bluuebunny/crossref_metadata_2025 huggingface dataset:
Mimesis of Remembering
Authors: Basseler Michael , Birke Dorothee
Published: September 2022

Literary narratives not only often thematize memory as a topic; they also directly represent or stage concrete processes of remembering by way of various narrative techniques. This article offers a systematic approach to these techniques which is informed both by narratology and interdisciplinary memory studies. Specifically, the contribution offers a toolbox for the analysis of what we refer to as the ›mimesis of remembering‹: through a variety of textual strategies, literary texts can create ›memory-like‹ effects. How such ›mnestic narration‹ is achieved and what functions it might fulfil is the main concern of this article.

Most generally, we argue, two basic structural principles are the basis for a narrative mimesis of remembering: first, such narratives feature a centre of subjective perception, a consciousness who performs the process of remembering (either on the level of the narrative mediation or the level of the characters), and second, they need to feature at least two distinct time levels. However, not all narratives that contain these very common aspects are equally invested in representing processes of remembering. We propose to think of the mnestic quality of texts as a scalar phenomenon, where passages set in the narrative past can be more or less emphatically (and continuously) marked as rendering products or processes of remembering.

Besides introducing various basic aspects of a mimesis of remembering – representation of time and space, narrative mediation and focalization, and questions of narrative unreliability –, the article not only offers a toolbox for analysis, but also discusses, on the basis of selected texts, how these aspects can be designed and combined in ways that serve to highlight a text’s mnestic qualities. We come to the conclusion that in order to fully understand these effects, one must set them into broader cultural and historical contexts. For one thing, it needs to be considered how the representations in the texts relate to evolving conceptualizations of the process of remembering itself. Moreover, one must be aware of changing narrative conventions for the representations of ›normal‹ or unmarked acts of remembering, which may also serve as a foil to foreground unusual instances.
Traffic Sign Classification Using Transfer Learning: An Investigation of Feature-Combining Model
Authors: Lim Wee Sheng , Ab. Nasir Ahmad Fakhri , Mohd Razman Mohd Azraai , P. P. Abdul Majeed Anwar , Kamarudin Nur Shazwani , Toh M. Zulfahmi
Published: September 2022

The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS systems
Design of a Fuzzy Logic Controller for Optimal African Catfish Water Production
Authors: Alim S. A. , Sumaila M. , Ritkangnga I. Y.
Published: September 2022

Fish, unlike other animals, feed and defecate inside the same water where they live. When water quality depreciates, consumed feed is not properly converted into body flesh, poor growth is recorded, fish survival is affected and ultimately massive fish deaths may occur. In fish production, key water quality parameters which need to be continually monitored and controlled are temperature, dissolved oxygen (DO), pH, and ammonia. These parameters are highly non-linear, and thus difficult and expensive to use conventional controllers. Fuzzy logic controllers can suitably adapt to non-linearity because it uses sentences for control actions rather than equations. FisPro (Fuzzy Inference System Professional) is used to create fuzzy inference system (FIS)/ fuzzy logic controller (FLC) for simulating physical or biological systems. The selected water quality parameter are temperature, 14 to 45oC, potency of hydrogen (pH), 0 to 14, and turbidity, 1 to 5. At a pH of 3.5 and 39oC, the aerator speed is 6982 rpm. Similarly, at a pH of 3.5 and turbidity of 1.5, the valve position is 47o. The results obtained shows the completeness, consistency and continuity of the designed rule system. Nine rules were designed for each control action giving a total of eighteen rules. The control actions are; water discharge/refill in form of valve actuation and aeration in form of air pump activation.
Guidelines for Perinatal Care
Authors: Practice AAP Comm. on Fetus and Newborn; ACOG Comm. on Obstetric
Published: September 2022

Significantly revised and updated, the new 8th edition of this bestselling manual provides the latest recommendations on quality care of pregnant women, their fetuses, and their newborn infants. Available for purchase at https://www.aap.org/Guidelines-for-Perinatal-Care-8th-edition-Paperback?
Understanding the mechanics and consumer risks associated with play-to-earn (P2E) gaming
Authors: Delfabbro Paul , Delic Amelia , King Daniel L.
Published: September 2022

Background and aims

Play-to-earn (P2E) gaming is a newly emerging form of gaming increasingly based on blockchain technology. In this paper, we examine the mechanics and business model of these games and their potential benefits and risks for players.

Methods

The paper draws upon and critically synthesises the developing published literature on predatory monetization in gaming as well as objective market data drawn from credible online sources.

Results

P2E gaming blurs the boundaries between gaming and trading and may not yield many of the benefits promoted to consumers or otherwise conveyed through marketing and social media messaging. Particular risks include the deflationary nature of reward currencies and the asymmetric reward structures that heavily favour early investors and exploit late adopters.

Discussion and conclusions

This paper highlights the need for greater consumer awareness of the mechanics and risks of these new gaming models. It will be important for business models to be more transparent and designed so as to encourage more equitable game outcomes, sustainable returns, a balance between intrinsic and extrinsic rewards, and protection for potentially vulnerable players.
Tactics of forecasting of obstetric and perinatal complications at the primapara
Authors:   Shupyk National Medical Academy of Postgraduate Education, Kiev, Markevich V.V.
Published: September 2022

The objective: to develop and introduce tactics of forecasting of obstetric and perinatal complications at the primapara. Materials and methods. The material, allowing to construct classifications for creation of prognostic screening programs of obstetric and perinatal complications at primapara, were the courses of pregnancy given clinical-statistical research, labors, a condition of a fetus and the newborn at 400 primapara. The method of the step-by-step discriminant analysis for which carrying out actual data of clinical-statistical research about prevalence of gestational, patrimonial and perinatal complications at the primapara in interrelation with complex of studied factors are used is the basis for forecasting of possible complications. Results. A method of the step-by-step discriminant analysis were selected the most significant criteria of the forecast (parity; body height; age; age of menarche of the woman; duration of gestation at debut of placental dysfunction; an average assessment on Fisher; a mark assessment of detseleration on a cardiotocogram; concentration - -fetoprotein, chorionic Gonadotropinum, placental lactogen, a theelol, Progesteronum at the end of the III trimester of pregnancy; the systolodiastolic relation in 36-40 weeks; an assessment of the newborn on a scale Apgar on the first and fifth minutes of life of risk of realization and development of obstetric and perinatal complications in the primapara. Discriminant functions which with probability from 59.6 to 92.88% allow to define risk of realization of placental dysfunction, developments of anomalies of patrimonial activity and fetus distress at first labor are constructed. Conclusion. The results received by us allow to recommend the developed algorithm for introduction in practical health care. Key words: pregnancy, labors, complications, forecasting.
Oxidative stress markers in pregnant women with fetus growth inhibition and their influence on results of labour process
Authors:   Zaporozhye State Medical University, Syusyuka V.G. , Kolokot N.G. ,   Zaporozhye State Medical University, Belenichev I.F. ,   Zaporozhye State Medical University
Published: September 2022

The objective: estimate the oxidative stress markers and their influence on result of labour process of pregnant women with fetus growth inhibition. Materials and methods. The complex examination of 63 pregnant women was made in term of 28–34 weeks of gestation and in dynamics (examination in 3–4 weeks). І group includes 33 pregnant women with fetus growth inhibition. Group ІІ was presented by 30 pregnant women without fetus growth inhibition. Markers of oxidative modification of proteins were analyzed in blood serum by means of spectrophotometric method and glutathione level was analyzed by fluorometric method. Variational and statistical processing of results was made using Statistica 13 – license standard application program packages for multidimensional statistical analysis. Results. Estimation results of markers of oxidative modification of proteins and glutathione level in pregnant women of group under investigation in dynamics indicate the progress of imbalance between oxidants and antioxidants among pregnant women with fetus growth inhibition compared to women without fetus growth inhibition (p<0.05). Such changes occur in conditions of lack of glutathione which level was significantly and statistically lower (p<0.05) in pregnant women with fetus growth inhibition. These results indicate intensification of oxidative modification of proteins together with decrease of reserve and adaptive abilities of antioxidant system of serum in the present group of pregnant women and it is the manifestation of oxidative stress. Pregnancy course and labour in case of women with fetus growth inhibition is characterized by rise of complication percentage that has negative effect both on rate of operative labour process and perinatal complications. Conclusions. Pregnancy course complicated by fetus growth inhibition is characterized by intensification of oxidative modification of proteins with decrease of reserve and adaptive abilities of antioxidant system of serum. These results indicate occurrence of oxidative stress in this group of women. It should be considered as one of the important links of pathogenesis of fetus growth inhibition. Course of labour among women with fetus growth inhibition is characterized by increase of complication rate that has direct influence on percentage rise of operative labour process under urgent indications. Condition of infants with growth inhibition at birth is characterized by significant and statistical decrease of average indicators under Apgar score and in case of estimation of anthropometric indicators it is characterized by significant and statistical (р < 0.05) decrease of weight-height parameters of newborns. Key words: pregnancy, fetus growth inhibition, oxidative modification of proteins, antioxidative system of protection, obstetric and perinatal complications
Features of the neurohumoral status in women with arterial hypertension and diastolic dysfunction depending on body weight
Authors:   Sumy State University, Kyrychenko N.M.
Published: September 2022

Obesity is associated with inflammation, arterial hypertension, diabetes mellitus, dyslipidemia and increased risk of cardiovascular disease. A structural changes of the left ventricle in obesity leads to diastolic dysfunction and, finally, to systolic dysfunction and heart failure. The objective: to study the characteristics of neurohumoral status in women with arterial hypertension and diastolic dysfunction, depending on body mass index. Materials and methods. 101 patients were examined. Patients were divided into groups according to BMI: group I consisted of 20 patients with a BMI of up to 25 kg/m2, group II included 22 people with a BMI of 30.0 kg/m2 and 34.9 kg/m2, group III – 34 patients with a BMI from 35.0 kg/m2 to 39.9 kg/m2, group IV consisted of 25 patients with a BMI of more than 40 kg/m2. The study involved female patients aged 40–60 with hypertension 2 of II–III degree, impaired relaxation and preserved ejection fraction. All groups of patients had a comprehensive clinical, laboratory and ultrasound examination. Statistical processing was carried out by Statistica for Windows version 6.0. Results. Patients with normal weight had significantly (p<0.05) lower levels of atherogenic lipoproteins and triglycerides than patients with obesity. Individuals with OB 3 had significantly (p<0.05) higher levels of total cholesterol, triglycerides and LDL than patients from groups I–III. Significant (p<0.05) differences in adipokine levels were also determined: the growth of leptin and the decrease of adiponectin with an increase in patient body weight. Indicators of pro-inflammatory activity in patients with OB 3 were significantly (p<0.05) higher than in patients with normal body weight, as well as compared with OB 1–2. Moreover, the levels of these indicators in patients with obesity of 1 and 2 degrees did not significantly differ between groups. Conclusions. Women with AH II–III, obesity of the I-III degree and diastolic dysfunction have a significant decrease in the blood content of adiponectin which observed in a BMI of more than 39.9 kg/m2, while the increase in leptinemia is proportional from I to III degree of obesity with the maximum levels in individuals with a BMI of more than 39.9 kg/m2, also in these individuals there is a significant increase in atherogenic lipid fractions and an increase in blood levels of systemic inflammatory response factors. Key words: obesity, diastolic dysfunction, arterial hypertension.
Influence of organ-preserving operations for good-quality neoplasms of ovaries on a psychoemotional condition of women
Authors:   Shupyk National Medical Academy of Postgraduate Education, Kiev, Boyko A.V. ,   Medical Institute of Sumy State University
Published: September 2022

The objective: to establish influence of organ-preserving operations for good-quality neoplasms of ovaries on psychoemotional condition of women. Materials and methods. Clinical-laboratory and psychological examination of 100 patients of the reproductive age interested in conservation of fecundity, after organ-preserving operative measures concerning good-quality neoplasms of ovaries is conducted. Results. By us it is established that before operation at 86.7±7.8% of patients with benign tumors of ovaries notice depression of quality of life that is explained by implications of basic disease. High levels of uneasiness, nervousness before expeditious treatment testify to the expressed psychoemotional strain of patients that causes attention disturbances, rising of an internal strain, deterioration of emotional health. At all patients with benign tumors and cysts of ovaries before expeditious treatment alarming and depressive neurotic disorders, and also the expressed psychopathologic phenomena that testifies to lack of psychological spirit of patients before operation as part of patients don’t represent the volume of operation take place and aren’t ready to possible consequences. With benign tumors and cysts of ovaries special attention to and the feelings, concern in condition of the health, absence of feeling of safety, uncertainty in, lowered resistance to stress, especially in situations with an unpredictable outcome are inherent to patients, at external lack of implication of alarm. Conclusion. For the purpose of depression of implications of lines of the disharmonious person and creation of favorable spirit on the future and prophylaxes of social disadaptation in to– and postoperative the periods to the patients arriving on expeditious treatment in hospital, it is necessary to carry out the rational psychotherapy referred on creation of favorable spirit on successful result of operation and homing to normal full-fledged life. Key words: organ-preserving operations, good-quality neoplasms of ovaries, psychoemotional state.
Actual aspects of clinical-anamnestic data of women of perimenopause age with hyperplastic processes an endometrium
Authors:   Shupyk National Medical Academy of Postgraduate Education, Kiev, Sadygov Yu.M.
Published: September 2022

The objective: studying of clinical-anamnestic features of hyperplastic processes an endometrium at women of perimenopause age. Materials and methods. 100 women with hyperplastic processes an endometrium during the period perimenopause (the main group) were surveyed. As selection criterion of patients and their distributions on subgroups the morphological form of hyperplastic process an endometriya served. Among the patients surveyed at 54% the glandular hyperplasia an endometrium, was diagnosed for 24% – polyps an endometrium, for 22% – an adenomatous hyperplasia an endometrium. The control group was made by 30 patients of similar age without hyperplastic processes an endometrium. Results. The comparative analysis of the main clinical-anamnestic characteristics of women with hyperplastic processes an endometrium in the perimenopause period, I taped the following risk factors: the extragenital diseases, including illnesses of endocrine system, illness of system circulations, diseases organs digestion; the accompanying gynecologic diseases, including inflammatory processes of genitails, dysplasia of mammary glands, hyperplastic diseases an endometrium and uterus cervix, hysteromyoma, and also transferred gynecological operations — a tubectomy, an adnexectomy. Also were allocated risk factors of development of an adenomatous hyperplasia an endometrium which treated a late menarche and early beginning of sexual life. Conclusion. The received results needs to be considered when developing tactics women of perimenopause age with hyperplastic processes an endometrium. Key words: hyperplastic processes an endometrium, perimenopause age, clinical aspects.
Learning to modalize is learning to reason
Authors: Morek Miriam
Published: September 2022

This exploratory case study examines the interplay between epistemic modalizations and children’s argumentation from a language acquisition perspective. Two (pre)adolescents’ written argumentative texts and recordings of oral decision tasks (parent–child dyads) across four years (grades 6 to 9) present the basis of the longitudinal case study. Repertoires of epistemic modalizations drawn on in written and oral argumentation are described; these are related to overall argumentative structures of the texts as well as to interactive patterns of oral reasoning. Findings show that the occurrence of epistemic modalizations systematically coincides with more elaborated argumentative structures that deal with counter-claims and alternative evidence. In addition, oral reasoning seems to function as an arena for discovering and practicing epistemic modalizations and their potential for constructing two-sided argumentation, and pave the way for a transfer into written communication.
Interactional and multimodal resources in children’s game explanations
Authors: Kern Friederike
Published: September 2022

This paper investigates how adults support pre-school children when accomplishing game explanations. Game explanations are ‘big packages’ that require specific discourse competences to establish local and global coherence. As explanations in general, they have the overall goal to distribute knowledge about a particular matter to the recipient(s). Analyses of explanations of 4-year-old children (n = 20) are presented who were asked to explain a board game to their caregiver. The following questions are addressed: (1) How do the caregivers support the children’s management of explaining the game by co-constructing global and local coherence? (2) What resources do the parents employ to maintain understanding throughout the explanation in order to support and ensure the transfer of knowledge? Analysis will concentrate on the adults’ multimodal practices to establish local and global coherence in the ongoing game explanations, and on their practices to achieve understanding on what the game is about and how it is played. The results will be discussed in relation to game explanations’ underlying function of stepwise knowledge transition, and with regard to developmental issues concerning the use of multimodal practices as scaffolding devices in situated talk.
Lighting Quality Self-assessment in Italian Home Offices
Authors: Bellia Laura , Diglio Francesca , Fragliasso Francesca ,   University of Naples Federico II, Naples, Italy
Published: September 2022

The spread of information technology and the improvement of telecommunicating systems have changed the way to conceive work. People performing typical office activities provided with a laptop and an internet connection can work in whatever place: a coffee house, a waiting room of a train station, an airport, or their own home. The habit to work at home has amazingly spread due to the Covid-19 pandemic, arising crucial questions. Are home work-stations comfortable enough to work? Are lighting conditions of residential spaces proper to perform typical office activities? This paper presents a survey investigating the typical characteristics of the luminous environment in home working spaces (considering both daylight and electric light) and people’s average awareness about light and lighting-connected themes. The research has been carried out through an online questionnaire addressed to home workers and students forced to stay home because of the Covid-19 spread. The results demonstrated that many interviewees have difficulties in managing light and are not completely aware of the right actions to take to improve lighting quality of home workplaces. Moreover, a complete lack of knowledge about electric light has emerged. So, popularizing more knowledge about light and lighting-connected themes is prominent. Indeed, now and even more in the future, people will work in places other than the traditional workplaces, for which standards regulating lighting characteristics do not exist. In this context, the definition of ad hoc guidelines and the spread of best practices should be a crucial goal for researchers and experts in lighting.
Efficient Poverty Mapping from High Resolution Remote Sensing Images
Authors: Ayush Kumar , Uzkent Burak , Tanmay Kumar , Burke Marshall , Lobell David , Ermon Stefano
Published: September 2022

The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. This creates a substantial hurdle to the efficient scaling and widespread adoption of high-resolution-based approaches. To reduce acquisition costs while maintaining accuracy, we propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images, prior to performing a deep learning task on the high-resolution images. We apply this approach to the task of poverty prediction in Uganda, building on an earlier approach that used object detection to count objects and use these counts to predict poverty. Our approach exceeds previous performance benchmarks on this task while using 80% fewer high-resolution images, and could be useful in many domains that require high-resolution imagery.
Universal Trading for Order Execution with Oracle Policy Distillation
Authors: Fang Yuchen , Ren Kan , Liu Weiqing , Zhou Dong , Zhang Weinan , Bian Jiang , Yu Yong , Liu Tie-Yan
Published: September 2022

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
Authors: Imajo Kentaro , Minami Kentaro , Ito Katsuya , Nakagawa Kei
Published: September 2022

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.
In-game Residential Home Planning via Visual Context-aware Global Relation Learning
Authors: Liu Lijuan , Yang Yin , Yuan Yi , Shao Tianjia , Wang He , Zhou Kun
Published: September 2022

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. Given a construction layout, we propose a visual context-aware graph generation network that learns the implicit global relations among the scene components and infers the location of a new building unit. The proposed network takes as input the scene graph and the corresponding top-view depth image. It provides the location recommendations for a newly added building units by learning an auto-regressive edge distribution conditioned on existing scenes. We also introduce a global graph-image matching loss to enhance the awareness of essential geometry semantics of the site. Qualitative and quantitative experiments demonstrate that the recommended location well reflects the implicit spatial rules of components in the residential estates, and it is instructive and practical to locate the building units in the 3D scene of the complex construction.
Programmatic Strategies for Real-Time Strategy Games
Authors: Mariño Julian R. H. , Moraes Rubens O. , Oliveira Tassiana C. , Toledo Claudio , Lelis Levi H. S.
Published: September 2022

Search-based systems have shown to be effective for planning in zero-sum games. However, search-based approaches have important disadvantages. First, the decisions of search algorithms are mostly non-interpretable, which is problematic in domains where predictability and trust are desired such as commercial games. Second, the computational complexity of search-based algorithms might limit their applicability, especially in contexts where resources are shared among other tasks such as graphic rendering. In this work we introduce a system for synthesizing programmatic strategies for a real-time strategy (RTS) game. In contrast with search algorithms, programmatic strategies are more amenable to explanations and tend to be efficient, once the program is synthesized. Our system uses a novel algorithm for simplifying domain-specific languages (DSLs) and a local search algorithm that synthesizes programs with self play. We performed a user study where we enlisted four professional programmers to develop programmatic strategies for mRTS, a minimalist RTS game. Our results show that the programs synthesized by our approach can outperform search algorithms and be competitive with programs written by the programmers.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Authors: Zhao Xiangyu , Gu Changsheng , Zhang Haoshenglun , Yang Xiwang , Liu Xiaobing , Tang Jiliang , Liu Hui
Published: September 2022

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce and news feed sites). However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos). Developing an optimal advertising algorithm in recommendations faces immense challenges because interpolating ads improperly or too frequently may decrease user experience, while interpolating fewer ads will reduce the advertising revenue. Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. To be specific, we develop an RL-based framework that can continuously update its advertising strategies and maximize reward in the long run. Given a recommendation list, we design a novel Deep Q-network architecture that can determine three internally related tasks jointly, i.e., (i) whether to interpolate an ad or not in the recommendation list, and if yes, (ii) the optimal ad and (iii) the optimal location to interpolate. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework.
Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification
Authors: Zhou Yi , Huang Lei , Zhou Tianfei , Shao Ling
Published: September 2022

Chest X-rays are an important and accessible clinical imaging tool for the detection of many thoracic diseases. Over the past decade, deep learning, with a focus on the convolutional neural network (CNN), has become the most powerful computer-aided diagnosis technology for improving disease identification performance. However, training an effective and robust deep CNN usually requires a large amount of data with high annotation quality. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. Thus, existing public chest X-ray datasets usually adopt language pattern based methods to automatically mine labels from reports. However, this results in label uncertainty and inconsistency. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods from two perspectives to improve a single model's disease identification performance, rather than focusing on an ensemble of models. MODL integrates multiple models to obtain a soft label distribution for optimizing the single target model, which can reduce the effects of original label uncertainty. Moreover, KNNS aims to enhance the robustness of the target model to provide consistent predictions on images with similar medical findings. Extensive experiments on the public NIH Chest X-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods.
Apparently Irrational Choice as Optimal Sequential Decision Making
Authors: Chen Haiyang , Chang Hyung Jin , Howes Andrew
Published: September 2022

In this paper, we propose a normative approach to modeling apparently human irrational decision making (cognitive biases) that makes use of inherently rational computational mechanisms. We view preferential choice tasks as sequential decision making problems and formulate them as Partially Observable Markov Decision Processes (POMDPs). The resulting sequential decision model learns what information to gather about which options, whether to calculate option values or make comparisons between options and when to make a choice. We apply the model to choice problems where context is known to influence human choice, an effect that has been taken as evidence that human cognition is irrational. Our results show that the new model approximates a bounded optimal cognitive policy and makes quantitative predictions that correspond well to evidence about human choice. Furthermore, the model uses context to help infer which option has a maximum expected value while taking into account computational cost and cognitive limits. In addition, it predicts when, and explains why, people stop evidence accumulation and make a decision. We argue that the model provides evidence that apparent human irrationalities are emergent consequences of processes that prefer higher value (rational) policies.
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
Authors: Li Xianfeng , Chen Weijie , Xie Di , Yang Shicai , Yuan Peng , Pu Shiliang , Zhuang Yueting
Published: September 2022

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.
On the Adequacy of Untuned Warmup for Adaptive Optimization
Authors: Ma Jerry , Yarats Denis
Published: September 2022

Adaptive optimization algorithms such as Adam (Kingma and Ba, 2014) are widely used in deep learning. The stability of such algorithms is often improved with a warmup schedule for the learning rate. Motivated by the difficulty of choosing and tuning warmup schedules, recent work proposes automatic variance rectification of Adam's adaptive learning rate, claiming that this rectified approach ("RAdam") surpasses the vanilla Adam algorithm and reduces the need for expensive tuning of Adam with warmup. In this work, we refute this analysis and provide an alternative explanation for the necessity of warmup based on the magnitude of the update term, which is of greater relevance to training stability. We then provide some "rule-of-thumb" warmup schedules, and we demonstrate that simple untuned warmup of Adam performs more-or-less identically to RAdam in typical practical settings. We conclude by suggesting that practitioners stick to linear warmup with Adam, with a sensible default being linear warmup over 2 / (1 - β₂) training iterations.
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Authors: Majeed Sultan J. , Hutter Marcus
Published: September 2022

The reinforcement learning (RL) framework formalizes the notion of learning with interactions. Many real-world problems have large state-spaces and/or action-spaces such as in Go, StarCraft, protein folding, and robotics or are non-Markovian, which cause significant challenges to RL algorithms. In this work we address the large action-space problem by sequentializing actions, which can reduce the action-space size significantly, even down to two actions at the expense of an increased planning horizon. We provide explicit and exact constructions and equivalence proofs for all quantities of interest for arbitrary history-based processes. In the case of MDPs, this could help RL algorithms that bootstrap. In this work we show how action-binarization in the non-MDP case can significantly improve Extreme State Aggregation (ESA) bounds. ESA allows casting any (non-MDP, non-ergodic, history-based) RL problem into a fixed-sized non-Markovian state-space with the help of a surrogate Markovian process. On the upside, ESA enjoys similar optimality guarantees as Markovian models do. But a downside is that the size of the aggregated state-space becomes exponential in the size of the action-space. In this work, we patch this issue by binarizing the action-space. We provide an upper bound on the number of states of this binarized ESA that is logarithmic in the original action-space size, a double-exponential improvement.
Composite Adversarial Attacks
Authors: Mao Xiaofeng , Chen Yuefeng , Wang Shuhui , Su Hang , He Yuan , Xue Hui
Published: September 2022

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artificially selected and tuned by human experts to break a ML system. However, manual selection of attackers tends to be sub-optimal, leading to a mistakenly assessment of model security. In this paper, a new procedure called Composite Adversarial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms and their hyper-parameters from a candidate pool of 32 base attackers. We design a search space where attack policy is represented as an attacking sequence, i.e., the output of the previous attacker is used as the initialization input for successors. Multi-objective NSGA-II genetic algorithm is adopted for finding the strongest attack policy with minimum complexity. The experimental result shows CAA beats 10 top attackers on 11 diverse defenses with less elapsed time (6 × faster than AutoAttack), and achieves the new state-of-the-art on linf, l2 and unrestricted adversarial attacks.
Deep Mutual Information Maximin for Cross-Modal Clustering
Authors: Mao Yiqiao , Yan Xiaoqiang , Guo Qiang , Ye Yangdong
Published: September 2022

Cross-modal clustering (CMC) aims to enhance the clustering performance by exploring complementary information from multiple modalities. However, the performances of existing CMC algorithms are still unsatisfactory due to the conflict of heterogeneous modalities and the high-dimensional non-linear property of individual modality. In this paper, a novel deep mutual information maximin (DMIM) method for cross-modal clustering is proposed to maximally preserve the shared information of multiple modalities while eliminating the superfluous information of individual modalities in an end-to-end manner. Specifically, a multi-modal shared encoder is firstly built to align the latent feature distributions by sharing parameters across modalities. Then, DMIM formulates the complementarity of multi-modalities representations as an mutual information maximin objective function, in which the shared information of multiple modalities and the superfluous information of individual modalities are identified by mutual information maximization and minimization respectively. To solve the DMIM objective function, we propose a variational optimization method to ensure it converge to a local optimal solution. Moreover, an auxiliary overclustering mechanism is employed to optimize the clustering structure by introducing more detailed clustering classes. Extensive experimental results demonstrate the superiority of DMIM method over the state-of-the-art cross-modal clustering methods on IAPR-TC12, ESP-Game, MIRFlickr and NUS-Wide datasets.
Searching for Machine Learning Pipelines Using a Context-Free Grammar
Authors: Marinescu Radu , Kishimoto Akihiro , Ram Parikshit , Rawat Ambrish , Wistuba Martin , Palmes Paulito P. , Botea Adi
Published: September 2022

AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. These complex pipeline structure may lead to discovering hidden features and thus boost performance considerably. We explore the power of heuristic search and context-free grammars to search and optimize these kinds of pipelines. Experiments on various benchmark datasets show that our approach is highly competitive and often outperforms existing AutoML systems.
Scheduling of Time-Varying Workloads Using Reinforcement Learning
Authors: Mondal Shanka Subhra , Sheoran Nikhil , Mitra Subrata
Published: September 2022

Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource usage between two workloads running on the same machine can create performance degradation, unused resources in a machine results in wastage and undesirable operational characteristics for a compute cluster. Prior works did not consider such temporal resource fluctuations or their alignment for scheduling decisions. Due to the variety of time-varying workloads, their complex resource usage characteristics, it is challenging to design well-defined heuristics for scheduling them optimally across different machines in a cluster. In this paper, we propose a Deep Reinforcement Learning (DRL) based approach to exploit various temporal resource usage patterns of time varying workloads as well as a technique for creating equivalence classes among a large number of production workloads to improve scalability of our method. Validations with real production traces from Google and Alibaba show that our technique can significantly improve metrics for operational excellence (e.g. utilization, fragmentation, resource exhaustion etc.) for a cluster, compared to the baselines.
Improved Mutual Information Estimation
Authors: Mroueh Youssef , Melnyk Igor , Dognin Pierre , Ross Jarret , Sercu Tom
Published: September 2022

We propose to estimate the KL divergence using a relaxed likelihood ratio estimation in a Reproducing Kernel Hilbert space. We show that the dual of our ratio estimator for KL in the particular case of Mutual Information estimation corresponds to a lower bound on the MI that is related to the so called Donsker Varadhan lower bound. In this dual form, MI is estimated via learning a witness function discriminating between the joint density and the product of marginal, as well as an auxiliary scalar variable that enforces a normalization constraint on the likelihood ratio. By extending the function space to neural networks, we propose an efficient neural MI estimator, and validate its performance on synthetic examples, showing advantage over the existing baselines. We demonstrate its strength in large-scale self-supervised representation learning through MI maximization.
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning
Authors: Nagalapatti Lokesh , Narayanam Ramasuri
Published: September 2022

The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server. In this setup, each client’s data is private to itself and is not transferable to other clients or the server. Though FL paradigm has received significant interest recently from the research community, the problem of selecting the relevant clients w.r.t. the central server's learning objective is under-explored. We refer to these problems as Federated Relevant Client Selection (FRCS). Because the server doesn't have explicit control over the nature of data possessed by each client, the problem of selecting relevant clients is significantly complex in FL settings.
In this paper, we resolve important and related FRCS problems viz., selecting clients with relevant data, detecting clients that possess data relevant to a particular target label, and rectifying corrupted data samples of individual clients. We follow a principled approach to address the above FRCS problems and develop a new federated learning method using the Shapley value concept from cooperative game theory. Towards this end, we propose a cooperative game involving the gradients shared by the clients. Using this game, we compute Shapley values of clients and then present Shapley value based Federated Averaging (S-FedAvg) algorithm that empowers the server to select relevant clients with high probability. S-FedAvg turns out to be critical in designing specific algorithms to address the FRCS problems. We finally conduct a thorough empirical analysis on image classification and speech recognition tasks to show the superior performance of S-FedAvg than the baselines in the context of supervised federated learning settings.