nils-herrmann/clean_scirepeval huggingface dataset:
Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect.

Many molecularly targeted anticancer agents entering the definitive stage of clinical development benefit only a subset of treated patients. This may lead to missing effective agents by the traditional broad-eligibility randomized trials due to the dilution of the overall treatment effect. We propose a statistically rigorous biomarker-adaptive threshold phase III design for settings in which a putative biomarker to identify patients who are sensitive to the new agent is measured on a continuous or graded scale.



The design combines a test for overall treatment effect in all randomly assigned patients with the establishment and validation of a cut point for a prespecified biomarker of the sensitive subpopulation. The performance of the biomarker-adaptive design, relative to a traditional design that ignores the biomarker, was evaluated in a simulation study. The biomarker-adaptive design was also used to analyze data from a prostate cancer trial.



In the simulation study, the biomarker-adaptive design preserved the power to detect the overall effect when the new treatment is broadly effective. When the proportion of sensitive patients as identified by the biomarker is low, the proposed design provided a substantial improvement in efficiency compared with the traditional trial design. Recommendations for sample size planning and implementation of the biomarker-adaptive design are provided.



A statistically valid test for a biomarker-defined subset effect can be prospectively incorporated into a randomized phase III design without compromising the ability to detect an overall effect if the intervention is beneficial in a broad population.
Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year’s US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems.
Cloud Based Self Driving Cars

This paper presents a novel idea for reducing the data storage problems in the self-driving cars. Self-driving cars is a technology that is observed by the modern word with most curiosity. However the vulnerability with the car is the growing data and the approach for handling such huge amount of data growth. This paper proposes a cloud based self-driving car which can optimize the data storage problems in such cars. The idea is to not store any data in the car, rather download everything from the cloud as per the need of the travel. This allows the car to not keep a huge amount of data and rely on a cloud infrastructure for the drive.
Developing a unified framework of the business model concept

2 February 2009  8 September 2009 2nd  9 January 2010 3rd Revision: 2 March 2010  4 March 2010  Recent rapid advances in Information and Communication Technologies (ICTs) have highlighted the rising importance of the Business Model (BM) concept in the field of Information Systems (IS). Despite agreement on its importance to an organization’s success, the concept is still fuzzy and vague, and there is little consensus regarding its compositional facets. Identifying the fundamental concepts, modeling principles, practical functions, and reach of the BM relevant to IS and other business concepts is by no means complete. This paper, following a comprehensive review of the literature, principally employs the content analysis method and utilizes a deductive reasoning approach to provide a hierarchical taxonomy of the BM concepts from which to develop a more comprehensive framework. This framework comprises four fundamental aspects. First, it identifies four primary BM dimensions along with their constituent elements forming a complete ontological structure of the concept. Second, it cohesively organizes the BM modeling principles, that is, guidelines and features. Third, it explains the reach of the concept showing its interactions and intersections with strategy, business processes, and IS so as to place the BM within the world of digital business. Finally, the framework explores three major functions of BMs within digital organizations to shed light on the practical significance of the concept. Hence, this paper links the BM facets in a novel manner offering an intact definition. In doing so, this paper provides a unified conceptual framework for the BM concept that we argue is comprehensive and appropriate to the complex nature of businesses today. This leads to fruitful implications for theory and practice and also enables us to suggest a research agenda using our conceptual framework.  (2010) 19, 359–376. doi:10.1057/ejis.2010.21; published online 11 May 2010
Unsupervised Imitation Learning

We introduce a novel method to learn a policy from unsupervised demonstrations of a process. Given a model of the system and a set of sequences of outputs, we find a policy that has a comparable performance to the original policy, without requiring access to the inputs of these demonstrations. We do so by first estimating the inputs of the system from observed unsupervised demonstrations. Then, we learn a policy by applying vanilla supervised learning algorithms to the (estimated)input-output pairs. For the input estimation, we present a new adaptive linear estimator (AdaL-IE) that explicitly trades-off variance and bias in the estimation. As we show empirically, AdaL-IE produces estimates with lower error compared to the state-of-the-art input estimation method, (UMV-IE) [Gillijns and De Moor, 2007]. Using AdaL-IE in conjunction with imitation learning enables us to successfully learn control policies that consistently outperform those using UMV-IE.
The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.
An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation

In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via sentiment labels. Our proposed model is based on the paradigm of conditional adversarial learning; the training of a sentiment-controlled dialogue generator is assisted by an adversarial discriminator which assesses the fluency and feasibility of the response generating from the dialogue history and a given sentiment label. Because of the flexibility of our framework, the generator could be a standard sequence-to-sequence (SEQ2SEQ) model or a more complicated one such as a conditional variational autoencoder-based SEQ2SEQ model. Experimental results using automatic and human evaluation both demonstrate that our proposed framework is able to generate both semantically reasonable and sentiment-controlled dialogue responses.
Do bisphosphonates affect bone healing? A meta-analysis of randomized controlled trials

Whether bisphosphonates affect indirect bone healing is still unclear.



We carried out a comprehensive search strategy. Only randomized controlled trials were included. Two reviewers independently assessed methodological qualities and extracted outcome data. Analysis was performed with RevMan 5.2.



Eight eligible randomized controlled trials with 2,508 patients were included. Meta-analysis results showed that no statistically significant differences were founded in indirect bone healing in short time (within 3 months) (relative risk (RR) 1.40, relative the control group; 95% CI 0.36 to 5.49) and in long-term (more than 12 months) postoperation (RR 1.0; 95% CI 0.98 to 1.02) between bisphosphonates infusion groups and control groups. There were no statistically significant differences of indirect bone healing between early and delay bisphosphonates administration groups. Bisphosphonates infusion after lumbar infusion surgery could promote bone healing and shorten fusion time in 6 months postoperation (RR 1.35; 95% CI 1.11 to 1.66).



There was no clinically detectable delay to fracture healing via external callus formation following bisphosphonates treatment. Considering the benefit aspects of bisphosphonates for osteoporosis treatment, we recommend bisphosphonates infusion after fracture fixation surgery and lumbar fusion surgery.
Link Prediction via Subgraph Embedding-Based Convex Matrix Completion

Link prediction is of fundamental importance in network science and machine learning. Early methods consider only simple topological features, while subsequent supervised approaches typically rely on human-labeled data and feature engineering. In this work, we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology. In contrast to the SGNS or SVD methods espoused in previous representation-based studies, our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank, and thereby, more effectively eliminate noise in the representation. Thus, subgraph embeddings and convex matrix completion are elegantly integrated into a novel link prediction framework. Experimental results on several datasets show the effectiveness of our method compared to previous work.
Nachdiplom Lecture: Statistics Meets Optimization Lecture 2 – Tuesday, September 29 2.1 a General Theorem on Gaussian Random Projections

Let K be a subset of the Euclidean sphere S d−1. As seen in Lecture #1, in analyzing how well a given random projection matrix S ∈ R m×d preserves vectors in K, a central object is the random variable Z(K) = sup u∈K Su 2 2 m − 1. (2.1) Suppose that our goal is to establish that, for some δ ∈ (0, 1), we have Z(K) ≤ δ with high probability. How large must the projection dimension m be, as a function of (δ, K), for this type of inequality to hold? In this lecture, we give a precise answer for Gaussian random projections.
Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem

In retailer management, the Newsvendor problem has widely attracted attention as one of basic inventory models. In the traditional approach to solving this problem, it relies on the probability distribution of the demand. In theory, if the probability distribution is known, the problem can be considered as fully solved. However, in any real world scenario, it is almost impossible to even approximate or estimate a better probability distribution for the demand. In recent years, researchers start adopting machine learning approach to learn a demand prediction model by using other feature information. In this paper, we propose a supervised learning that optimizes the demand quantities for products based on feature information. We demonstrate that the original Newsvendor loss function as the training objective outperforms the recently suggested quadratic loss function. The new algorithm has been assessed on both the synthetic data and real-world data, demonstrating better performance.
Effects of DHA on Hippocampal Autophagy and Lysosome Function After Traumatic Brain Injury

Traumatic brain injury (TBI) triggers endoplasmic reticulum (ER) stress and impairs autophagic clearance of damaged organelles and toxic macromolecules. In this study, we investigated the effects of the post-TBI administration of docosahexaenoic acid (DHA) on improving hippocampal autophagy flux and cognitive functions of rats. TBI was induced by cortical contusion injury in Sprague–Dawley rats, which received DHA (16 mg/kg in DMSO, intraperitoneal administration) or vehicle DMSO (1 ml/kg) with an initial dose within 15 min after the injury, followed by a daily dose for 3 or 7 days. First, RT-qPCR reveals that TBI induced a significant elevation in expression of autophagy-related genes in the hippocampus, including SQSTM1/p62 (sequestosome 1), lysosomal-associated membrane proteins 1 and 2 (Lamp1 and Lamp2), and cathepsin D (Ctsd). Upregulation of the corresponding autophagy-related proteins was detected by immunoblotting and immunostaining. In contrast, the DHA-treated rats did not exhibit the TBI-induced autophagy biogenesis and showed restored CTSD protein expression and activity. T2-weighted images and diffusion tensor imaging (DTI) of ex vivo brains showed that DHA reduced both gray matter and white matter damages in cortical and hippocampal tissues. DHA-treated animals performed better than the vehicle control group on the Morris water maze test. Taken together, these findings suggest that TBI triggers sustained stimulation of autophagy biogenesis, autophagy flux, and lysosomal functions in the hippocampus. Swift post-injury DHA administration restores hippocampal lysosomal biogenesis and function, demonstrating its therapeutic potential.
meets Optimization Fall 2015 Lecture 7 –

In previous lectures, we have analyzed random forms of optimization problems, in which the randomness was injected (via random projection) for algorithmic reasons. On the other hand, in statistical problems—even without considering approximations—the starting point is a random instance of an optimization problem. To be more concrete, suppose that we are interested in estimating some parameter θ * ∈ R d based on a set of n samples, say {Z 1 ,. .. , Z n }. Many estimators of θ * are based on solving the (random) optimization problem θ ∈ argmin θ∈C L n (θ), where C ⊂ R d is some subset of R d , and L n (θ) = 1 n n i=1 i (θ; Z i) decomposes as a sum of terms, one for each data point. Our interest will be in analyzing the sequence {θ t } ∞ t=0 generated by some optimization algorithm. A traditional analysis in (deterministic) optimization involves bounding the optimization error θ t − θ, measuring the distance between the iterates and (some) optimum. On the other hand, the population version of this problem is defined in terms of the averaged function ¯ L(θ) : = E[L n (θ)]. If the original problem has been constructed in a reasonable way, then it should be the case that θ * ∈ arg min θ∈C ¯ L(θ), meaning that the quantity of interest is a global minimizer of the population function.
On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include huge state space, varying action space, long horizon, etc. In this paper, we investigate a set of techniques of reinforcement learning for the full-length game of StarCraft II. We investigate a hierarchical approach, where the hierarchy involves two levels of abstraction. One is the macro-actions extracted from expert’s demonstration trajectories, which can reduce the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture, which is modular and easy to scale. We also investigate a curriculum transfer learning approach that trains the agent from the simplest opponent to harder ones. On a 64×64 map and using restrictive units, we train the agent on a single machine with 4 GPUs and 48 CPU threads. We achieve a winning rate of more than 99% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93% winning rate against the most difficult non-cheating built-in AI (level-7) within days. We hope this study could shed some light on the future research of large-scale reinforcement learning.
Automatically Reinforcing a Game AI

A recent research trend in Artificial Intelligence (AI) is the combination of several programs into one single, stronger, program; this is termed portfolio methods. We here investigate the application of such methods to Game Playing Programs (GPPs). In addition, we consider the case in which only one GPP is available by decomposing this single GPP into several ones through the use of parameters or even simply random seeds. These portfolio methods are trained in a learning phase. We propose two different offline approaches. The simplest one, BestArm, is a straightforward optimization of seeds or parameters; it performs quite well against the original GPP, but performs poorly against an opponent which repeats games and learns. The second one, namely Nash-portfolio, performs similarly in a “one game” test, and is much more robust against an opponent who learns. We also propose an online learning portfolio, which tests several of the GPP repeatedly and progressively switches to the best one using a bandit algorithm.
Evaluating exploitation strategy for technological innovation: cost or compatibility?

Innovation becomes an important asset for firms to compete in today's business environments. It is essential for firms to choose method of exploitation wisely in order to exercise proprietary invention to its utmost potential. Some of the past literatures suggested cost perspective to evaluate exploitation strategy while its validity has been questioned by scholars who believe in contradictory theories. This research is dedicated to explore determinants of exploitation strategy. It takes qualitative approach by in-depth interviewing five case studies of innovative products from multiple industries. The finding revealed that firms may not factor in cost perspective even though the main objective is to gain big profit. In contrary the results indicated that firms indeed pay attention on factors which lead to evaluation of compatibility between the invention and commercialization environment. Additionally, the finding suggested that exploitation strategy is a dynamic process and shall be reevaluated throughout life cycle of invention especially when the commercialization environment has changed.
A Literature Survey on Ontology of Different Computing Platforms in Smart Environments

Smart environments integrates various types of technologies, including cloud computing, fog computing, and the IoT paradigm. In such environments, it is essential to organize and manage efficiently the broad and complex set of heterogeneous resources. For this reason, resources classification and categorization becomes a vital issue in the control system. In this paper we make an exhaustive literature survey about the various computing systems and architectures which defines any type of ontology in the context of smart environments, considering both, authors that explicitly propose resources categorization and authors that implicitly propose some resources classification as part of their system architecture. As part of this research survey, we have built a table that summarizes all research works considered, and which provides a compact and graphical snapshot of the current classification trends. The goal and primary motivation of this literature survey has been to understand the current state of the art and identify the gaps between the different computing paradigms involved in smart environment scenarios. As a result, we have found that it is essential to consider together several computing paradigms and technologies, and that there is not, yet, any research work that integrates a merged resources classification, taxonomy or ontology required in such heterogeneous scenarios.
Hierarchical Multilabel Classification with Minimum Bayes Risk

Hierarchical multilabel classification (HMC) allows an instance to have multiple labels residing in a hierarchy. A popular loss function used in HMC is the H-loss, which penalizes only the first classification mistake along each prediction path. However, the H-loss metric can only be used on tree-structured label hierarchies, but not on DAG hierarchies. Moreover, it may lead to misleading predictions as not all misclassifications in the hierarchy are penalized. In this paper, we overcome these deficiencies by proposing a hierarchy-aware loss function that is more appropriate for HMC. Using Bayesian decision theory, we then develop a Bayes-optimal classifier with respect to this loss function. Instead of requiring an exhaustive summation and search for the optimal multilabel, the proposed classification problem can be efficiently solved using a greedy algorithm on both tree-and DAG-structured label hierarchies. Experimental results on a large number of real-world data sets show that the proposed algorithm outperforms existing HMC methods.
Chinese Grammatical Error Diagnosis with Long Short-Term Memory Networks

Grammatical error diagnosis is an important task in natural language processing. This paper introduces our Chinese Grammatical Error Diagnosis (CGED) system in the NLP-TEA-3 shared task for CGED. The CGED system can diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the CGED task as a sequence labeling task and describe three models, including a CRFbased model, an LSTM-based model and an ensemble model using stacking. We also show in details how we build and train the models. Evaluation includes three levels, which are detection level, identification level and position level. On the CGED-HSK dataset of NLP-TEA-3 shared task, our system presents the best F1-scores in all the three levels and also the best recall in the last two levels.
Efficient Inferencing of Compressed Deep Neural Networks

Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as “inferencing as a service” environments on cloud. Prior work has considered reduction in the size of the models, through compression techniques like pruning, quantization, Huffman encoding etc. However, efficient inferencing using the compressed models has received little attention, specially with the Huffman encoding in place. In this paper, we propose efficient parallel algorithms for inferencing of single image and batches, under various memory constraints. Our experimental results show that our approach of using variable batch size for inferencing achieves 15-25% performance improvement in the inference throughput for AlexNet, while maintaining memory and latency constraints.