Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Scaling on Parallel Servers With MapReduce Type Precedence Constraints

A multiple server setting is considered, where each server has tunable speed, and increasing the speed incurs an energy cost. Jobs arrive to a single queue, and each job has two types of sub-tasks, map and reduce, and a precedence constraint among them: any reduce task of a job can only be processed once all the map tasks of the job have been completed. In addition to the scheduling problem, i.e., which task to execute on which server, with tunable speed, an additional decision variable is the choice of speed for each server, so as to minimize a linear combination of the sum of the flow times of jobs/tasks and the total energy cost. The precedence constraints present new challenges for the speed scaling problem with multiple servers, namely that the number of tasks that can be executed at any time may be small but the total number of outstanding tasks might be quite large. We present simple speed scaling algorithms that are shown to have competitive ratios, that depend on the power cost function, and/or the ratio of the size of the largest task and the shortest reduce task, but not on the number of jobs, or the number of servers.
Retail store layout optimization for maximum product visibility

Abstract It is well-established that increased product visibility to shoppers leads to higher sales for retailers. In this study, we propose an optimization methodology which assigns product categories and subcategories to store locations and sublocations to maximize the overall visibility of products to shoppers. The methodology is hierarchically developed to meet strategic and tactical layout planning needs of brick-and-mortar retailers. Layouts in both levels of planning are optimized considering eligibility requirements and complete set of shopper paths, thus, they successfully capture the unique shopping behaviour of consumers in a store’s region. The resulting mathematical optimization problem is recognized as a special instance of the well-known Quadratic Assignment Problem, which is considered computationally as one of the hardest optimization problems. We adopt a linearization technique and demonstrate via a real-world numerical example that our linearized optimization models substantially improve the store layout, hence, can be used in practical applications as a vital decision support model for store layout planning.
A real-time distributed post-disaster restoration planning strategy for distribution networks

After disasters, distribution networks have to be restored by repair, reconfiguration, and power dispatch. During the restoration process, changes can occur in real time that deviate from the situations considered in pre-designed planning strategies. That may result in the pre-designed plan to become far from optimal or even unimplementable. This paper proposes a centralized-distributed bi-level optimization method to solve the real-time restoration planning problem. The first level determines integer variables related to routing of the crews and the status of the switches using a genetic algorithm (GA), while the second level determines the dispatch of active/reactive power by using distributed model predictive control (DMPC). A novel Aitken- DMPC solver is proposed to accelerate convergence and to make the method suitable for real-time decision making. A case study based on the IEEE 123-bus system is considered, and the acceleration performance of the proposed Aitken-DMPC solver is evaluated and compared with the standard DMPC method.
Variability of Artiﬁcial Neural Networks

What makes an artiﬁcial neural network easier to train and more likely to produce desirable solutions than other comparable networks? In this paper, we provide a new angle to study such issues under the setting of a ﬁxed number of model parameters which in general is the most dominant cost factor. We introduce a notion of variability and show that it correlates positively to the activation ratio and negatively to a phenomenon called Collapse to Constants (or C2C), which is closely related but not identical to the phenomenon commonly known as vanishing gradient. Experiments on a styled model problem empirically verify that variability is indeed a key performance indicator for fully connected neural networks. The insights gained from this variability study will help the design of new and eﬀective neural network architectures.
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics

Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, existing automatic metrics are observed to correlate poorly with human evaluation. The lack of standardized benchmark datasets makes it difficult to fully evaluate the capabilities of a metric and fairly compare different metrics. Therefore, we propose OpenMEVA, a benchmark for evaluating open-ended story generation metrics. OpenMEVA provides a comprehensive test suite to assess the capabilities of metrics, including (a) the correlation with human judgments, (b) the generalization to different model outputs and datasets, (c) the ability to judge story coherence, and (d) the robustness to perturbations. To this end, OpenMEVA includes both manually annotated stories and auto-constructed test examples. We evaluate existing metrics on OpenMEVA and observe that they have poor correlation with human judgments, fail to recognize discourse-level incoherence, and lack inferential knowledge (e.g., causal order between events), the generalization ability and robustness. Our study presents insights for developing NLG models and metrics in further research.
Charge Transfer and $d-d$ excitations in AgF$_{2}$.

Charge transfer (CT) insulators are the parent phase of a large group of today's unconventional high temperature superconductors. Here we study experimentally the interband excitations of the electronic structure of a CT insulator in the form of silver fluoride, AgF$_2$, which has been proposed as an excellent analogue of oxocuprates. Optical conductivity and resonant inelastic X-Ray scattering (RIXS) on AgF$_2$ polycrystalline sample show a close similarity with that being measured on undoped La$_2$CuO$_4$ while the former show a larger CT gap of approximately 3.4 eV, $d-d$ excitations are nearly at the same energy in both materials. DFT and exact computations in a small cluster show that AgF$_2$ is more covalent than cuprates despite having a larger fundamental gap. In addition, our computations show that AgF$_2$ is at the verge of a charge transfer instability. The overall resemblance of our data on AgF$_2$ to those published before on its cuprate equivalent La$_2$CuO$_4$ suggests that the underlying CT insulator physics is the same, while AgF$_2$ could also benefit from a proximity to a charge density wave phase as in BaBiO$_3$. Therefore, our work provides a compelling support to the future use of fluoroargentates for the growth of novel high-temperature superconductors.
Where are we in embedding spaces?

Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space should be considered. To fill this gap, This paper provides theoretical analysis and empirical results on when and where to use hyperbolic space and hyperbolic embeddings in recommender systems. Specifically, we answer the questions that which type of models and datasets are more suited for hyperbolic space, as well as which latent size to choose. We evaluate our answers by comparing the performance of Euclidean space and hyperbolic space on different latent space models in both general item recommendation domain and social recommendation domain, with 6 widely used datasets and different latent sizes. Additionally, we propose a new metric learning based recommendation method called SCML and its hyperbolic version HSCML. We evaluate our conclusions regarding hyperbolic space on SCML and show the state-of-the-art performance of hyperbolic space by comparing HSCML with other baseline methods.
Learning Language Specific Sub-network for Multilingual Machine Translation

Multilingual neural machine translation aims at learning a single translation model for multiple languages. These jointly trained models often suffer from performance degradationon rich-resource language pairs. We attribute this degeneration to parameter interference. In this paper, we propose LaSS to jointly train a single unified multilingual MT model. LaSS learns Language Specific Sub-network (LaSS) for each language pair to counter parameter interference. Comprehensive experiments on IWSLT and WMT datasets with various Transformer architectures show that LaSS obtains gains on 36 language pairs by up to 1.2 BLEU. Besides, LaSS shows its strong generalization performance at easy adaptation to new language pairs and zero-shot translation. LaSS boosts zero-shot translation with an average of 8.3 BLEU on 30 language pairs. Codes and trained models are available at https://github.com/NLP-Playground/LaSS.
Point estimation for adaptive trial designs II: Practical considerations and guidance

In adaptive clinical trials, the conventional end‐of‐trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two‐part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias‐reduced estimates are to be preferred.
GRB 180418A: A Possibly Short Gamma-Ray Burst with a Wide-angle Outflow in a Faint Host Galaxy

We present X-ray and multiband optical observations of the afterglow and host galaxy of GRB 180418A, discovered by Swift/BAT and Fermi/GBM. We present a reanalysis of the GBM and BAT data deriving durations of the prompt emission of T 90 ≈ 2.56 and 1.90 s, respectively. Modeling the Fermi/GBM catalog of 1405 bursts (2008–2014) in the hardness–T 90 plane, we obtain a probability of ≈60% that GRB 180418A is a short-hard burst. From a combination of Swift/XRT and Chandra observations, the X-ray afterglow is detected to ≈38.5 days after the burst and exhibits a single power-law decline with F X ∝ t −0.98. Late-time Gemini observations reveal a faint r ≈ 25.69 mag host galaxy at an angular offset of ≈0. ″16. At the likely redshift range of z ≈ 1–2.25, we find that the X-ray afterglow luminosity of GRB 180418A is intermediate between short and long gamma-ray bursts (GRBs) at all epochs during which there are contemporaneous data and that GRB 180418A lies closer to the E γ,peak–E γ,iso correlation for short GRBs. Modeling the multiwavelength afterglow with the standard synchrotron model, we derive the burst explosion properties and find a jet opening angle of θ j ≳ 9°–14°. If GRB 180418A is a short GRB that originated from a neutron star merger, it has one of the brightest and longest-lived afterglows along with an extremely faint host galaxy. If, instead, the event is a long GRB that originated from a massive star collapse, it has among the lowest-luminosity afterglows and lies in a peculiar space in terms of the hardness–T 90 and E γ,peak–E γ,iso planes.
Interactive Geometry Modification of High Performance Finite Element Simulations

In the context of high performance ﬁnite element analysis, the cost of iteratively modifying a computational domain via re-meshing and restarting the analysis becomes time prohibitive as the size of simulations increases. In this paper, we demonstrate a new interactive simulation pipeline targeting high performance ﬁnite element simulations where the computational domain is modiﬁable in situ , that is, while the simulation is ongoing. This pipeline is designed to be modular so that it may interface with any existing ﬁnite element simulation framework. A server-client architecture is employed to manage simulation mesh data existing on a high performance computing resource while user-prescribed freeform geometric modiﬁcations take place on a separate workstation. We employ existing in situ visualization techniques to rapidly inform the user of simulation progression, enabling computational steering. By expressing the simulation domain in a reduced fashion on the client application, this pipeline manages highly reﬁned ﬁnite element simulation domains on the server while maintaining good performance on the client application.
Evolution of Autopoiesis and Multicellularity in the Game of Life

Abstract Recently we introduced a model of symbiosis, Model-S, based on the evolution of seed patterns in Conway's Game of Life. In the model, the fitness of a seed pattern is measured by one-on-one competitions in the Immigration Game, a two-player variation of the Game of Life. Our previous article showed that Model-S can serve as a highly abstract, simplified model of biological life: (1) The initial seed pattern is analogous to a genome. (2) The changes as the game runs are analogous to the development of the phenome. (3) Tournament selection in Model-S is analogous to natural selection in biology. (4) The Immigration Game in Model-S is analogous to competition in biology. (5) The first three layers in Model-S are analogous to biological reproduction. (6) The fusion of seed patterns in Model-S is analogous to symbiosis. The current article takes this analogy two steps further: (7) Autopoietic structures in the Game of Life (still lifes, oscillators, and spaceships—collectively known as ashes) are analogous to cells in biology. (8) The seed patterns in the Game of Life give rise to multiple, diverse, cooperating autopoietic structures, analogous to multicellular biological life. We use the apgsearch software (Ash Pattern Generator Search), developed by Adam Goucher for the study of ashes, to analyze autopoiesis and multicellularity in Model-S. We find that the fitness of evolved seed patterns in Model-S is highly correlated with the diversity and quantity of multicellular autopoietic structures.
Commentary: Leveraging Edge Computing Technology for Digital Pathology

A reliable telepathology system requires responsiveness to transmit and view high‐resolution pathology images, often of large file size. Telepathology for clinical practice, therefore, requires low delays, high bandwidth, and fast image processing. Sacco et al. recently published an article entitled “On Edge Computing for Remote Pathology Consultations and Computations. ”[1] This paper explains the power of edge computing technology in the field of telepathology to augment live remote microscope sessions. This group from Saint Louis in the USA developed a telepathology system called LiveMicro that facilitates remote collaboration and digital image sharing in addition to remote computation on live microscopic images. Contrary to other available telepathology solutions, by employing edge computing, their system integrates an image processing algorithm together with high‐speed data transfer with a latency on the order of hundreds of milliseconds. [2] In addition to allowing remote control of microscopic images, this innovative system also provides benefits such as performing application‐specific image processing and speeding up image transmission time, unlike other traditional cloud‐based systems often plagued by bottlenecks from networks with high traffic.
Effect of a synbiotic on functional abdominal pain in childhood

Background: One of the common functional gastrointestinal disorders in children is functional abdominal pain (FAP). The aim of the present study was to determine whether the administration of a synbiotic composed of fructo-oligosaccharides (FOS) and the seven types of beneficial bacteria is useful in FAP of childhood. Methods: In this placebo-controlled, double-blind trial, 4-15-year-old children who met the Rome III criteria for FAP were randomly divided to receive either synbiotic or placebo twice daily for 4 weeks. Primary outcome was at least 50% reduction in the number of pain episodes, and secondary outcomes were a decline of at least two scales in the pain duration and intensity based on Wong-Baker scale. Response to therapy was decrease of pain frequency/intensity. Results: A total of 67 children completed the trial (35 with synbiotic). Response rate was higher with synbiotic than placebo after four weeks (53.1 vs 11.4%; p<0.001), and synbiotic had significant superiority to placebo to relieve the duration (4.56±9.12 vs12±18.59, min/day, P=0.04), frequency (0.31±0.53vs 1.17±0.7, episode/Wk., P<.001) and intensity (2.38±2.29 vs 5.49±1.83, p<0.001) of abdominal pain. Conclusion: Synbiotic compared to placebo significantly decreased the intensity, frequency and duration of FAP in children.
An Inflammation-Index Signature Predicts Prognosis of Patients with Intrahepatic Cholangiocarcinoma After Curative Resection

Background The prognosis of patients with intrahepatic cholangiocarcinoma (ICC) after resection is at great variance. We aimed to establish a novel prognostic nomogram in facilitating the risk stratification for these patients. Methods A total of 82 high-dimensional radiological and pathological data were analyzed by LASSO-penalized Cox regression analyses and the panels with the best predictive performance were selected. Specific nomograms were established based on the selected panels and were validated in both primary (n=292) and validation cohorts (n=107). The area under the receiver operating characteristic curve (AUC) and the concordance index (C-index) were used to compare the predictive ability of nomograms and other staging systems. Results The modified Glasgow Prognostic Score (mGPS) was identified as the prognostic factor for both overall survival (OS) and progression-free survival (PFS). The nomograms built on the prognostic factors showed powerful efficacy in survival prediction, with C-indexes of 0.800 (95% CI 0.767–0.833) and 0.752 (95% CI 0.718–0.786) for OS and PFS in the primary cohort, 0.659 (95% CI 0.586–0.732) and 0.638 (95% CI 0.571–0.705) for OS and PFS in the validation cohort, respectively. Compared with tumor-node-metastasis stage, Barcelona Clinic Liver Cancer staging score, Cancer of the Liver Italian Program score, and Okuda staging system, the nomograms had significantly higher values of AUC and C-indexes in survival prediction in the primary and validation cohorts. Conclusion Compared with currently used staging systems, the nomograms showed significantly higher efficacy in predicting survival of ICC patients after resection. The nomograms provide new versions of personalized management for these patients.
Multi-step rainfall forecasting using deep learning approach

Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecasting, where a separate time series model for each forecasting horizon is considered and forecasts are computed using observed data samples. Forecasting in this method is performed by proposing a deep learning approach, i.e, Temporal Deep Belief Network (DBN). The best model is selected from several baseline models on the basis of performance analysis metrics. The results suggest that the temporal DBN model outperforms the conventional Convolutional Neural Network (CNN) specifically on rainfall time series forecasting. According to our experimentation, a modified DBN with hidden layes (300-200-100-10) performs best with 4.59E−05, 0.0068 and 0.94 values of MSE, RMSE and R value respectively on testing samples. However, we found that training DBN is more exhaustive and computationally intensive than other deep learning architectures. Findings of this research can be further utilized as basis for the advance forecasting of other weather parameters with same climate conditions.
DNA methylome and transcriptome analysis established a model of four differentially methylated positions (DMPs) as a diagnostic marker in esophageal adenocarcinoma early detection

Background Esophageal carcinogenesis involves in alterations of DNA methylation and gene transcription. This study profiled genomic DNA methylome vs. gene expression using transcriptome data on esophageal adenocarcinoma (EAC) tissues from the online databases in order to identify methylation biomarkers in EAC early diagnosis. Materials and Methods The DNA methylome and transcriptome data were downloaded from the UCSC Xena, Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases and then bioinformatically analyzed for the differentially methylated positions (DMPs) vs. gene expression between EAC and normal tissues. The highly methylated DMPs vs. reduced gene expression in EAC were selected and then stratified with those of the corresponding normal blood samples and other common human cancers to construct an EAC-specific diagnostic model. The usefulness of this model was further verified in other three GEO datasets of EAC tissues. Result A total of 841 DMPs were associated with expression of 320 genes, some of which were aberrantly methylated in EAC tissues. Further analysis showed that four (cg07589773, cg10474350, cg13011388 and cg15208375 mapped to gene IKZF1, HOXA7, EFS and TSHZ3, respectively) of these 841 DMPs could form and establish a diagnostic model after stratified them with the corresponding normal blood samples and other common human cancers. The data were further validated in other three GEO datasets on EAC tissues in early EAC diagnosis. Conclusion This study revealed a diagnostic model of four genes methylation to diagnose EAC early. Further study will confirm the usefulness of this model in a prospective EAC cases.