perform effectively. Natural gradients solves these challenges by converging the model parameters better. It avoids taking bad actions that collapse the training performance. However updating parameters in neural networks requires expensive computation and thus increase in training time. Our proposed method solves the above mentioned challenges with better sample efficiency and faster convergence with increased success rate. A common failure mode for DDPG is that the learned Q-function begins to dramatically overestimate Q-values, which then leads to the policy breaking, because it exploits the errors in the Q-function. We solve this issue by including Twin Delayed Deep Deterministic Policy Gradients(TD3) in HER. TD3 learns two Q-functions instead of one and it adds noise tothe target action, to make it harder for the policy to exploit Q-function errors. The experiments are done with the help of OpenAis Mujoco environments. Results on these environments show that our algorithm (TDHER+KFAC) performs better inmost of the scenarios
A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics such as Balanced Accuracy are commonly used to evaluate a classifier's prediction performance under such scenarios. However, these metrics fall short when classes vary in importance. In this paper, we propose a simple and general-purpose evaluation framework for imbalanced data classification that is sensitive to arbitrary skews in class cardinalities and importances. Experiments with several state-of-the-art classifiers tested on real-world datasets from three different domains show the effectiveness of our framework - not only in evaluating and ranking classifiers, but also training them.
Aspect-based Document Similarity for Research Papers

Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity approach for research papers. Paper citations indicate the aspect-based similarity, i.e., the title of a section in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. According to our results, SciBERT is the best performing system with F1-scores of up to 0.83. A qualitative analysis validates our quantitative results and indicates that aspect-based document similarity indeed leads to more fine-grained recommendations.
Differentially expressed proteins in the primary focus of nasopharyngeal carcinoma in different clinical types

Objective To explore the differentially expressed protiens of the tumor tissues of nasopharyngeal carcinoma(NPC) primary focus in different clinical types including upward progressing type,downward progressing type and mixed progressing type.Methods The total proteins of tissues from normal nasopharyngeal mucosa and NPC in different clinical types were separated by two-dimensional electrophoresis(2-DE),then proteins spots were analyzed and the differentially expressed proteins were identified by matrix assisted laser desorption/ionization time-offlight mass spectrometry(MALDI-TOF-MS) combined with bioinformatics analysis.Results Thirty-one significant differentially expressed proteins between mixed progressing type of NPC and normal tissues were found.Eighteen proteins were up-regulated and 13 proteins were down-regulated in mixed type of NPC.Five proteins of ten differential expressed proteins selected from mixed progressing type of NPC were identified by MALDI-TOF-MS.Heat shock cognate 71 kDa protein,alpha-1-antitrypsin and macrophage-capping protein were up-regulated and Protein S100A9 and Protein 4.1 were down-regulated in mixed progressing type of NPC.Thirty-one significant differentially expressed proteins between upward and downward progressing types of NPC were also found.And among of them,15 proteins were up-regulated and 16 proteins were down-regulated in upward progressing type of NPC as compared with those in downward progressing type of NPC.Six proteins of ten differential expressed proteins selected from upward progressing types of NPC were identified by MALDI-TOF-MS.Mitochondrial aconitate hydratase,alcohol dehydrogenase and Rab GDP dissociation inhibitor beta were up-regulated and NM23-H1,nuclear chloride channel-27 and aldehyde dehydrogenase X were down-regulated in upward progressing type of NPC as compared with those in downward progressing type of NPC.Conclusions There were significant differences at proteins related to tumor metastasis,energy metabolism,retoxity and anti-metastasis between different clinical type of NPC.These proteins may function as markers of clinical types of NPC.
Machine learning for the diagnosis of Parkinson's disease: A systematic review

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a systematic literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this systematic review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
Constraining the Chemical Signatures and the Outburst Mechanism of the Class 0 Protostar HOPS 383

We present observations toward HOPS 383, the first known outbursting Class 0 protostar located within the Orion molecular cloud using the Atacama Large Millimeter/submillimeter Array (ALMA), Very Large Array (VLA), and Submillimeter Array (SMA). The SMA observations reveal envelope scale continuum and molecular line emission surrounding HOPS 383 at 0.85, 1.1, and 1.3 mm. The images show that HCO+ and H13CO+ peaks on or near the continuum, while N2H+ is reduced at the same position. This reflects the underlying chemistry where CO evaporating close to the protostar destroys N2H+ while forming HCO+. We also observe the molecular outflow traced by 12CO ( ) and ( ). A disk is resolved in the ALMA 0.87 mm dust continuum, orthogonal to the outflow direction, with an apparent radius of ∼62 au. Radiative transfer modeling of the continuum gives disk masses of 0.02 M⊙ when fit to the ALMA visibilities. The models including VLA 8 mm data indicate that the disk mass could be up to a factor of 10 larger due to lower dust opacity at longer wavelengths. The disk temperature and surface density profiles from the modeling, and an assumed protostar mass of 0.5 M⊙ suggest that the Toomre Q parameter < 1 before the outburst, making gravitational instability a viable mechanism to explain outbursts at an early age if the disk is sufficiently massive.
A matching procedure for sequential experiments that iteratively learns which covariates improve power

We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “SeqExpMatch” for use by practitioners is available on CRAN.
Multipole decomposition of the general luminosity distance Hubble law — a new framework for observational cosmology

We present the luminosity distance series expansion to third order in redshift for a general space-time with no assumption on the metric tensor or the field equations prescribing it. It turns out that the coefficients of this general Hubble law can be expressed in terms of a finite number of physically interpretable multipole coefficients. The multipole terms can be combined into effective direction dependent parameters replacing the Hubble constant, deceleration parameter, curvature parameter, and jerk parameter of the Friedmann-Lemaître-Robertson-Walker (FLRW) class of metrics. Due to the finite number of multipole coefficients, the exact anisotropic Hubble law is given by 9, 25, 61 degrees of freedom in the 𝒪(z), 𝒪(z2), 𝒪(z3) vicinity of the observer respectively, where z:=redshift. This makes possible model independent determination of dynamical degrees of freedom of the cosmic neighbourhood of the observer and direct testing of the FLRW ansatz. We argue that the derived multipole representation of the general Hubble law provides a new framework with broad applications in observational cosmology.
EmoG- Towards Emojifying Gmail Conversations

Emails are one of the most frequently used medium of communication in the present day across multiple domains including industry and educational institutions. Understanding sentiments being expressed in an email could have a considerable impact on the recipients' action or response to the email. However, it is difficult to interpret emotions of the sender from pure text in which emotions are not explicitly present. Researchers have tried to predict customer attrition by integrating emails in client-company environment with emotions. However, most of the existing works deal with static assessment of email emotions. Presenting sentiments of emails dynamically to the reader could help in understanding senders' emotion and as well have an impact on readers' action. Hence, in this paper, we present EmoG as a Google Chrome Extension which is intended to support university students. It augments emails with emojis based on the sentiment being conveyed in the email, which might also offer faster overview of email sentiments and act as tags that could help in automatic sorting and processing of emails. Currently, EmoG has been developed to support Gmail inbox on a Google Chrome browser, and could be extended to other inboxes and browsers with ease. We have conducted a user survey with 15 university students to understand the usefulness of EmoG and received positive feedback.
BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity

This paper presents the team BRUMS submission to SemEval-2020 Task 3: Graded Word Similarity in Context. The system utilises state-of-the-art contextualised word embeddings, which have some task-specific adaptations, including stacked embeddings and average embeddings. Overall, the approach achieves good evaluation scores across all the languages, while maintaining simplicity. Following the final rankings, our approach is ranked within the top 5 solutions of each language while preserving the 1st position of Finnish subtask 2.
Factors affecting daily milk yield, fat and protein percentage, and somatic cell count in primiparous Holstein cows

The objective of this study was to determine effective factors on the variation of daily milk yield (DMY), fat (F%) and protein percentage (P%), and somatic cell count (SCC) in Holstein cows. A total of 278 primiparous cows were examined by four parameters in four calving seasons (CS), three years and six test days (TD) post-calving. While SCC and fat values were affected by CS (P<0.05), no significant difference was found among DMY and P% by CS. Both year and TD were not effective on SCC, but significant differences (P<0.05) were determined among DMY, F% and P% by two factors. Correlation of P% with SCC and F% was estimated to be positive (P<0.01; r=0.168 and r=0.457, respectively), and correlation of DMY with fat and protein was determined as negative (P<0.01; r= -0.363 and r= -0.335, respectively). The study confirmed that multiple milk parameter factors are highly important to obtain more quality and quantity milk from primiparous dairy cows.
Study on the Anti-aging Action of the Extracts of Cuscuta chinensis in Natural Aging Mice

OBJECTIVE:To observe the Anti-aging activity of the extracts of Cuscuta chinensis in natural aging mice.METHODS:Natural aging mice were given the extracts of C.chinensis via i.g. gtt.Y-type electric maze was used to determine their abilities of learning and memory,then biochemical method was adopted to detect the activity of superoxide dismutase(SOD) and glutathione peroxidase(GSH-PX),and the content of malondialdehyde(MDA) in cerebral tissues of aging mice.RESULTS:The test behavior of mice expressed that C.chinensis could significantly lower the correct percentage in Y-type electric maze test,but had little effect on the memory retention.The antioxidase activity experiment of cerebral tissues of mice expressed that C.chinensis could improve the activity of SOD and GSH-PX in mice cerebral tissue,and lower the contents of MDA.CONCLUSION:The anti-aging activity of the extracts of C.chinensis is achieved by improving the activity of SOD and GSH-PX,and lowering the contents of MDA.
Extending Implicit Discourse Relation Recognition to the PDTB-3

The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses. We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future state-of-the-art recognizers for implicit discourse relations.
Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. Although the most common formulation of text ranking is search, instances of the task can also be found in many text processing applications. This tutorial provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. These models produce high quality results across many domains, tasks, and settings. This tutorial, which is based on the preprint of a forthcoming book to be published by Morgan and & Claypool under the Synthesis Lectures on Human Language Technologies series, provides an overview of existing work as a single point of entry for practitioners who wish to deploy transformers for text ranking in real-world applications and researchers who wish to pursue work in this area. We cover a wide range of techniques, grouped into two categories: transformer models that perform reranking in multi-stage ranking architectures and learned dense representations that perform ranking directly.
On invariant rank two vector bundles on $\mathbb{P}^2$

In this paper we characterize the rank two vector bundles on P 2 which are invariant under the action of Gp := Stabp(PGL(3)), that fixes a point in the projective plane, GL := StabL(PGL(3)), that fixes a line, and T = Gp ∩ GL. Moreover, we prove that the geometrical configuration of the jumping locus induced by the invariance does not, on the other hand, characterize the invariance itself. Indeed, we find infinity families that are almost uniform but not almost homogeneous.
MYCOTROPHY: ITS OCCURRENCE AND POSSIBLE SIGNIFICANCE AMONG EPIPHYTIC ORCHIDACEAE

Orchidaceae and mycotrophy are practically synonymous to many botanists. This impression could be illusory, however, its basis being little more than the long-known symbiotic germination of certain orchid seeds which is thought to be representative of all members of the family (Arditti, 1967, 1979; Warcup, 1975), and the persistence of extensive fungal infections in the root systems of at least a few terrestrial adults. Species native to soil do pass through heterotrophic phases assisted by mycobionts, some lasting years (e.g., Cypripedium) or, in exceptional cases where the phytobiont is an achlorophyllous saprophyte or epiparasite (e.g., Corallorhiza), a lifetime (Burgeff, 1936, 1959; Harley, 1969).
Homogeneous Turbulent Field within a Rotating Frame

With a view to explaining the behavior of turbulent flows in turbomachine rotors, a turbulent shear flow is examined in a rotating coordinate system. When the turbulent field is homogeneous, equations of second order correlations are written at two points. In order to carry out spectral computations, triple correlations are neglected. Coriolis forces are found to have an effect on the stability of turbulence. Consequences concerning turbulence modeling and applications to turbomachinery are discussed.
Automated scoring of aortic calcification in vertebral fracture assessment images

The severity of abdominal aortic calcification (AAC) is a strong, independent predictor of cardiovascular disease (CVD). Vertebral fracture assessment (VFA) is a low radiation screening tool which can be used to incidentally measure AAC. This work compares the performance of Haar feature random forest classification with a Unet based convolutional neural network (CNN) segmentation, to automatically quantify AAC. Clinical semiquantitative scores were also generated using U-net. Scores were calculated using the relative length of labelled calcification and compared to manual scoring. The U-net outperformed the random forest, showed sensible segmentations and AAC scores, though it could not match human annotation accuracy.
Cutaneous Metastasis in the Setting of Both Colon and Breast Primary Malignancies

Colorectal cancer (CRC) is the third most diagnosed cancer in the United States, and many patients unfortunately have metastases at the time of their diagnosis. Cutaneous metastases of CRC have been reported in few journals and primarily as case reports due to their rarity. Here, we present the case of an 83-year-old woman with recently resected colon cancer, T4aN1bMx stage IIIB. She presented to our clinic for evaluation of a right midback mass, and a punch biopsy revealed dermal involvement by invasive, poorly differentiated carcinoma with epidermoid features. The mass was excised, and we ordered a PET scan in search of the primary tumor, which at that time was suspected to be of skin cancer origin. Surprisingly, this revealed a second malignancy triple-negative invasive ductal carcinoma of the left breast. The back mass stained positive for CK20, which was compatible with a metastasis from a colonic primary. After initially declining adjuvant therapy, the patient completed one cycle of capecitabine and oxaliplatin, which she tolerated poorly. She continued to further decline, developed widespread cutaneous metastases, and went home on hospice. Cutaneous lesions are an exceedingly rare site of metastasis for colon adenocarcinoma, and their clinical presentation can vary widely. It is important for providers to investigate any new skin lesion in a patient with a recent or remote history of malignancy, even if there were no sites of distant metastasis at initial diagnosis.