Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot molecular core candidates in the Galactic center 50 km s−1 molecular cloud

We present results based on ${2{^{\prime \prime}_{. }}5}$-resolution observations using the Atacama Large Millimeter/Submillmeter Array (ALMA) of the Galactic center molecular cloud G−0.02−0.07, or the 50 km s−1 molecular cloud (50 MC), in the SO (NJ = 22–11) line and 86 GHz continuum emission, the combination of which is considered to trace “hot molecular core candidates” (HMCCs) appearing in the early stage of massive-star formation. In the 86 GHz continuum image, we identified nine dust cores in the central part of the 50 MC, in which four famous compact H ii regions are located. No new ultra-compact H ii regions were found. We also identified 28 HMCCs in the 50 MC with the SO line. The overall SO distribution had no clear positional correlation with the identified H ii regions. The HMCCs in the 50 MC showed a variety of association and non-association with dust and Class I CH3OH maser emissions. The variety suggests that they are not in a single evolutionary stage or environment. Nevertheless, the masses of the identified HMCCs were found to be well approximated by a single power law of their radii, MLTE/(M⊙) = 5.44 × 105 [r/(pc)]2.17 at Tex = 50–100 K. The derived HMCC masses were larger than those of the molecular cores with the same radii in the 50 MC, and also than those of the molecular clumps in the Galactic disk. Additional observations are needed to confirm the nature of these HMCCs in the 50 MC.
From Physiology to Pathology of Cortico-Thalamo-Cortical Oscillations: Astroglia as a Target for Further Research

The electrographic hallmark of childhood absence epilepsy (CAE) and other idiopathic forms of epilepsy are 2.5–4 Hz spike and wave discharges (SWDs) originating from abnormal electrical oscillations of the cortico-thalamo-cortical network. SWDs are generally associated with sudden and brief non-convulsive epileptic events mostly generating impairment of consciousness and correlating with attention and learning as well as cognitive deficits. To date, SWDs are known to arise from locally restricted imbalances of excitation and inhibition in the deep layers of the primary somatosensory cortex. SWDs propagate to the mostly GABAergic nucleus reticularis thalami (NRT) and the somatosensory thalamic nuclei that project back to the cortex, leading to the typical generalized spike and wave oscillations. Given their shared anatomical basis, SWDs have been originally considered the pathological transition of 11–16 Hz bursts of neural oscillatory activity (the so-called sleep spindles) occurring during Non-Rapid Eye Movement (NREM) sleep, but more recent research revealed fundamental functional differences between sleep spindles and SWDs, suggesting the latter could be more closely related to the slow (<1 Hz) oscillations alternating active (Up) and silent (Down) cortical activity and concomitantly occurring during NREM. Indeed, several lines of evidence support the fact that SWDs impair sleep architecture as well as sleep/wake cycles and sleep pressure, which, in turn, affect seizure circadian frequency and distribution. Given the accumulating evidence on the role of astroglia in the field of epilepsy in the modulation of excitation and inhibition in the brain as well as on the development of aberrant synchronous network activity, we aim at pointing at putative contributions of astrocytes to the physiology of slow-wave sleep and to the pathology of SWDs. Particularly, we will address the astroglial functions known to be involved in the control of network excitability and synchronicity and so far mainly addressed in the context of convulsive seizures, namely (i) interstitial fluid homeostasis, (ii) K+ clearance and neurotransmitter uptake from the extracellular space and the synaptic cleft, (iii) gap junction mechanical and functional coupling as well as hemichannel function, (iv) gliotransmission, (v) astroglial Ca2+ signaling and downstream effectors, (vi) reactive astrogliosis and cytokine release.
Epidemiology of Melanoma

Melanoma accounts for 1.7% of global cancer diagnoses and is the fifth most common cancer in the US. Melanoma incidence is rising in developed, predominantly fair-skinned countries, growing over 320% in the US since 1975. However, US mortality has fallen almost 30% over the past decade with the approval of 10 new targeted or immunotherapy agents since 2011. Mutations in the signaling-protein BRAF, present in half of cases, are targeted with oral BRAF/MEK inhibitor combinations, while checkpoint inhibitors are used to restore immunosurveillance likely inactivated by UV radiation. Although the overall 5-year survival has risen to 93.3% in the US, survival for stage IV disease remains only 29.8%. Melanoma is most common in white, older men, with an average age of diagnosis of 65. Outdoor UV exposure without protection is the main risk factor, although indoor tanning beds, immunosuppression, family history and rare congenital diseases, moles, and obesity contribute to the disease. Primary prevention initiatives in Australia implemented since 1988, such as education on sun-protection, have increased sun-screen usage and curbed melanoma incidence, which peaked in Australia in 2005. In the US, melanoma incidence is not projected to peak until 2022–2026. Fewer than 40% of Americans report practicing adequate protection (sun avoidance from 10 a.m.–4 p.m. and regular application of broad-spectrum sunscreen with an SPF > 30). A 2-4-fold return on investment is predicted for a US sun-protection education initiative. Lesion-directed skin screening programs, especially for those at risk, have also cost-efficiently reduced melanoma mortality.
Mining Cross Features for Financial Credit Risk Assessment

For reliability, machine learning models in some areas, e.g., finance and healthcare, require to be both accurate and globally interpretable. Among them, credit risk assessment is a major application of machine learning for financial institutions to evaluate credit of users and detect default or fraud. Simple white-box models, such as Logistic Regression (LR), are usually used for credit risk assessment, but not powerful enough to model complex nonlinear interactions among features. In contrast, complex black-box models are powerful at modeling, but lack of interpretability, especially global interpretability. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, existing automatic feature crossing methods have problems in efficiency on credit risk assessment, for corresponding data usually contains hundreds of feature fields. In this work, we find local interpretations in Deep Neural Networks (DNNs) of a specific feature are usually inconsistent among different samples. We demonstrate this is caused by nonlinear feature interactions in the hidden layers of DNN. Thus, we can mine feature interactions in DNN, and use them as cross features in LR. This will result in mining cross features more efficiently. Accordingly, we propose a novel automatic feature crossing method called DNN2LR. The final model, which is a LR model empowered with cross features, generated by DNN2LR is a white-box model. We conduct experiments on both public and business datasets from real-world credit risk assessment applications, which show that, DNN2LR outperform both conventional models used for credit assessment and several feature crossing methods. Moreover, comparing with state-of-the-art feature crossing methods, i.e., AutoCross, the proposed DNN2LR method accelerates the speed by about 10 to 40 times on financial credit assessment datasets, which contain hundreds of feature fields.
Development of in-wheel motor systems for formula SAE electric vehicles

With the threat of anthropogenic climate change and humanity’s dependence on non-renewable petroleum, the need for a transition to zero-emission transport is widely acknowledged. Battery electric vehicles represent the most promising solution for urban transport, being the most efficient technology which can be powered from renewable energy sources. As of 2011, most major automobile manufacturers have either released or announced development of electric vehicles, and it is clear that they are going to play a big role in our future transport needs. To date, all such vehicles employ a conventional drivetrain with a single motor, driving the wheels through a transmission and differential. In contrast, many small electric vehicles such as bicycles and scooters have employed in-wheel motor systems (also known as hub motors) – where the electric motor is contained within the wheel hub itself. In-wheel motor systems offer many advantages over conventional drivetrains including fewer moving parts, lower transmission losses, and space savings. However the performance requirements of road-going automobiles has so far precluded the use of in-wheel motor systems. This thesis reviews electric motor technology and recent academic research on in-wheel motor systems to determine the most promising candidates. A design for a direct-drive wheel motor is proposed and optimised through magnetostatic Finite Element Analysis (FEA) experiments. Finally the development of an in-wheel motor system for a Formula SAE Electric vehicle is presented, based on a high-speed motor with reduction drive, and its performance is compared with the direct-drive solution. Formula SAE is an international competition which challenges university students to construct and race Formula-style vehicles. The competition was started in 1978 for petroleum-powered vehicles. In response to growing interest in sustainable transport and electric vehicles, in 2007 many universities began developing electric FSAE vehicles. The competition represents a valuable arena for electric vehicle research and evaluation. tAble of contents page
Diffusion equation engine deep learning for diffuse optical tomography

Diffuse optical tomography (DOT) is a promising non-invasive optical imaging technique that can provide functional information of biological tissues. Since diffuse light undergoes multiple scattering in biological tissues and boundary measurements are limited, DOT reconstruction is ill-posedness and ill-conditioned. To overcome these limitations, Tikhonov regularization is the most popular algorithm. Recently, deep learning based reconstruction methods have attracted increasing attention, and promising results have been reported. However, they lack generalization for unstructured physical model. Therefore, a model-base convolution neural network framework (Model-CNN) is developed. It composes of two layers: data consistency layer and depth layer, which increases the interpretability of the model. Its performance is evaluated with numerical simulations. Our results demonstrate that Model-CNN can get better reconstructed results than those obtained by Tikhonov Regularization in terms of ABE, MSE, and PSNR.
Bistable laminates for energy harvesting

This thesis presents novel research in the area of energy harvesting from broadband vibrations. The aim of energy harvesting is to recover energy wasted or unused in the environment to power low-consumption devices on the order of hundreds of microwatts to milliwatts. The motivation is twofold. In providing a localized, self-contained power source, device reliability, flexibility of installation location can be improved, and maintenance costs can be reduced. Furthermore, reduced reliance on batteries will mitigate the environmental impact associated with resource extraction, and disposal. To this end, this thesis investigates bistable laminates with piezoelectric transduction as broadband energy harvesters. Hitherto, a wealth of literature exists in which narrowband energy harvesters have been studied and optimized to operate over a small frequency interval. While these have been successful to the point of having devices commercially available, many situations exist where the dominant frequencies from which energy is to be harvested change with respect to time, or may be dominated by noise, thus not having a truly dominating frequency. Energy harvesters with nonlinear frequency responses have attracted substantial research interest because of their ability to respond over a broader frequency band. Due to complexities of the response of these harvesters, particularly when the intensity of the vibrational input is high, modeling their behavior is difficult. Designing these harvesters is therefore challenging as the relationships between the various design parameters and power output can be highly involved, or require numerical solutions as analytical solutions may not be possible. This thesis helps to address this knowledge gap. Bistable laminates of both cantilever and plate configuration are studied. Parametric studies are undertaken to empirically demonstrate the relationship between power output and parameters such as resistance load, proof mass addition, operation orientation, different shapes, ply angles, and introduction of adjustable magnetic compression. Modeling work is also undertaken to capture the main features of the nonlinear response such as subharmonics, superharmonics, and snap-through. A study is also carried out to quantify the differences of performance between a linear harvester and an equivalent bistable counterpart. As a practical demonstration, some plate-type harvesters are subjected to excitation patterns based on measured train data. Ultimately, this thesis provides an in depth understanding of bistable shape, layup, and design on harvesting performance.
Engineered co-existence of novel green roof habitat holds conservation promise

Spencer, P.J.A. , (2017) ‘Engineered co-existence of novel green roof habitat holds conservation promise. ’ Proceedings of the 11 Australasian Plant Conservation Conference, 14 – 18 November 2016, Royal Botanic Gardens Victoria, Melbourne. New Approaches to Plant Conservation Challenges in the Modern World. Australian Network for Plant Conservation Inc. ACT, Canberra (Accepted Feb. 2017, In press), Orchid id: 0000 – 0002 – 1452 1099
Bile Reflux Gastritis: Insights into Pathogenesis, Relevant Factors, Carcinomatous Risk, Diagnosis, and Management

Bile reflux gastritis (BRG), a kind of gastrointestinal disorder in clinical practice, is characterized by regurgitation and inflammation. However, lack of guidelines leads to simple cognition and even ignorance of this disease for clinicians. Primarily, making the pathogenesis of BRG clear contributes to a correct and general understanding of this disease for physicians. Next, although recently there has been an increasing awareness among researchers in terms of the relevant factors for BRG, further studies involving large samples are still required to certify the relationship between them explicitly. Besides, researches have established that BRG is closely associated with the development of precancerous lesions and gastric cancer. Till now, there is still no golden standard for diagnosis of BRG. Nevertheless, advances in techniques, especially extensive applications of endoscopy and chemical analysis of reflux contents, have improved our ability to identify the occurrence of this disease as well as distinguishing physiological reflux from pathological reflux. Finally, it is fortunate for patients that more and more importance has been attached to the treatment of BRG. From lifestyle modification to drug therapy to surgery, all of them with the view of realizing symptomatic relief are employed for patients with BRG. In this review, we briefly evaluate this disorder based on the best available evidence, offering an overview of its complicated pathogenesis, diverse relevant factors, potential carcinomatous risk, modern diagnostic investigations, and effective therapeutic plans.
POTENSI ANTIBAKTERI EKSTRAK ETANOL DAUN BELUNTAS (Pluchea indica L.) TERHADAP BAKTERI Staphylococcus epidermidis

Akne vulgaris merupakan penyakit inflamasi kronis pada kelenjar pilosebasea yang sebagian besar terjadi pada masa remaja hingga dewasa muda. Secara global akne vulgaris diderita semua usia dengan prevalensi sebesar 9,38%. Di Indonesia prevalensi selalu meningkat tiap tahunnya. Staphylococcus epidermidis terlibat dalam patogenesis akne vulgaris dibuktikan dengan meningkatnya produksi sebum pada lesi jerawat. Salah satu penanganan akne vulgaris akibat bakteri adalah dengan menggunakan terapi antibiotik. Namun terapi antibiotik memiliki banyak efek yang tidak diinginkan. Beluntas (Pluchea indica L.) digunakan sebagai obat tradisional untuk mengobati demam, disentri, ulkus, reumatik, meredakan luka dan wasir serta digunakan sebagai suplemen untuk diabetes melitus. Tujuan penelitian ini dilakukan untuk melihat aktivitas Pluchea indica sebagai antibakteri terhadap bakteri Staphylococcus epidermidis. Penelitian ini dilakukan dengan melakukan karakterisasi simplisia, skrining fitokimia, dan uji aktivitas antibakteri menggunakan metode difusi cakram dengan seri konsentrasi 45%, 60%, 75% dan 90%. Hasil dari penelitian ini didapatkan bahwa semua uji karakterisasi simplisia memenuhi persyaratan. Ekstrak dari daun beluntas positif alkaloid, flavonoid, tannin dan saponin. Uji aktivitas ekstrak daun Pluchea indica menunjukkan zona hambat 16,10 mm, 17,30 mm, 18,13 mm dan 18,63 mm untuk konsentrasi 45%, 60%, 75% dan 90%, secara berurutan. Dari hasil yang didapatkan daun beluntas berpotensi dikembangkan sebagai antibakteri.
Redeﬁning Neighbor for Improved Knowledge Graph Based Recommendations

Recommender systems are ubiquitous in today’s customer facing services. Traditionally, researchers have modelled recommender system as a collaborative filtering problem. Data prepared for this type of problem can be represented by a tuple (c, r, i), indicating customer c has interacted through relation r with item i. These tuples can also be stored in matrix form, with each array element representing the user’s prior interactions with the items (e.g. watched, rated, bought the item) for each user-item pair. The downside of traditional collaborative filtering method is that it only makes use of information in the utility matrix but not metadata available for items. In other words, the problem formulation only looks for similar users who also bought the item, or similar items bought by the same user, ignoring other similarity in items such as movies starred by the same actor or books authored by the same person. Inclusion of knowledge graph addresses this issue, but introduces a rather sophisticated set of new relations and millions more entities. Combining user-item interactions with knowledge graph results in a hybrid graph that requires more advanced handling and training methods. Current state-of-the-art models which build on graph convolutional networks retain existing neighborhood structure as-is. While this approach respects the given network structure, it leaves out potential correlations among similar items and structures in the network. As pointed out by Wu (1), distant neighbors bring little information, thus decreasing the utility of additional convolution layers, yet no existing work can be found on alternative local neighborhood definition. More intuitively, instead of growing graph convolution in depth, we would like to change its grows horizontally. In this work, we bridge the gap by experimenting with a number of neighborhood definitions, hoping to improve embedding propagation, a step critical for utilizing internal structures and similarities within the knowledge graph. Neighborhood definitions experimented in our work include grouping by cluster, choosing top-k similar nodes and pooling 2-hop neighbors. To test our ideas, we simultaneously developed a more generic framework for Pytorch Geometric to support processing of different types of graphs with interchangeable neighbor discovery and definition function. Through our experiments, we are able to show that using nodes from 2-hops away yields the improved result, leading to recall@20 of 0.1495 and ndcg@20 of 0.1011 for Amazon Book dataset and ndcg@20 of 0.1327 for Last-FM dataset, higher than that achived by vanilla KGAT-implementation. More significant improvements are observed when the models are trained without pre-trained embeddings under the same experiment condition.
Casimir effect as a sum over modes in dissipative systems

The aim of this paper is to show that within the open-system framework the sum-over-modes approach \'a la Casimir leads to the Lifshitz formula for the Casimir free energy. A general result applicable to arbitrary geometries is obtained through the use of Ford, Lewis, & O'Connell's remarkable formula. Additionally, we address the possibility for obtaining the Casimir energy as a sum over complex "modes". We show in this case that the standard sum-over-modes formula must be suitably generalized to avert unphysical complex energies. Finally, we apply our results to several standard examples.
Perceptual compensation for vowel undershoot may be explained by general perceptual principles.

When presented a series of /dVd/ syllables varying perceptually from /ded/ to /d■d/, listeners identify the vowel sound as /■/ more often than when the vowels are presented alone as steady states. Conversely, /bVb/ series lead to more /e/ identifications. This pattern of responses appears to demonstrate a compensation for assimilatory effects of coarticulation or vowel ‘‘undershoot. ’’ Experiments will be presented which test the specificity of this perceptual process. Two ten‐step (/bVb/ and /dVd/) series of 100‐ms /CVC/ syllables varying in F2 midpoint frequency (1260–1710 Hz) were synthesized. Nonspeech analogs were created by appending 70‐ms sine‐wave glides, which followed a similar trajectory to F2 in the speech stimuli, to 60‐ms steady‐state vowels varying in F2 frequency. A second set of nonspeech series was created by appending constant‐frequency sine‐wave tones to the vowel stimuli. The frequency of these tones were set at the F2 starting frequencies for the speech stimuli. These nonspeech contexts had effects on vowel identification that were analogous to those for the speech contexts. Lower frequency glides and tones (modeling /bVb/F2) led to more /e/ (higher F2) identifications with the complementary pattern for higher frequency glides and tones. [Work supported by NIDCD and NSF. ]
Challenges Facing Statistical Genetics

Covariances: A Bayesian Alternative to Kriging,” Journal of Multivariate Analysis, 43, 35 1-374. Liang, K. Y. , and Zeger, S. L. (1986), “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, 73, 13-22. Lubin, J. H. , Boice, J. D. J., and Edling, C. (1994). Radon and Lung Cancer Risk A Joint Analysis of 11 Underground Miners Studies, Bethesda, MD: U.S. Department of Health and Human Services. Lumley, T. , and Levy, D. (1999), “Bias in the Case-Crossover Design: Implications for Studies of Air Pollution,” unpublished manuscript. Maclure, M. (1991). “The Case-Crossover Design: A Method for Studying Transient Effects on the Risk of Acute Events,” American Journal of Epidemiology, 133, 144153. Madigan, D. , and Raftery, A. E. (1994), “Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam’s Window,” Journal of the American Statistical Association, 89, 1335-1346. Marshall, R. (1991), “Mapping Disease and Mortality Rates Using Empirical Bayes Estimators,” Statistics in Medicine, 40, 283-294. Mollie, A. , and Richardson, S. (1991), “Empirical Bayes Estimates of Cancer Mortality Rates Using Spatial Models,” Statistics in Medicine, 10,