Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulk superconductivity and non-trivial band topology analysis of Pb2Pd

In this article, we report single crystal growth of superconducting binary compound Pb2Pd. The crystal is well characterized through x-ray diffraction, selected area electron diffraction, transmission electron microscopy, field emission scanning electron microscopy and x-ray photoelectron spectroscopy. The bulk superconducting nature of the synthesized crystal is determined through AC susceptibility and magneto-heat capacity measurements. The specific heat jump at superconducting transition suggests Pb2Pd to be a moderately coupled s-wave superconductor. The topological non-trivial character of Pb2Pd is evidenced through bulk electronic band structure and Z2 invariants, which are calculated under the protocols of density functional theory. Surface states spectrum of Pb2Pd is also studied, which further claims Pb2Pd to have topological non-trivial band structure.
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning

We consider the regret minimization problem in reinforcement learning (RL) in the episodic setting. In many real-world RL environments, the state and action spaces are continuous or very large. Existing approaches establish regret guarantees by either a low-dimensional representation of the stochastic transition model or an approximation of the $Q$-functions. However, the understanding of function approximation schemes for state-value functions largely remains missing. In this paper, we propose an online model-based RL algorithm, namely the CME-RL, that learns representations of transition distributions as embeddings in a reproducing kernel Hilbert space while carefully balancing the exploitation-exploration tradeoff. We demonstrate the efficiency of our algorithm by proving a frequentist (worst-case) regret bound that is of order $\tilde{O}\big(H\gamma_N\sqrt{N}\big)$\footnote{ $\tilde{O}(\cdot)$ hides only absolute constant and poly-logarithmic factors. }, where $H$ is the episode length, $N$ is the total number of time steps and $\gamma_N$ is an information theoretic quantity relating the effective dimension of the state-action feature space. Our method bypasses the need for estimating transition probabilities and applies to any domain on which kernels can be defined. It also brings new insights into the general theory of kernel methods for approximate inference and RL regret minimization.
Transparent single-cell set classification with kernel mean embeddings

Modern single-cell flow and mass cytometry technologies measure the expression of several proteins of the individual cells within a blood or tissue sample. Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's associated phenotype with machine learning models. Such a large set cardinality also limits the interpretability of machine learning models due to the difficulty in tracking how each individual cell influences the ultimate prediction. We propose using Kernel Mean Embedding to encode the cellular landscape of each profiled biological sample. Although our foremost goal is to make a more transparent model, we find that our method achieves comparable or better accuracies than the state-of-the-art gating-free methods through a simple linear classifier. As a result, our model contains few parameters but still performs similarly to deep learning models with millions of parameters. In contrast with deep learning approaches, the linearity and sub-selection step of our model makes it easy to interpret classification results. Analysis further shows that our method admits rich biological interpretability for linking cellular heterogeneity to clinical phenotype.
Dojo: A Differentiable Physics Engine for Robotics

We present Dojo, a differentiable physics engine for robotics that prioritizes stable simulation, accurate contact physics, and differentiability with respect to states, actions, and system parameters. Dojo achieves stable simulation at low sample rates and conserves energy and momentum by employing a variational integrator. A nonlinear complementarity problem with second-order cones for friction models hard contact, and is reliably solved using a custom primal-dual interior-point method. Special properties of the interior-point method are exploited using implicit differentiation to efficiently compute smooth gradients that provide useful information through contact events. We demonstrate Dojo with a number of examples including: planning, policy optimization, and system identification, that demonstrate the engine's unique ability to simulate hard contact while providing smooth, analytic gradients.
Stability region and critical delay

The location of roots of the characteristic equation of a linear delay diﬀerential equation (DDE) determines the stability of the linear DDE. However, by its transcendency, there is no general criterion on the contained parameters for the stability. Here we mainly concentrate on the study of a simple transcendental equation ( ∗ ) z + a − w e − τz = 0 with coeﬃcients of real a and complex w and a delay parameter τ > 0 to tackle this transcendency brought by delay. The consideration is twofold: (i) to give the stability region in the parameter space for Eq. ( ∗ ) by using the critical delay and (ii) to compare this with a graphical method (so-called the method of D-partitions) by combining with the delay sequence obtained by conditions for purely imaginary roots. By (i), we obtain another proof of Hayes’ and Sakata’s results, which reveals the nature of imaginary w case in Eq. ( ∗ ). By (ii), we propose a method combining the analytic one and geometric one. This combination is important because it will be helpful in studying characteristic equations having higher-dimensional parameters.
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning

Contrastive self-supervised learning (SSL) learns an embedding space that maps similar data pairs closer and dissimilar data pairs farther apart. Despite its success, one issue has been overlooked: the fairness aspect of representations learned using contrastive SSL. Without mitigation, contrastive SSL techniques can incorporate sensitive information such as gender or race and cause potentially unfair predictions on downstream tasks. In this paper, we propose a Conditional Contrastive Learning (CCL) approach to improve the fairness of contrastive SSL methods. Our approach samples positive and negative pairs from distributions conditioning on the sensitive attribute, or empirically speaking, sampling positive and negative pairs from the same gender or the same race. We show that our approach provably maximizes the conditional mutual information between the learned representations of the positive pairs, and reduces the effect of the sensitive attribute by taking it as the conditional variable. On seven fairness and vision datasets, we empirically demonstrate that the proposed approach achieves state-of-the-art downstream performances compared to unsupervised baselines and significantly improves the fairness of contrastive SSL models on multiple fairness metrics.
Automatic expert knowledge acquisition from text for closing the knowledge loop in PLM

Expert knowledge is important for any product's lifecycle, especially during the manufacturing part of the lifecycle. Most of such knowledge is obtained through experience and its reuse can help prevent potential issues in subsequent product development. Extracting the knowledge acquired for reuse in subsequent development closes the knowledge loop within a product's lifecycle. This work is aimed at acquiring expert knowledge from text documents, about problems faced in aircraft assembly. For doing so, a method is proposed, and is implemented using a pipeline of natural language processing tools. Though existing tools have been used, domain related studies and subsequent modifications have been shown to be necessary. An implementation of the proposed method is also developed and evaluated. Future directions for expanding the work have also been discussed.
Relationship of excessive daytime sleepiness with bruxism, depression, anxiety, stress, and sex in odontology students – A cross sectional study

Background To verify if there is a correlation of excessive daytime sleepiness (EDS) with bruxism, depression, anxiety, stress, and sex in odontology students. Material and Methods Four hundred dental students were selected. Students included in the study were those that were healthy, over 18 years old, and with similar weekly academic activities. Students excluded were those with temporomandibular disfunction, a high possibility of possessing obstructive sleep apnea and illnesses that cause EDS; along with smokers, users of illicit drugs, users of psychiatric medication, and those that abuse the consumption of alcohol. After the application of these criteria, 128 students were included in this study. EDS, bruxism, depression, anxiety, and stress were evaluated by the Epworth Sleepiness Scale, the Pintado et al. questionnaire, Beck Depression Inventory, Beck Anxiety Inventory, and the LIPP Adult Stress Symptom Inventory. Afterwards, the Spearman test (p< 0.05) was applied. Results A high probability of EDS was present in 33.6% of the students. There was a positive correlation of EDS with females (Rank Correlation= 0.209; p= 0.018) and depression (Rank Correlation= 0.174; p= 0.049); between females and stress (Rank Correlation= 0.199; p= 0.024), and between females and anxiety (Rank Correlation= 0.178; p= 0.045). There was a positive correlation of bruxism with anxiety (Rank Correlation= 0.255; p= 0.004) and stress (Rank Correlation= 0.201; p= 0.023). There was no correlation of EDS with bruxism (p= 0.354), stress (p= 0.277), or anxiety (p= 0.114). There was no correlation of bruxism with females (p= 0.082) or depression (p= 0.362). Conclusions A high probability of EDS was present in 33.6% of dentistry students, and there was a positive correlation of EDS with females and depression. Key words:Bruxism, depression, anxiety, stress, disorders of excessive somnolence, dentistry.
Blood Mononuclear Cells Lymphoblastoid T Cells and Primary Peripheral CREB Transcription Factor in Both Jurkat Phosphorylation and Activation of 133 Rapid Ser Extracellular HIV-1 Tat Protein Induces the

Extracellular HIV-1 Tat protein (0.1–100 ng/ml) induced a rapid (peak at 30 min) increase in the Ser 133 phosphorylation levels of the transcription factor CREB in serum-starved Jurkat cells, as revealed by Western blot and indirect immunoﬂuorescence analyses. Nuclear cAMP-responsive element (CRE) binding activity in electrophoretic mobility shift assays was constitutive in unstimulated Jurkat cells, showing only a small increase upon Tat treatment. However, transient transfection experiments performed with various chloramphenicol acetyl-transferase (CAT) constructs showed that Tat produced a fourfold induction of CAT activity only in the presence of a CRE-dependent CAT construct. Moreover, the use of plasmids encoding for GAL4-CREB fusion proteins demonstrated that Tat induction of pG4-CAT reporter gene required the CREB moiety of the GAL4-CREB fusion protein and that Ser 133 CREB was essential for Tat activity. Extracellular Tat also stimulated Ser 133 CREB phosphorylation in freshly isolated PBMC; this effect was completely blocked by either staurosporin, a broad-spectrum inhibitor of various protein kinases, or PD 98059, a speciﬁc inhibitor of mitogen-activated protein kinases (MAPK). Furthermore, extracellular Tat induced a rapid (peak at 5–15 min) stimulation of the MAPK catalytic activity in primary PBMC. Altogether, these ﬁndings suggest that HIV-1 Tat protein activates CREB in lymphoid cells through a signal cascade involving the MAPK pathway. The Journal of Immunology, 1998, 160: 3891–3898.
profiling of the synergistic effects of melittin in combination with cisplatin on ovarian cancer cells.

: Melittin, the main peptide present in bee venom, has been proposed as having potential for anticancer therapy; the addition of melittin to cisplatin, a ﬁrst line treatment for ovarian cancer, may increase the therapeutic response in cancer treatment via synergy, resulting in improved tolerability, reduced relapse, and decreased drug resistance. Thus, this study was designed to compare the metabolomic effects of melittin in combination with cisplatin in cisplatin-sensitive (A2780) and resistant (A2780CR) ovarian cancer cells. Liquid chromatography (LC) coupled with mass spectrometry (MS) was applied to identify metabolic changes in A2780 (combination treatment 5 µ g/mL melittin + 2 µ g/mL cisplatin) and A2780CR (combination treatment 2 µ g/mL melittin + 10 µ g/mL cisplatin) cells. Principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) multivariate data analysis models were produced using SIMCA-P software. All models displayed good separation between experimental groups and high-quality goodness of ﬁt (R 2 ) and goodness of prediction (Q 2 ), respectively. The combination treatment induced signiﬁcant changes in both cell lines involving reduction in the levels of metabolites in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, purine and pyrimidine metabolism, and the arginine/proline pathway. The combination of melittin with cisplatin that targets these pathways had a synergistic effect. The melittin-cisplatin combination had a stronger effect on the A2780 cell line in comparison with the A2780CR cell line. The metabolic effects of melittin and cisplatin in combination were very different from those of each agent alone.
A quantum ﬂuctuation theorem for dissipative processes

We present a general quantum ﬂuctuation theorem for the entropy production of an open quantum system coupled to multiple environments, not necessarily at equilibrium. Such a general theorem, when restricted to the weak-coupling and Markovian regime, holds for both local and global master equations, corroborating the thermodynamic consistency of local quantum master equations. The theorem is genuinely quantum, as it can be expressed in terms of conservation of a Hermitian operator, describing the dynamics of the system state operator and of the entropy change in the baths. The integral ﬂuctuation theorem follows from the properties of such an operator. Furthermore, it is also valid when the system is described by a time-dependent Hamiltonian. As such, the quantum Jarzynski equality is a particular case of the general result presented here. Moreover, our result can be extended to nonthermal baths, as long as microreversibility is preserved. We present some numerical examples to showcase the exact results previously obtained. We ﬁnally generalize the ﬂuctuation theorem to the case where the interaction between the system and the bath is explicitly taken into account. We show that the ﬂuctuation theorem amounts to a relation between time-reversed dynamics of the global density matrix and a two-time correlation function along the forward dynamics involving the baths’ entropy alone.
Time dependence of biomarkers

Background: We investigated the impact of follow-up duration to determine whether two immunohistochemical prognostic panels, IHC4 and Mammostrat, provide information on the risk of early or late distant recurrence using the Edinburgh Breast Conservation Series and the Tamoxifen vs Exemestane Adjuvant Multinational (TEAM) trial. Methods: The multivariable fractional polynomial time (MFPT) algorithm was used to determine which variables had possible non-proportional effects. The performance of the scores was assessed at various lengths of follow-up and Cox regression modelling was performed over the intervals of 0–5 years and 4 5 years. Results: We observed a strong time dependence of both the IHC4 and Mammostrat scores, with their effects decreasing over time. In the first 5 years of follow-up only, the addition of both scores to clinical factors provided statistically significant information ( P o 0.05), with increases in R 2 between 5 and 6% and increases in D-statistic between 0.16 and 0.21. Conclusions: Our analyses confirm that the IHC4 and Mammostrat scores are strong prognostic factors for time to distant recurrence but this is restricted to the first 5 years after diagnosis. This provides evidence for their combined use to predict early recurrence events in order to select those patients who may/will benefit from adjuvant chemotherapy. Prediction of residual risk following adjuvant endocrine treatment in early breast cancer has become a critical component in the selection of treatment options. In addition, recent data on the impact of up to 10 years of adjuvant endocrine therapy in early breast cancer suggest that some women may benefit from extended treatment with tamoxifen(Goss et al , 2003; Davies et al , 2013; Gray et al , 2013). At present, patients and their clinicians may be presented with three time-related choices related to the treatment of luminal breast cancers: (1) whether it is beneficial to treat patients with adjuvant chemotherapy and therefore delay endocrine therapy; (2) whether to follow a switch strategy for endocrine therapy; and (3) whether to extend endocrine therapy after 5 years for a total of 10 years. Multiple diagnostic algorithms have been developed to provide information on residual risk for patients facing these choices; however, there is limited information on how time impacts the risk assessments provided by these diagnostic tools.
Resilience in Mechanical Engineering - A Concept for Controlling Uncertainty during Design, Production and Usage Phase of Load-Carrying Structures

Resilience as a concept has found its way into different disciplines to describe the ability of an individual or system to withstand and adapt to changes in its environment. In this paper, we provide an overview of the concept in different communities and extend it to the area of mechanical engineering. Furthermore, we present metrics to measure resilience in technical systems and illustrate them by applying them to load-carrying structures. By giving application examples from the Collaborative Research Centre (CRC) 805, we show how the concept of resilience can be used to control uncertainty during different stages of product life.
Imputing other survey data sets

Missing data is a serious problem in cross-cultural survey research, especially for regression models employing data sets such as the Ethnographic Atlas (EA) or the Standard Cross-Cultural Sample (SCCS). In comparative survey research the most common procedure for handling missing data is listwise deletion, where one simply drops any observation containing a missing value for any of the model’s variables. Listwise deletion leads to the loss of all non-missing information in the dropped rows, frequently leading to statistical analysis being based on subsamples that are no longer representative of the full sample. Estimates based on such subsamples are valid only if certain assumptions are made about the mechanism(s) by which the data become missing. These assumptions are reviewed by Dow and Eff (2009b) who conclude that they will seldom hold for cross-cultural data sets.
on the fly : the effects of wing position on noctuid moth

Title: Hearing on the fly: the effects of wing position on noctuid moth hearing Summary Noctuid hearing sensitivity. Abstract The ear of the noctuid moth has only two auditory neurons, A1 and A2, which function in detecting 16 predatory bats. However, the noctuid’s ears are located on the thorax behind the wings. Therefore, 17 since these moths need to hear during flight, it was hypothesized that wing position may affect their 18 hearing. The wing was fixed in three different positions: up, flat, and down. An additional subset of 19 animals was measured with freely moving wings. In order to negate any possible acoustic 20 shadowing or diffractive effects, all wings were snipped, leaving the proximal most portion and the 21 wing hinge intact. Results revealed that wing position plays a factor in threshold sensitivity of the 22 less sensitive auditory neuron A2, but not in the more sensitive neuron A1. Furthermore, when the 23 wing was set in the down position, fewer A1 action potentials were generated prior to the initiation 24 of A2 activity. Analyzing the motion of the tympanal membrane did not reveal differences in 25 movement due to wing position. Therefore, these neural differences due to wing position are 26 proposed to be due to other factors within the animal such as different muscle tensions.
Systematic mapping review on student’s performance analysis using big data predictive model

This paper classify the various existing predicting models that are used for monitoring and improving students’ performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students’ performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.
Implementation of a Beacon-Enabled Mobile Indoor Navigation System Using Augmented Reality

: In last decade, expansion of mobile phones increased people’s usage of these devices. In parallel with the increasing usage rate, the number of developed applications increased for mobile phones. Thanks to mobile phones, many different fields have been affected. However, the biggest contributions of mobile phones are orientation and location finder services. Although people spend major time of their life in indoor areas, usage of navigation systems must be outdoors since these systems get coordinate information from satellites. In indoor areas, walls, and any object in a building cause wrong results or block navigation systems. In this study, orientation and location finder services for indoor navigation will be done by using accelerometer, compass and camera that have been already included in the phones and augmented reality technology for iOS based mobile phones and locations will be provided thanks to beacon Bluetooth devices.