Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Santa Claus, Hypergraphs and Matroids

A well-known problem in scheduling and approximation algorithms is the Santa Claus problem. Suppose that Santa Claus has a set of gifts, and he wants to distribute them among a set of children so that the least happy child is made as happy as possible. Here, the value that a child $i$ has for a present $j$ is of the form $p_{ij} \in \{ 0,p_j\}$. A polynomial time algorithm by Annamalai et al. gives a $12.33$-approximation and is based on a modification of Haxell's hypergraph matching argument. In this paper, we introduce a matroid version of the Santa Claus problem. Our algorithm is also based on Haxell's augmenting tree, but with the introduction of the matroid structure we solve a more general problem with cleaner methods. Our result can then be used as a blackbox to obtain a $(4+\varepsilon)$-approximation for Santa Claus. This factor also compares against a natural, compact LP for Santa Claus.
Label Aggregation via Finding Consensus Between Models

Label aggregation is an efficient and low cost way to make large datasets for supervised learning. It takes the noisy labels provided by non-experts and infers the unknown true labels. In this paper, we propose a novel label aggregation algorithm which includes a label aggregation neural network. The learning task in this paper is unsupervised. In order to train the neural network, we try to design a suitable guiding model to define the loss function. The optimization goal of our algorithm is to find the consensus between the predictions of the neural network and the guiding model. This algorithm is easy to optimize using mini-batch stochastic optimization methods. Since the choices of the neural network and the guiding model are very flexible, our label aggregation algorithm is easy to extend. According to the algorithm framework, we design two novel models to aggregate noisy labels. Experimental results show that our models achieve better results than state-of-the-art label aggregation methods.
Relationship between effective and demographic population size in continuously distributed populations

Genetic monitoring of wild populations can offer insights into demographic and genetic information simultaneously. However, widespread application of genetic monitoring is hindered by large uncertainty in the estimation and interpretation of target metrics such as contemporary effective population size, Ne. We used four long‐term genetic and demographic studies (≥9 years) to evaluate the temporal stability of the relationship between Ne and demographic population size (Nc). These case studies focused on mammals that are continuously distributed, yet dispersal‐limited within the spatial scale of the study. We estimated local, contemporary Ne with single‐sample methods (LDNE, Heterozygosity Excess, and Molecular Ancestry) and demographic abundance with either mark–recapture estimates or catch‐per‐unit effort indices. Estimates of Ne varied widely within each case study suggesting interpretation of estimates is challenging. We found inconsistent correlations and trends both among estimates of Ne and between Ne and Nc suggesting the value of Ne as an indicator of Nc is limited in some cases. In the two case studies with consistent trends between Ne and Nc, FIS was more stable over time and lower, suggesting FIS may be a good indicator that the population was sampled at a spatial scale at which genetic structure is not biasing estimates of Ne. These results suggest that more empirical work on the estimation of Ne in continuous populations is needed to understand the appropriate context to use LDNe as a useful metric in a monitoring programme to detect temporal trends in either Ne or Nc.
Evaluating Word Embeddings in Multi-label Classification Using Fine-Grained Name Typing

Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence context.
CCR2-dependent monocytes/macrophages exacerbate acute brain injury but promote functional recovery after ischemic stroke in mice

Rationale: Peripheral blood monocytes are recruited into the ischemic brain and transform into macrophages after stroke. Nevertheless, the exact role of CCR2-dependent monocytes/macrophages in brain injury after stroke remains elusive. Methods: We used CCR2 knockout (KO) mice and the CCR2 pharmacological inhibitor, propagermanium (PG), to address the role of CCR2-dependent monocytes/macrophages in the acute stage and neurological functional recovery after middle cerebral artery (MCA) occlusion and reperfusion. Results: CCR2 KO resulted in smaller infarct size and lower mortality than in wild type (WT) mice, when measured 3 days after stroke. However, from 5 to 28 days after stroke, the KO mice had higher mortality and showed no obvious neurological functional recovery. In addition, WT mice treated with PG had similar stroke outcomes compared with CCR2 KO, as measured by T2 weighted MRI. Flow cytometry and real-time PCR analyses suggest that monocyte-derived macrophages (MoDMs) in the stroke brains mainly polarized to pro-inflammatory macrophages at the early stage, but gradually switched to anti-inflammatory macrophages at 7 days after stroke. In addition, adoptive transfer of anti-inflammatory macrophages into CCR2 KO mice at 4 and 6 days after stroke alleviated mortality and promoted neurological recovery. Conclusion: CCR2-dependent monocytes/macrophages are a double-edged sword; they worsen acute brain injury, but are essential for neurological recovery by promoting anti-inflammatory macrophage polarization.
A unified theory of adaptive stochastic gradient descent as Bayesian filtering

We formulate the problem of neural network optimization as Bayesian filtering, where the observations are the backpropagated gradients. While neural network optimization has previously been studied using natural gradient methods which are closely related to Bayesian inference, they were unable to recover standard optimizers such as Adam and RMSprop with a root-mean-square gradient normalizer, instead getting a mean-square normalizer. To recover the root-mean-square normalizer, we find it necessary to account for the temporal dynamics of all the other parameters as they are geing optimized. The resulting optimizer, AdaBayes, adaptively transitions between SGD-like and Adam-like behaviour, automatically recovers AdamW, a state of the art variant of Adam with decoupled weight decay, and has generalisation performance competitive with SGD.
Bcl-2 Overexpression Improves Survival and Efficacy of Neural Stem Cell-Mediated Enzyme Prodrug Therapy

Tumor-tropic neural stem cells (NSCs) can be engineered to localize gene therapies to invasive brain tumors. However, like other stem cell-based therapies, survival of therapeutic NSCs after transplantation is currently suboptimal. One approach to prolonging cell survival is to transiently overexpress an antiapoptotic protein within the cells prior to transplantation. Here, we investigate the utility and safety of this approach using a clinically tested, v-myc immortalized, human NSC line engineered to contain the suicide gene, cytosine deaminase (CD-NSCs). We demonstrate that both adenoviral- and minicircle-driven expression of the antiapoptotic protein Bcl-2 can partially rescue CD-NSCs from transplant-associated insults. We further demonstrate that the improved CD-NSC survival afforded by transient Bcl-2 overexpression results in decreased tumor burden in an orthotopic xenograft glioma mouse model following administrations of intracerebral CD-NSCs and systemic prodrug. Importantly, no evidence of CD-NSC transformation was observed upon transient overexpression of Bcl-2. This research highlights a critical need to develop clinically relevant strategies to improve survival of therapeutic stem cell posttransplantation. We demonstrate for the first time in this disease setting that improving CD-NSC survival using Bcl-2 overexpression can significantly improve therapeutic outcomes.
Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

As an attempt to reduce reliance on fossil fuels to satisfy electricity demand, the penetration of renewable sources like wind or solar power has experienced rapid growth in recent years. Due to their intermittent nature, the exact contribution of renewable sources to electricity supply is at least partly unknown. In order to be able to accommodate this uncertainty in real-time, sufficient capacities in the form of thermal and hydro power plants must be available. On that account, power system operators solve the so-called unit commitment problem after bidding in the day-ahead market closes to derive schedules for their power plants which can ensure system reliability at low costs. In literature, two approaches have mainly been studied in this context: Robust and stochastic unit commitment. Being a worst-case formulation, robust unit commitment puts its focus on reliability. A common drawback of this approach is that it tends to deliver over-conservative schedules. Stochastic unit commitment on the other hand creates more cost-effective schedules by preparing for the expected case, but either fails to guarantee system reliability or puts a high workload on the CPU. The limitations of known formulations have sparked interest in so-called hybrid approaches, which aim at combining the ideas of robust and stochastic unit commitment in a favorable way. This paper reports a novel hybrid approach to solve the unit commitment under uncertainty, which yields both robust and cost-efficient schedules. The new method respects the continuous nature of uncertainties and is thus in particular favorable for applications in power systems with high penetration of volatile renewable sources. By merging the ideas of robust and stochastic unit commitment, the proposed hybrid formulation minimizes the expected worst-case dispatch costs. Our method relies on partitioning the continuous range of the uncertainties into subsets. By means of the number of partitions, the solution can be adjusted between the conservative robust and the cost-efficient stochastic unit commitment in a user-friendly manner. A Benders decomposition algorithm is derived to solve the hybrid unit commitment efficiently. Finally, a case study confirms the superior performance of the proposed method.
Overexpression of Phosphate Transporter Gene CmPht1;2 Facilitated Pi Uptake and Alternated the Metabolic Profiles of Chrysanthemum Under Phosphate Deficiency

Low availability of phosphorus (P) in the soil is the principal limiting factor for the growth of cut chrysanthemum. Plant phosphate transporters (PTs) facilitate acquisition of inorganic phosphate (Pi) and its homeostasis within the plant. In the present study, CmPht1;2 of the Pht1 family was cloned from chrysanthemum. CmPht1;2 is composed of 12 transmembrane domains and localized to the plasma membrane. Expression of CmPht1;2 in roots was induced by Pi starvation. Chrysanthemum plants with overexpression of CmPht1;2 (Oe) showed higher Pi uptake, as compared to the wild type (WT), both under Pi-starvation and Pi-sufficient conditions, and also showed a higher root biomass compared to WT in the Pi-starvation conditions. Seven days after the P-deficiency treatment, 85 distinct analytes were identified in the roots and 27 in the shoots between the Oe1 plant and WT, in which sophorose, sorbitol (sugars), hydroxybutyric acid (organic acids), and ornithine (amino acid) of CmPht1;2 overexpressing chrysanthemum are specific responses to P-starvation.
Long non-coding RNA CRYBG3 regulates glycolysis of lung cancer cells by interacting with lactate dehydrogenase A

Cancer cells usually utilize glucose as a carbon source for aerobic glycolysis, a phenomenon known as the Warburg effect. And a high rate of glycolysis has been observed in lung cancer cells. The growing evidence indicates that long non-coding RNAs (lncRNAs) are important players in lung cancer initiation and progression. However, the correlation between lncRNAs and glycolysis remains unclear. In this study, we recognized a lncRNA, LNC CRYBG3, which can interact with lactate dehydrogenase A (LDHA), a vital enzyme of glycolysis, is highly upregulated in both clinical lung cancer tissues and in vitro cultured lung cancer cell lines. A positive correlation between the expression level of LNC CRYBG3 and LDHA expression levels is observed. In another hand, LNC CRYBG3 is a regulator of glycolysis and its overexpression promoted the uptake of glucose and the production of lactate whereas the knockdown of LNC CRYBG3 led to opposite results and suppressed cell proliferation. These results indicated that LNC CRYBG3 might be a novel target for lung cancer treatment.
Interventricular differences in sodium current and its potential role in Brugada syndrome

Brugada syndrome (BrS) is an inherited disease associated with ST elevation in the right precordial leads, polymorphic ventricular tachycardia (PVT), and sudden cardiac death in adults. Mutations in the cardiac sodium channel account for a large fraction of BrS cases. BrS manifests in the right ventricle (RV), which led us to examine the biophysical and molecular properties of sodium channel in myocytes isolated from the left (LV) and right ventricle. Patch clamp was used to record sodium current (INa) in single canine RV and LV epicardial (epi) and endocardial (endo) myocytes. Action potentials were recorded from multicellular preparations and single cells. mRNA and proteins were determined using quantitative RT‐PCR and Western blot. Although LV wedge preparations were thicker than RV wedges, transmural ECG recordings showed no difference in the width of the QRS complex or transmural conduction time. Action potential characteristics showed RV epi and endo had a lower Vmax compared with LV epi and endo cells. Peak INa density was significantly lower in epi and endo RV cells compared with epi and endo LV cells. Recovery from inactivation of INa in RV cells was slightly faster and half maximal steady‐state inactivation was more positive. β2 and β4 mRNA was detected at very low levels in both ventricles, which was confirmed at the protein level. Our observations demonstrate that Vmax and Na+ current are smaller in RV, presumably due to differential Nav1.5/β subunit expression. These results provide a potential mechanism for the right ventricular manifestation of BrS.
ELTD1 Function in Hepatocellular Carcinoma is Carcinoma-Associated Fibroblast-Dependent

Introduction: EGF, latrophilin, and seven transmembrane domain containing 1 (ELTD1) constitutes an orphan G-protein-coupled receptor (GPCR) of the adhesion family. High expression of ELTD1 is correlated with favorable prognosis of hepatocellular carcinoma (HCC). After silencing ELTD1 expression, however, tumor invasiveness is drastically reduced. The underlying mechanism of this apparent contradictory phenomenon is unknown. Because adhesion GPCRs couple extracellular adhesion to intracellular signaling, as a member of this family, ELTD1 function may be related to its tumor microenvironment. We therefore investigated the interaction between ELTD1 and the HCC tumor microenvironment. Methods: ELTD1 expression was assessed by immunohistochemical analyses of tissue samples from two independent groups of 333 patients with HCC. Correlations between the ELTD1 expression and the clinicopathological values were examined. We also constructed ELTD1 overexpression and knockdown HCC cell lines and conducted a series of in vivo and in vitro ELTD1 functional assays. We further collected carcinoma associated fibroblast (CAF) culture supernatants to culture HCC cell lines and repeat the respective functional assays in comparison with the control group. Results: Clinicopathologic correlations and in vivo models indicated ELTD1 as a tumor suppressor gene, whereas in vitro experiments suggested that ELTD1 could promote malignancy in HCC cell lines. Immunohistochemical staining of the generated ELTD1 overexpression xenograft tumors demonstrated that the CAF markers vimentin and α-SMA were highly expressed compared to the control group. This suggests that ELTD1 expression is correlated to CAF distribution. In addition, culturing with CAF supernatants inhibited HCC cell proliferation and invasion rates, confirming the correlation between CAF and ELTD1. Conclusion: The results of this study indicated that ELTD1 regulation of HCC progression is CAF-dependent, suggesting that ELTD1 function is regulated by its tumor microenvironment. Further investigation is required to determine the underlying mechanisms.
Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings

Abstract In this paper, we explore the application of artificial neural network (‘deep learning’) methods to the problem of detecting chemical-protein interactions in PubMed abstracts. We present here a system using multiple Long Short Term Memory layers to analyse candidate interactions, to determine whether there is a relation and which type. A particular feature of our system is the use of unlabelled data, both to pre-train word embeddings and also pre-train LSTM layers in the neural network. On the BioCreative VI CHEMPROT test corpus, our system achieves an F score of 61.51% (56.10% precision, 67.84% recall).
The Successor Representation: Its Computational Logic and Neural Substrates

Reinforcement learning is the process by which an agent learns to predict long-term future reward. We now understand a great deal about the brain's reinforcement learning algorithms, but we know considerably less about the representations of states and actions over which these algorithms operate. A useful starting point is asking what kinds of representations we would want the brain to have, given the constraints on its computational architecture. Following this logic leads to the idea of the successor representation, which encodes states of the environment in terms of their predictive relationships with other states. Recent behavioral and neural studies have provided evidence for the successor representation, and computational studies have explored ways to extend the original idea. This paper reviews progress on these fronts, organizing them within a broader framework for understanding how the brain negotiates tradeoffs between efficiency and flexibility for reinforcement learning.
A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research

In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.
Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization

We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality of different architectures. The drawbacks of the F1-metric are analyzed. Several techniques are proposed to improve the quality of AD: exponentially weighted smoothing, mean p-powered error measure, individual error weight for each variable, disjoint prediction windows. Based on the techniques used, an approach to anomaly interpretation is introduced.
A new species of the genus Blaptogonia from the Himalayas with four DNA markers (Coleoptera, Tenebrionidae, Blaptini)

Abstract A new species of the genus Blaptogonia Medvedev, 1998, B. zhentanga sp. n. , is described from the southern Himalayas of China. Two fragments of mitochondrial protein-coding genes (COI, Cytb), one fragment of mitochondrial ribosomal RNA gene (16S), and one fragment of nuclear rRNA gene (28SD2) of the new species were obtained. A key to the known species of the genus is presented.
Hand Pose Estimation with CNN-RNN

Hand pose estimation plays an important role in human-computer interaction. The traditional way is to deal with a single frame image. We know that the gesture is continuous, so the adjacent frames must be highly correlated. Therefore, the input of model of this paper was changed from single frame image to multi-frame images in order to use the condition that the adjacent frames have relevance. So the structure of CNNRNN was used in this paper. We discussed the effect of using the RNN module in the model. Finally, we demonstrated that our approach significantly outperforms state-of-the-art techniques in the NYU dataset.
Effects of an Economic Approach for Test Case Selection and Reduction for a Large Industrial Project

Extensive testing in large projects can lead to tremendous test costs with superlinear growth over time. Researchers have proposed several techniques to tackle this problem. However, the practical effects of these techniques on the asymptotic behaviour of test costs growth in large industrial software projects remains poorly characterized. We introduce and analyse a fixed time budget for test executions for SAP HANA, a large industrial project. Our approach assigns a global fixed time budget to several components. Each component can only execute tests within its budget, which can change only by transfers from other components. This limits the number of test executions for each test run to a constant, thus reducing the asymptotic growth of test costs. Budget transfers and test optimizations adhere to balances between value and costs, thus creating an economic environment for test case selection and reduction. Specifically, this creates incentives to remove unnecessary tests and to optimize test execution times. For SAP HANA, our approach leads to effective test case selection and reduction, and reduces test execution times by 105 years in four months with a negligible effect on quality. The trade-off between runtime savings and failure detection is 1.83 years per failure.