nils-herrmann/clean_scirepeval huggingface dataset:
Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.
A multi-agent Q-Learning-based framework for achieving fairness in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for Over-The-Top video streaming. In HAS, each video is temporally segmented and stored in different quality levels. Quality selection heuristics, deployed at the video player, allow dynamically requesting the most appropriate quality level based on the current network conditions. Today's heuristics are deterministic and static, and thus not able to perform well under highly dynamic network conditions. Moreover, in a multi-client scenario, issues concerning fairness among clients arise, meaning that different clients negatively influence each other as they compete for the same bandwidth. In this article, we propose a Reinforcement Learning-based quality selection algorithm able to achieve fairness in a multi-client setting. A key element of this approach is a coordination proxy in charge of facilitating the coordination among clients. The strength of this approach is three-fold. First, the algorithm is able to learn and adapt its policy depending on network conditions, unlike current HAS heuristics. Second, fairness is achieved without explicit communication among agents and thus no significant overhead is introduced into the network. Third, no modifications to the standard HAS architecture are required. By evaluating this novel approach through simulations, under mutable network conditions and in several multi-client scenarios, we are able to show how the proposed approach can improve system fairness up to 60% compared to current HAS heuristics.
γ-Aminobutyric Acid Imparts Partial Protection from Salt Stress Injury to Maize Seedlings by Improving Photosynthesis and Upregulating Osmoprotectants and Antioxidants

γ-Aminobutyric acid (GABA) has high physiological activity in plant stress physiology. This study showed that the application of exogenous GABA by root drenching to moderately (MS, 150 mM salt concentration) and severely salt-stressed (SS, 300 mM salt concentration) plants significantly increased endogenous GABA concentration and improved maize seedling growth but decreased glutamate decarboxylase (GAD) activity compared with non-treated ones. Exogenous GABA alleviated damage to membranes, increased in proline and soluble sugar content in leaves, and reduced water loss. After the application of GABA, maize seedling leaves suffered less oxidative damage in terms of superoxide anion (O2·-) and malondialdehyde (MDA) content. GABA-treated MS and SS maize seedlings showed increased enzymatic antioxidant activity compared with that of untreated controls, and GABA-treated MS maize seedlings had a greater increase in enzymatic antioxidant activity than SS maize seedlings. Salt stress severely damaged cell function and inhibited photosynthesis, especially in SS maize seedlings. Exogenous GABA application could reduce the accumulation of harmful substances, help maintain cell morphology, and improve the function of cells during salt stress. These effects could reduce the damage to the photosynthetic system from salt stress and improve photosynthesis and chlorophyll fluorescence parameters. GABA enhanced the salt tolerance of maize seedlings.
Nicotine-Induced Changes in Neurotransmitter Levels in Brain Areas Associated with Cognitive Function

Nicotine, one of the most widespread drugs of abuse, has long been shown to impact areas of the brain involved in addiction and reward. Recent research, however, has begun to explore the positive effects that nicotine may have on learning and memory. The mechanisms by which nicotine interacts with areas of cognitive function are relatively unknown. Therefore, this paper is part of an ongoing study to evaluate regional effects of nicotine enhancement of cognitive function. Nicotine-induced changes in the levels of three neurotransmitters, dopamine (DA), serotonin (5-HT), norepinepherine (NE), their metabolites, homovanillic acid (HVA), dihydroxyphenylacetic acid (DOPAC), 5-hydroxyindoleacetic acid (5-HIAA), and their precursor, l-DOPA, were evaluated in the ventral and dorsal hippocampus (VH and DH), prefrontal and medial temporal cortex (PFC and MTC), and the ventral tegmental area (VTA) using in vivo microdialysis in awake, freely moving, male Sprague-Dawley rats. The animals were treated with acute nicotine (0.5 mg/kg, s.c.) halfway through the 300-min experimental period. The reuptake blockers, desipramine (100 μM) and fluoxetine (30 μM), were given to increase the levels of NE and 5-HT so that they could be detected. Overall, a nicotine-induced DA increase was found in some areas, and this increase was potentiated by desipramine and fluoxetine. The two DA metabolites, HVA and DOPAC, increased in all the areas throughout the experiments, both with and without the inhibitors, indicating a rapid metabolism of the released DA. The increase in these metabolites was greater than the increase in DA. 5-HT was increased in the DH, MTC, and VTA in the presence of fluoxetine; its metabolite, 5-HIAA, was increased in the presence and absence of fluoxetine. Except in the VTA, NE levels increased to a similar extent with desipramine and fluoxetine. Overall, nicotine appeared to increase the release and turnover of these three neurotransmitters, which was indicated by significant increases in their metabolites. Furthermore, DA, and especially HVA and DOPAC, increased for the 150 min following nicotine administration; 5-HT and NE changes were shorter in duration. As gas chromatography experiments showed that nicotine levels in the brain decreased by 75% after 150 min, this may indicate that DA is more susceptible to lower levels of nicotine than 5-HT or NE. In conclusion, acute nicotine administration caused alterations in the levels of DA, 5-HT, and NE, and in the metabolism of DA and 5-HT, in brain areas that are involved in cognitive processes.
Show , Attend , Control , and Justify : Interpretable Learning for Self-Driving Cars

Deep neural perception and control networks have become key components of selfdriving vehicles. User acceptance is likely to benefit from easy-to-interpret visual and textual driving rationales which allow end-users to understand what triggered a particular behavior. Our approach involves two stages. In the first stage, we use visual (spatial) attention model to train a convolutional network end-to-end from images to steering angle commands. The attention model identifies image regions that potentially influence the network’s output. We then apply a causal filtering step to determine which input regions causally influence the vehicle’s control signal. In the second stage, we use a video-to-text language model to produce textual rationales that justify the model’s decision. The explanation generator uses a spatiotemporal attention mechanism, which is encouraged to match the controller’s attention.
An OCR System with OCRopus for Scientific Documents Containing Mathematical Formulas

This paper describes the installation of a mathematical formula recognition module into an open source OCR system: OCRopus. In particular we consider the identification of inline formulas utilizing existing modules. Text lines including math formulas are first processed using a N-gram language model to reduce the number of formula candidates by thresholding the conditional probability of words. Then the formula candidates are classified into formulas and texts by SVM using geometric features associated with the bounding boxes of symbols.
Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.
Requirements elicitation: Issues and challenges

There is no doubt to say that researchers have made significant contributions by developing numerous tools and techniques of various Requirements Engineering (RE) processes but at the same time, the field still demands further research to come up with the novel solutions for many ongoing issues. Some of the key challenges in RE may be the issues in describing the system limit, issues in understanding among the different groups affected by the improvement of a given system, and challenges in dealing with the explosive nature of requirements. These challenges may lead to poor requirements and the termination of system progress, or else the disappointing or inefficient result of a system, which increases high maintenance costs or suffers from frequent changes. RE can be decomposed into various sub-phases: requirements elicitation, specification, documentation and validation. Through proper requirements elicitation, RE process can be upgraded, resulting in enriched system requirements and possibly a much better system. Keeping in view the importance of the area, major elicitation techniques have already been identified in one of our previous papers. This paper is an extension of our previous work and here, an attempt is made to identify and describe the recurring issues and challenges in various requirements elicitation techniques.
Assessing optimal assignment under uncertainty: An interval-based algorithm

We consider the problem of multi-robot taskallocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure of the uncertainty (e.g., a probability distribution, or moments of such distributions). We introduce a new O(n) algorithm, the Interval Hungarian algorithm, that extends the classic KuhnMunkres Hungarian algorithm to compute the maximum interval of deviation (for each entry in the assignment matrix) which will retain the same optimal assignment. This provides an efficient measurement of the tolerance of the allocation to the uncertainties, for both a specific interval and a set of interrelated intervals. We conduct experiments both in simulation and with physical robots to validate the approach and to gain insight into the effect of location uncertainty on allocations for multi-robot multi-target navigation tasks.
Neurological findings in complex regional pain syndromes--analysis of 145 cases.

Early diagnosis is a prerequisite for a successful treatment of complex regional pain syndrome (CRPS). In order to describe neurological symptoms which characterize CRPS, we evaluated 145 patients prospectively. Two-thirds of these were women, the mean age at time of investigation was 50.4 years. CRPS followed limb trauma, surgery and nerve lesion. Employing the current IASP criteria 122 patients were classified as CRPS I and 23 as CRPS II. All patients were assessed clinically pain was quantified using the McGill pain questionnaire, skin temperature was measured by an infrared thermometer and a subgroup of 57 patients was retested in order to determine thermal thresholds (QST). Of our patients 42% reported stressful life events in a close relationship to the onset of CRPS and 41% had a history of chronic pain before CRPS. The latter group of patients gave a higher rating of CRPS pain (P<0.05). The major symptoms were pain at rest in 77% and hyperalgesia in 94%. Typical pain was deep in the limb having a tearing character. Patients getting physical therapy had significantly less pain than those without (P<0.04). Autonomic signs were frequent (98%) and often changed with the duration of CRPS. Skin temperature was warmer in acute and colder in chronic stages (P<0.001). Likewise edema had a higher incidence in acute stages (P<0.001). We found no correlation between pain and autonomic dysfunction. Motor dysfunction (present in 97%) included weakness, tremor, exaggerated tendon reflexes, dystonia or myoclonic jerks. QST revealed increased warm perception thresholds (P<0.02) and decreased cold pain thresholds (P<0.03) of the affected limb. The detailed knowledge of clinical features of CRPS could help physicians early to recognize the disease and thus to improve therapy outcome.
Spin-Transfer Torque Memories: Devices, Circuits, and Systems

Spin-transfer torque magnetic memory (STT-MRAM) has gained significant research interest due to its nonvolatility and zero standby leakage, near unlimited endurance, excellent integration density, acceptable read and write performance, and compatibility with CMOS process technology. However, several obstacles need to be overcome for STT-MRAM to become the universal memory technology. This paper first reviews the fundamentals of STT-MRAM and discusses key experimental breakthroughs. The state of the art in STT-MRAM is then discussed, beginning with the device design concepts and challenges. The corresponding bit-cell design solutions are also presented, followed by the STT-MRAM cache architectures suitable for on-chip applications.
Economic growth and subjective well-being : reassessing the Easterlin paradox

The “Easterlin paradox” suggests that there is no link between a society’s economic development and its average level of happiness. We re-assess this paradox analyzing multiple rich datasets spanning many decades. Using recent data on a broader array of countries, we establish a clear positive link between average levels of subjective well-being and GDP per capita across countries, and find no evidence of a satiation point beyond which wealthier countries have no further increases in subjective well-being. We show that the estimated relationship is consistent across many datasets and is similar to the relationship between subject well-being and income observed within countries. Finally, examining the relationship between changes in subjective well-being and income over time within countries we find economic growth associated with rising happiness. Together these findings indicate a clear role for absolute income and a more limited role for relative income comparisons in determining happiness.
Poisson image editing

Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection.
Improving Adversarial Discriminative Domain Adaptation

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. While ADDA has already achieved better training efficiency and competitive accuracy in comparison to other adversarial based methods, we investigate whether we can improve performance by incorporating task knowledge into the adversarial loss functions. We achieve this by extending the discriminator output over the source classes and leverage on the distribution over the source encoder posteriors, which is fixed during adversarial training, in order to align a shared encoder distribution to the source domain. The shared encoder can receive a proportion of examples from both the source and target datasets, in order to smooth the learned distribution and improve its convergence properties during adversarial training. We additionally consider how the extended discriminator can be regularized in order to further improve performance, by treating the discriminator as a denoising autoencoder and corrupting its input. Our final design employs maximum mean discrepancy and reconstruction-based loss functions for adversarial training. We validate our framework on standard datasets like MNIST, USPS, SVHN, MNIST-M and Office31. Our results on all datasets show that our proposal is both simple and efficient, as it competes or outperforms the state-of-the-art in unsupervised domain adaptation, whilst offering lower complexity than other recent adversarial methods such as DIFA and CoGAN.
The Evolution of Computing: AlphaGo

G oogle’s AlphaGo program made headlines recently when it played a game of Go with a top human player and won. We’re conditioned to like challenges until we face a formidable opponent, but what happens when we face a computer? I learned to play Go when I was in elementary school. It’s the simplest board game concept, yet it’s the most difficult to play. I quickly gave up because it can take hours to finish just one game, and it takes seemingly forever to improve your skills. It’s worth learning the game, but I wouldn’t recommend it to all youngsters.  1 shows the Go board—on the 19 × 19 array, two players (black and white) take turns placing a stone of their own color on an empty grid point. Adjacent stones of the same color along the vertical and horizontal grid lines form an area. When an area is surrounded by an opponent’s stones, it’s “alive” if it has at least one empty point inside the area; otherwise, the stones in the area are captured and removed from the board. However, if the surrounded area has only one empty point left, it’s captured because an opponent can place a stone in that point and solidly surround it. Therefore, an area is only really alive if it has two separate empty points inside—for example, as Figure 2 shows, if black
How to Use Bitcoin to Incentivize Correct Computations

We study a model of incentivizing correct computations in a variety of cryptographic tasks. For each of these tasks we propose a formal model and design protocols satisfying our model's constraints in a hybrid model where parties have access to special ideal functionalities that enable monetary transactions. We summarize our results: Verifiable computation. We consider a setting where a delegator outsources computation to a worker who expects to get paid in return for delivering correct outputs. We design protocols that compile both public and private verification schemes to support incentivizations described above. Secure computation with restricted leakage. Building on the recent work of Huang et al. (Security and Privacy 2012), we show an efficient secure computation protocol that monetarily penalizes an adversary that attempts to learn one bit of information but gets detected in the process.  Fair secure computation. Inspired by recent work, we consider a model of secure computation where a party that aborts after learning the output is monetarily penalized. We then propose an ideal transaction functionality FML and show a constant-round realization on the Bitcoin network. Then, in the FML-hybrid world we design a constant round protocol for secure computation in this model.  Noninteractive bounties. We provide formal definitions and candidate realizations of noninteractive bounty mechanisms on the Bitcoin network which (1) allow a bounty maker to place a bounty for the solution of a hard problem by sending a single message, and (2) allow a bounty collector (unknown at the time of bounty creation) with the solution to claim the bounty, while (3) ensuring that the bounty maker can learn the solution whenever its bounty is collected, and (4) preventing malicious eavesdropping parties from both claiming the bounty as well as learning the solution.
 All our protocol realizations (except those realizing fair secure computation) rely on a special ideal functionality that is not currently supported in Bitcoin due to limitations imposed on Bitcoin scripts. Motivated by this, we propose validation complexity of a protocol, a formal complexity measure that captures the amount of computational effort required to validate Bitcoin transactions required to implement it in Bitcoin. Our protocols are also designed to take advantage of optimistic scenarios where participating parties behave honestly.
A Relational Model of Data for Large Shared Data Banks

Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at terminals and most application programs should remain unaffected when the internal representation of data is changed and even when some aspects of the external representation are changed. Changes in data representation will often be needed as a result of changes in query, update, and report traffic and natural growth in the types of stored information.
Existing noninferential, formatted data systems provide users with tree-structured files or slightly more general network models of the data. In Section 1, inadequacies of these models are discussed. A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced. In Section 2, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model.