bluuebunny/crossref_metadata_2025 huggingface dataset:
Copula-Based Anomaly Scoring and Localization for Large-Scale, High-Dimensional Continuous Data
Authors: Horváth Gábor , Kovács Edith , Molontay Roland , Nováczki Szabolcs
Published: May 2020

The anomaly detection method presented by this article has a special feature: it not only indicates whether or not an observation is anomalous but also tells what exactly makes an anomalous observation unusual. Hence, it provides support to localize the reason of the anomaly.

The proposed approach is model based; it relies on the multivariate probability distribution associated with the observations. Since the rare events are present in the tails of the probability distributions, we use copula functions, which are able to model the fat-tailed distributions well. The presented procedure scales well; it can cope with a large number of high-dimensional samples. Furthermore, our procedure can cope with missing values as well, which occur frequently in high-dimensional datasets.

In the second part of the article, we demonstrate the usability of the method through a case study, where we analyze a large dataset consisting of the performance counters of a real mobile telecommunication network. Since such networks are complex systems, the signs of sub-optimal operation can remain hidden for a potentially long time. With the proposed procedure, many such hidden issues can be isolated and indicated to the network operator.
Exploring the Needs, Preferences, and Concerns of Persons with Visual Impairments Regarding Autonomous Vehicles
Authors: Brinkley Julian , Huff Earl W. , Posadas Briana , Woodward Julia , Daily Shaundra B. , Gilbert Juan E.
Published: May 2020

Fully autonomous or “self-driving” vehicles are an emerging technology that may hold tremendous mobility potential for blind or visually impaired persons who are currently unable to drive a conventional motor vehicle. Despite the considerable potential of self-driving vehicle technology to address this mobility issue, however, the needs and preferences of persons with visual disabilities regarding this technology have been insufficiently investigated. In this article, we present the results of two studies that are focused on exploring the needs, preferences, and concerns of persons with visual impairments as it relates to self-driving vehicles. Study one investigated user acceptance, concerns, and willingness to buy partially and fully automated vehicles using a 39-question Internet-based survey distributed in the United States to visually impaired respondents (n= 516). Study two explores the opinions of 38 participants who are blind and low vision, using focus group methodology, regarding emerging self-driving vehicle technology. Collectively our findings suggest that while persons with visual impairments may be optimistic regarding the potential for enhanced mobility and independence that may result from the emergence of self-driving vehicles, concerns exist regarding the implementation of this technology that have been largely unexplored and under investigated.
Architecture and Performance of Devito, a System for Automated Stencil Computation
Authors: Luporini Fabio , Louboutin Mathias , Lange Michael , Kukreja Navjot , Witte Philipp , Hückelheim Jan , Yount Charles , Kelly Paul H. J. , Herrmann Felix J. , Gorman Gerard J.
Published: May 2020

Stencil computations are a key part of many high-performance computing applications, such as image processing, convolutional neural networks, and finite-difference solvers for partial differential equations. Devito is a framework capable of generating highly optimized code given symbolic equations expressed in
Python
, specialized in, but not limited to, affine (stencil) codes. The lowering process—from mathematical equations down to C++ code—is performed by the Devito compiler through a series of intermediate representations. Several performance optimizations are introduced, including advanced common sub-expressions elimination, tiling, and parallelization. Some of these are obtained through well-established stencil optimizers, integrated in the backend of the Devito compiler. The architecture of the Devito compiler, as well as the performance optimizations that are applied when generating code, are presented. The effectiveness of such performance optimizations is demonstrated using operators drawn from seismic imaging applications.
Exploiting Proxy Sensing for Efficient Monitoring of Large-Scale Sensor Networks
Authors: Pal Amitangshu , Kant Krishna
Published: May 2020

Large networks of IoT devices, each consisting of one or more sensors, are being increasingly deployed for comprehensive real-time monitoring of cyber-physical systems. Such networks form an essential component of the emerging edge computing paradigm and are expected to increase in complexity and size. The physical phenomenon sensed by different sensors (within the same or different IoT devices in close proximity) often have relationships that makes them correlated. This is a form of
proxy sensing
that can be exploited for achieving better energy efficiency and higher robustness in monitoring. In this article, we explore how a set of sensors can optimize its data collection rates efficiently in a semi-distributed manner and yet provide the advantages of autonomy, relative isolation, and distributed control that is essential in a large-scale network.
Emoji Skin Tone Modifiers
Authors: Robertson Alexander , Magdy Walid , Goldwater Sharon
Published: May 2020

Emoji are widely used in computer-mediated communication to express concepts and emotions. Skin tone modifiers were added in 2015 with the hope of better representing user diversity, and, indeed, recent work has shown that these modifiers are especially popular among darker-skinned users, who are a minority on Twitter. Previous work also showed that the vast majority of tone-modified emoji have a tone similar to the user’s own skin tone, suggesting that self-representation is a major factor in tone use. In this article, we first show that the basic finding (users mainly choose a tone that is similar to their own skin tone) generalizes to different sub-populations of users, including users from majority-Black regions. We then extend the analysis of tone use to quantify and examine cases where users
modulate
their tones—that is, for a particular emoji, they choose either to use a different tone than their usual one or no tone at all (after having previously used one). We show that even though these uses constitute only a small proportion of emoji usage, many instances are readily classifiable as ways of representing
other
people. The evidence we present is therefore crucial in working toward a broader understanding of the connection between emoji and identity expression online. We also offer explanations for why the darkest emoji skin tones are not used, by examining aspects of their design, which make them less suited to self-representational usage. This highlights the need for careful consideration of both design and human diversity when creating emoji. Moreover, despite early fears in the media, we find little evidence of negative usage, even when tones are used in a non-self-representational manner. In sum, our findings lend even more support to the highly positive role that emoji and skin tone modifiers play in identity and expression in computer-mediated communication.
WiSign
Authors: Zhang Lei , Zhang Yixiang , Zheng Xiaolong
Published: May 2020

In this article, we propose WiSign that recognizes the continuous sentences of American Sign Language (ASL) with existing WiFi infrastructure. Instead of identifying the individual ASL words from the manually segmented ASL sentence in existing works, WiSign can automatically segment the original channel state information (CSI) based on the power spectral density (PSD) segmentation method. WiSign constructs a five-layer Deep Belief Network (DBN) to automatically extract the features of isolated fragments, and then uses the Hidden Markov Model (HMM) with Gaussian mixture and Forward-Backward algorithm to recognize sign words. In order to further improve the accuracy, WiSign also integrates the language model N-gram, which uses the grammar rules of ASL to calibrate the recognized results of sign words. We implement a prototype of WiSign with commercial WiFi devices and evaluate its performance in real indoor environments. The results show that WiSign achieves satisfactory accuracy when recognizing ASL sentences that involve the movements of the head, arms, hands, and fingers.
The Security of Lazy Users in Out-of-Band Authentication
Authors: Naor Moni , Rotem Lior , Segev Gil
Published: May 2020

Faced with the threats posed by man-in-the-middle attacks, messaging platforms rely on “out-of-band” authentication, assuming that users have access to an external channel for authenticating one short value. For example, assuming that users recognizing each other’s voice can authenticate a short value, Telegram and WhatApp ask their users to compare 288-bit and 200-bit values, respectively. The existing protocols, however, do not take into account the plausible behavior of users who may be “lazy” and only compare parts of these values (rather than their entirety).

Motivated by such a security-critical user behavior, we study the security of lazy users in out-of-band authentication. We start by showing that both the protocol implemented by WhatsApp and the statistically optimal protocol of Naor, Segev, and Smith (CRYPTO’06) are completely vulnerable to man-in-the-middle attacks when the users consider only a half of the out-of-band authenticated value. In this light, we put forward a framework that captures the behavior and security of lazy users. Our notions of security consider both statistical security and computational security, and for each flavor we derive a lower bound on the tradeoff between the number of positions that are considered by the lazy users and the adversary’s forgery probability.

Within our framework, we then provide two authentication protocols. First, in the statistical setting, we present a transformation that converts any out-of-band authentication protocol into one that is secure even when executed by lazy users. Instantiating our transformation with a new refinement of the protocol of Naor et al. results in a protocol whose tradeoff essentially matches our lower bound in the statistical setting. Then, in the computational setting, we show that the computationally optimal protocol of Vaudenay (CRYPTO’05) is secure even when executed by lazy users—and its tradeoff matches our lower bound in the computational setting.
Using Generative Adversarial Networks to Break and Protect Text Captchas
Authors: Ye Guixin , Tang Zhanyong , Fang Dingyi , Zhu Zhanxing , Feng Yansong , Xu Pengfei , Chen Xiaojiang , Han Jungong , Wang Zheng
Published: May 2020

Text-based CAPTCHAs remains a popular scheme for distinguishing between a legitimate human user and an automated program. This article presents a novel genetic text captcha solver based on the generative adversarial network. As a departure from prior text captcha solvers that require a labor-intensive and time-consuming process to construct, our scheme needs significantly fewer real captchas but yields better performance in solving captchas. Our approach works by first learning a synthesizer to automatically generate synthetic captchas to construct a base solver. It then improves and fine-tunes the base solver using a small number of labeled real captchas. As a result, our attack requires only a small set of manually labeled captchas, which reduces the cost of launching an attack on a captcha scheme. We evaluate our scheme by applying it to 33 captcha schemes, of which 11 are currently used by 32 of the top-50 popular websites. Experimental results demonstrate that our scheme significantly outperforms four prior captcha solvers and can solve captcha schemes where others fail. As a countermeasure, we propose to add imperceptible perturbations onto a captcha image. We demonstrate that our countermeasure can greatly reduce the success rate of the attack.
A Survey of Hierarchical Energy Optimization for Mobile Edge Computing
Authors: Cong Peijin , Zhou Junlong , Li Liying , Cao Kun , Wei Tongquan , Li Keqin
Published: May 2020

With the development of wireless technology, various emerging mobile applications are attracting significant attention and drastically changing our daily lives. Applications such as augmented reality and object recognition demand stringent delay and powerful processing capability, which exerts enormous pressure on mobile devices with limited resources and energy. In this article, a survey of techniques for mobile device energy optimization is presented in a hierarchy of device design and operation, computation offloading, wireless data transmission, and cloud execution of offloaded computation. Energy management strategies for mobile devices from hardware and software aspects are first discussed, followed by energy-efficient computation offloading frameworks for mobile applications that trade application response time for device energy consumption. Then, techniques for efficient wireless data communication to reduce transmission energy are summarized. Finally, the execution mechanisms of application components or tasks in various clouds are further described to provide energy-saving opportunities for mobile devices. We classify the research works based on key characteristics of devices and applications to emphasize their similarities and differences. We hope that this survey will give insights to researchers into energy management mechanisms on mobile devices, and emphasize the crucial importance of optimizing device energy consumption for more research efforts in this area.
A Critical Survey of the Multilevel Method in Complex Networks
Authors: Valejo Alan , Ferreira Vinícius , Fabbri Renato , Oliveira Maria Cristina Ferreira de , Lopes Alneu de Andrade
Published: May 2020

Multilevel optimization aims at reducing the cost of executing a target network-based algorithm by exploiting coarsened, i.e., reduced or simplified, versions of the network. There is a growing interest in multilevel algorithms in networked systems, mostly motivated by the urge for solutions capable of handling large-scale networks. Notwithstanding the success of multilevel optimization in a multitude of application problems, we were unable to find a representative survey of the state-of-the-art, or consistent descriptions of the method as a general theoretical framework independent of a specific application domain. In this article, we strive to fill this gap, presenting an extensive survey of the literature that contemplates a systematic overview of the state-of-the-art, a panorama of the historical evolution and current challenges, and a formal theoretical framework of the multilevel optimization method in complex networks. We believe our survey provides a useful resource to individuals interested in learning about multilevel strategies, as well as to those engaged in advancing theoretical and practical aspects of the method or in developing solutions in novel application domains.
Incorporating LSTM Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem
Authors: Shao Wei , Tan Siyu , Zhao Sichen , Qin Kyle Kai , Hei Xinhong , Chan Jeffrey , Salim Flora D.
Published: May 2020

The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help parking officers monitor parking violations. The traveling officer problem was customized to formulate a path-finding problem that aims to maximize the probability of catching overstayed cars before they leave. One of the challenges is to extract effective features from the big spatio-temporal data and provide a data-driven solution to replace conventional solutions such as a simple rule-based system or single optimization methods. In this article, we propose a seamless end-to-end learning and optimization framework that combines the long short-term memory auto-encoder neural network, clustering, and path-finding methods to solve the traveling officer problem. Our extensive comparison experiments on a large-scale real-world dataset have shown that our proposed solution outperforms any other single-step or optimization methods.
Multidimensional Dynamic Pricing for Welfare Maximization
Authors: Roth Aaron , Slivkins Aleksandrs , Ullman Jonathan , Wu Zhiwei Steven
Published: May 2020

We study the problem of a seller dynamically pricing
d
distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an unknown distribution. The goods are not in limited supply, but can only be produced at a limited rate and are costly to produce. The seller observes only the bundle of goods purchased at each day, but nothing else about the buyer’s valuation function. Our main result is a dynamic pricing algorithm for optimizing welfare (including the seller’s cost of production) that runs in time and a number of rounds that are polynomial in
d
and the approximation parameter. We are able to do this despite the fact that (i) the price-response function is not continuous, and even its fractional relaxation is a non-concave function of the prices, and (ii) the welfare is not observable to the seller.

We derive this result as an application of a general technique for optimizing welfare over
divisible
goods, which is of independent interest. When buyers have strongly concave, Hölder continuous valuation functions over
d
divisible goods, we give a general polynomial time dynamic pricing technique. We are able to apply this technique to the setting of unit-demand buyers despite the fact that in that setting the goods are not divisible, and the natural fractional relaxation of a unit-demand valuation is not strongly concave. To apply our general technique, we introduce a novel price randomization procedure that has the effect of implicitly inducing buyers to “regularize” their valuations with a strongly concave function. Finally, we also extend our results to a limited-supply setting in which the supply of each good cannot be replenished.
Distributed Signaling Games
Authors: Feldman Moran , Tennenholtz Moshe , Weinstein Omri
Published: May 2020

A recurring theme in recent computer science literature is that proper design of signaling schemes is a crucial aspect of effective mechanisms aiming to optimize social welfare or revenue. One of the research endeavors of this line of work is understanding the algorithmic and computational complexity of designing efficient signaling schemes. In reality, however, information is typically not held by a central authority but is distributed among multiple sources (third-party “mediators”), a fact that dramatically changes the strategic and combinatorial nature of the signaling problem.

In this article, we introduce
distributed signaling games
, while using display advertising as a canonical example for introducing this foundational framework. A distributed signaling game may be a pure coordination game (i.e., a distributed optimization task) or a non-cooperative game. In the context of pure coordination games, we show a wide gap between the computational complexity of the centralized and distributed signaling problems, proving that distributed coordination on revenue-optimal signaling is a much harder problem than its “centralized” counterpart.

In the context of non-cooperative games, the outcome generated by the mediators’ signals may have different value to each. The reason for that is typically the desire of the auctioneer to align the incentives of the mediators with his own by a compensation relative to the marginal benefit from their signals. We design a mechanism for this problem via a novel application of Shapley’s value and show that it possesses some interesting properties; in particular, it always admits a pure Nash equilibrium, and it never decreases the revenue of the auctioneer (relative to his
a priori
revenue when there are no mediators).
Pricing (and Bidding) Strategies for Delay Differentiated Cloud Services
Authors: Song Jiayi , Guérin Roch
Published: May 2020

We consider a cloud provider that seeks to maximize its revenue by offering services with different trade-offs between cost and timeliness of job completion. Spot instances and preemptible instances are examples of such services, with, in both cases, possible service interruptions delaying a job’s completion. Our focus is on exploiting heterogeneity across jobs in terms of value and sensitivity to execution delay, with a joint distribution that determines their relationship across the user population. We characterize optimal (revenue maximizing) pricing strategies and, in the case of spot instances, optimal bidding strategies as well as identify conditions under which bidding at a fixed price is optimal. We show that correlation between delay sensitivity and job value needs to exceed a certain threshold for a service offering that differentiates based on speed of execution to be beneficial to the provider. We further assess the results’ robustness under more general assumptions, and we offer guidelines for users and providers.
Towards Synaptic Behavior of Nanoscale ReRAM Devices for Neuromorphic Computing Applications
Authors: Beckmann Karsten , Olin-Ammentorp Wilkie , Chakma Gangotree , Amer Sherif , Rose Garrett S. , Hobbs Chris , Nostrand Joseph Van , Rodgers Martin , Cady Nathaniel C.
Published: May 2020

Resistive Random Access Memory (ReRAM), a form of non-volatile memory, has been proposed as a Flash memory replacement. In addition, novel circuit architectures have been proposed that rely on newly discovered or predicted behavior of ReRAM. One such architecture is the memristive Dynamic Adaptive Neural Network Array, developed to emulate the functionality of a biological neuron system. We demonstrated ReRAM devices that show a synaptic tendency by changing their resistance in an analog fashion. The CMOS compatible nanoscale ReRAM devices shown are based on an HfO2switching layer that sits on a tungsten electrode and is covered by a titanium oxygen scavenger layer and a titanium nitride top electrode. In this work, we showed devices exceeding endurance values of 10B cycles with a discrete Roff/Ronratio of 15. Multi-level states were achieved by using consecutive ultra-short 5/1.5 ns pulses during the reset operation. A neural network simulation was performed in which the synaptic weights were perturbed with the ReRAM variability, which was extracted from two different characterization methods: (1) via direct write, and (2) via a write/read verification approach during the reset operation. A substantial improvement of the neural network fitness was demonstrated when using the write/read verification approach.
Understanding the Motivations of Final-year Computing Undergraduates for Considering Accessibility
Authors: Conn Paula , Gotfrid Taylor , Zhao Qiwen , Celestine Rachel , Mande Vaishnavi , Shinohara Kristen , Ludi Stephanie , Huenerfauth Matt
Published: May 2020

We investigate the degree to which undergraduate computing students in a United States university consider accessibility several years after instruction. Prior work has found that cultural and ethical norms become ingrained early in STEM professionals’ careers; so, we focus on students approaching graduation and after an internship experience, who are just getting started in their career. In semi-structured interviews, a majority of these final-year computing students (14 of 16) indicated that they were not motivated to improve their skills in accessibility, attributing this to
not being required to consider accessibility in subsequent work or classes, not seeing accessibility as an essential skill in their profession
, and
challenges due to a learn-it-on-your-own approach
in computing. Participants suggested instructional methods and topics that they believed would have better prepared them for considering accessibility. A survey of 114 additional final-year students revealed similar themes, including that students did not personally view accessibility training as essential career preparation. Prior research has largely focused on evaluating short-term changes in students’ knowledge after an educational intervention. Therefore, by focusing on students several years after an intervention, this work highlights lingering barriers for university programs in promoting accessibility among rising computing professionals.
Effective Detection of Sleep-in-atomic-context Bugs in the Linux Kernel
Authors: Bai Jia-Ju , Lawall Julia , Hu Shi-Min
Published: May 2020

Atomic context is an execution state of the Linux kernel in which kernel code monopolizes a CPU core. In this state, the Linux kernel may only perform operations that cannot sleep, as otherwise a system hang or crash may occur. We refer to this kind of concurrency bug as a sleep-in-atomic-context (SAC) bug. In practice, SAC bugs are hard to find, as they do not cause problems in all executions.

In this article, we propose a practical static approach named DSAC to effectively detect SAC bugs in the Linux kernel. DSAC uses three key techniques: (1) a summary-based analysis to identify the code that may be executed in atomic context, (2) a connection-based alias analysis to identify the set of functions referenced by a function pointer, and (3) a path-check method to filter out repeated reports and false bugs. We evaluate DSAC on Linux 4.17 and find 1,159 SAC bugs. We manually check all the bugs and find that 1,068 bugs are real. We have randomly selected 300 of the real bugs and sent them to kernel developers. 220 of these bugs have been confirmed, and 51 of our patches fixing 115 bugs have been applied.
A Simpler and Faster Strongly Polynomial Algorithm for Generalized Flow Maximization
Authors: Olver Neil , Végh László A.
Published: May 2020

We present a new strongly polynomial algorithm for generalized flow maximization that is significantly simpler and faster than the previous strongly polynomial algorithm [34]. For the uncapacitated problem formulation, the complexity bound
O
(
mn
(
m
+
n
log
n
)log (
n
2
/
m
)) improves on the previous estimate by almost a factor
O
(
n
2
). Even for small numerical parameter values, our running time bound is comparable to the best weakly polynomial algorithms. The key new technical idea is relaxing the primal feasibility conditions. This allows us to work almost exclusively with integral flows, in contrast to all previous algorithms for the problem.
The Impact of Gamification on Learning Outcomes of Computer Science Majors
Authors: Ahmad Adnan , Zeshan Furkh , Khan Muhammad Salman , Marriam Rutab , Ali Amjad , Samreen Alia
Published: May 2020

Gamification is the use of game elements in domains other than games. Gamification use is often suggested for difficult activities because it enhances users’ engagement and motivation level. Due to such benefits, the use of gamification is also proposed in education environments to improve students’ performance, engagement, and satisfaction. Computer science in higher education is a tough area of study and thus needs to utilize various already explored benefits of gamification. This research develops an empirical study to evaluate the effectiveness of gamification in teaching computer science in higher education. Along with the learning outcomes, the effect of group size on students’ satisfaction level is also measured. Furthermore, the impact of gamification over time is analyzed throughout a semester to observe its effectiveness as a long-term learning technique. The analysis, covering both learning outcome and students’ satisfaction, suggests that gamification is an effective tool to teach tough courses at higher education level; however, group size should be taken into account for optimal classroom size and better learning experience.
Data Quality and Explainable AI
Authors: Bertossi Leopoldo , Geerts Floris
Published: May 2020

In this work, we provide some insights and develop some ideas, with few technical details, about the role of explanations in Data Quality in the context of data-based machine learning models (ML). In this direction, there are, as expected, roles for causality, and
explainable artificial intelligence
. The latter area not only sheds light on the models, but also on the data that support model construction. There is also room for defining, identifying, and explaining errors in data, in particular, in ML, and also for suggesting repair actions. More generally, explanations can be used as a basis for defining dirty data in the context of ML, and measuring or quantifying them. We think dirtiness as relative to the ML task at hand, e.g., classification.
Upcoming SIGWEB supported conferences
Authors: Samuel Hamman
Published: May 2020

The Special Interest Group on Hypertext and the Web, SIGWEB was created in 1989 to support the community participating in the annual ACM Hypertext Conference. Now in its third decade, SIGWEB has grown considerably and now sponsors seven annual conferences of different sizes and covering a wide range of topics. SIGWEB supports several specialized conferences, short courses, and workshops, as well as the Annual Hypertext Conference. SIGWEB sponsored conferences focus on timely topics in applied and computational hypertext and Web disciplines and provide a place for members and the entire applied Hypermedia and Web community to exchange ideas and to meet with and expand their network of colleagues. In this article, we provide a brief overview of SIGWEB sponsored conferences, in addition to events that are in cooperation with SIGWEB.
Register complexity and determinisation of max-plus automata
Authors: Daviaud Laure
Published: May 2020

We survey some results about the sequentiality problem for max-plus automata and its generalisation, the register complexity problem for cost register automata. We compare classes of functions computed by maxplus automata and by cost register automata with respect to the notion of ambiguity. The two models are introduced gently, so the novice reader is welcome!
Analysis of MRI Data in Diagnostic Neuroradiology
Authors: Rathore Saima , Abdulkadir Ahmed , Davatzikos Christos
Published: May 2020

Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.
Concentration in Seed and Biotech Markets: Extent, Causes, and Impacts
Authors: Deconinck Koen
Published: May 2020

The merger of Dow and DuPont, the acquisition of Syngenta by ChemChina, and the acquisition of Monsanto by Bayer have recently reshaped the global seed and biotech industry and caused concern about growing market concentration. This review documents market concentration in seed and agricultural biotech markets and discusses its causes and impacts. The available evidence suggests that concentration in seed markets varies strongly by crop and by country, while markets for biotech traits are considerably more concentrated. Complementarities between seed, biotech, and crop protection chemicals explain much of the observed structural changes in the industry, and new complementarities may be emerging with digital agriculture. Although growing concentration might in theory lead to higher prices and less innovation, evidence on this is currently limited; this tendency is also in part offset by the remedies imposed by competition authorities.
Stranded Assets in the Transition to a Carbon-Free Economy
Authors: van der Ploeg Frederick , Rezai Armon
Published: May 2020

Assets in the fossil fuel industries are at risk of losing market value due to unanticipated breakthroughs in renewable technology and governments stepping up climate policies in light of the Paris commitments to limit global warming to 1.5 or 2°C. Stranded assets arise due to uncertainty about the future timing of these two types of events and substantial intertemporal and intersectoral investment adjustment costs. Stranding of assets mostly affects the 20 biggest oil, gas, and coal companies who have been responsible for at least one-third of global warming since 1965, but it also affects carbon-intensive industries such as steel, aluminum, cement, plastics, and greenhouse horticulture. A disorderly transition to the carbon-free economy will lead to stranded assets and legal claims. Institutional investors should be aware of these financial risks. A broader definition of stranded assets also includes countries reliant on fossil fuel exports and workers with technology-specific skills.
Effect of Denosumab on Bone Mineral Density of Hematopoietic Stem Cell Transplantation Recipients
Authors: Jeong Chaiho , Kim Hee-Je , Lee Seok , Kang Moo Il , Ha Jeonghoon
Published: May 2020

Purpose. Denosumab is a monoclonal antibody that prevents the development of osteoclasts. The effect of denosumab in solid organ transplant recipients has been elucidated, but its effect in haematopoietic stem cell transplantation recipients has not been studied yet. The aim of this study was to determine the effectiveness and safety of denosumab in haematopoietic stem cell transplantation recipients. Methods. We retrospectively evaluated 33 female patients with osteoporosis (mean age 52.6 ± 9.8 years) following allogeneic haematopoietic stem cell transplantation. Patients were treated with denosumab every 6 months for 12 months. Changes in bone mineral density were evaluated for denosumab-treated patients in a 12-month interval after the first administration of denosumab. Results. Significant increases in bone mineral density were observed in all measured skeletal sites including 4.39 ± 6.63% in the lumbar spine (p=0.014), 3.11 ± 7.69% in the femoral neck (p=0.048), and 1.97 ± 6.01% in the total hip (p=0.138). The bone turnover marker serum cross-linked C-terminal telopeptide of type 1 collagen was decreased at 18 months (−51.6 ± 17.6%, p<0.001). No serious symptomatic hypocalcaemia was observed. Serious adverse drug reactions requiring drug discontinuation were not observed. Conclusion. Denosumab improved bone mineral density in haematopoietic stem cell transplantation recipients. The use of denosumab could be a good therapeutic option without causing severe adverse effects in recipients of haematopoietic transplantation.
Rapid Convergence of Approximate Solutions for Fractional Differential Equations
Authors: Wu Xiran , Bao Junyan , Sun Yufeng
Published: May 2020

In this paper, we develop a generalized quasilinearization technique for a class of Caputo’s fractional differential equations when the forcing function is the sum of hyperconvex and hyperconcave functions of order m (m≥0), and we obtain the convergence of the sequences of approximate solutions by establishing the convergence of order k (k≥2).
Integrated Scheduling for Steelmaking Continuous Casting— Hot Rolling Processes considering Hot Chain Logistics
Authors: Wang Sen , Shi Yarong , Liu Shixin
Published: May 2020

Steelmaking continuous casting (SCC)—hot rolling (HR) is a key process in the production of steel products. It is also a process with large energy consumption. Energy saving has always been an important goal of production scheduling of this process. In this paper, aiming at integrated scheduling optimization for SCC-HR processes, energy saving objective is converted to minimize waiting time of slabs in slab yard, so as to reduce slab temperature loss and achieve the goal of saving energy. An integrated two-stage mathematical programming model is established for the above problems, and a hybrid algorithm of genetic algorithm and linear programming is designed for the integrated model. The correctness of the model and the validity of the algorithm are verified by computational experiments using simulated instances.
Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks
Authors: Chen Shuang , Chen Ying , Chen Xin , Hu Yuemei
Published: May 2020

With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost.
A Novel Hypogenetic Chaotic Jerk System: Modeling, Circuit Implementation, and Its Application
Authors: Liu Jiancheng , Rajagopal Karthikeyan , Lei Tengfei , Kaçar Sezgin , Arıcıoğlu Burak , Çavuşoğlu Ünal , Kökçam Abdullah Hulusi , Karthikeyan Anitha
Published: May 2020

When revising the polarity and amplitude information in the feedback, a unique hypogenetic jerk system was obtained which has two controllers to switch the equilibria between stable and unstable. After providing some basic dynamical analysis, an electronic circuit was implemented, and the phase trajectory in the oscilloscope agrees with the numerical simulation. Further exploration shows that this unique chaotic system has superior performance as a random number generator or in voice encryption application.
Risk of Severe Adverse Maternal and Neonatal Outcomes in Deliveries with Repeated and Primary Cesarean Deliveries versus Vaginal Deliveries: A Cross-Sectional Study
Authors: Kongwattanakul Kiattisak , Thamprayoch Rungroj , Kietpeerakool Chumnan , Lumbiganon Pisake
Published: May 2020

Objective. To determine risks of severe adverse maternal and neonatal outcomes in women with repeated cesarean delivery (CD) and primary CD compared with those with vaginal delivery (VD). Methods. Data of this cross-sectional study were extracted from 2,262 pregnant women who gave birth between August 2014 and December 2016, at Srinagarind Hospital, Khon Kaen University. Severe maternal outcomes were categorized based on the World Health Organization criteria. Adjusted odds ratio (aOR) and 95% confidence intervals (CI) were calculated to indicate the risk of severe adverse maternal and neonatal outcomes among women underwent CD compared with those who underwent VD. Results. There were no cases of maternal death in this study. CD significantly increased risk of severe adverse maternal outcomes (SMO) (aOR 10.59; 95% CI, 1.19-94.54 for primary CD and aOR 17.21; 95% CI, 1.97-150.51 for repeated CD) compared with women who delivered vaginally. When compared with vaginal delivery, the risks of neonatal near miss (NNM) and severe adverse neonatal outcomes (SNO) were significantly higher in primary CD group (aOR 1.71; 95% CI 1.17-2.51 and aOR 1.66; 95% CI 1.14-2.43), respectively. For repeated CD, the risks were borderline significant (aOR, 1.58; 95% CI, 0.98-2.56 for NNM and aOR, 1.61; 95% CI, 0.99-2.60 for SNO). Conclusion. Primary and repeated CD significantly increased the risk of SMO compared with VD. Risks of NNM and SNO were also significantly increased in women with primary CD. The risks of NNM and SNO for repeated CD trended toward a significant increase.
A Premalignant Cell-Based Model for Functionalization and Classification of PTEN Variants
Authors: Chao Jesse T. , Hollman Rocio , Meyers Warren M. , Meili Fabian , Matreyek Kenneth A. , Dean Pamela , Fowler Douglas M. , Haas Kurt , Roskelley Calvin D. , Loewen Christopher J.R.
Published: May 2020

As sequencing becomes more economical, we are identifying sequence variations in the population faster than ever. For disease-associated genes, it is imperative that we differentiate a sequence variant as either benign or pathogenic, such that the appropriate therapeutic interventions or surveillance can be implemented. PTEN is a frequently mutated tumor suppressor that has been linked to the PTEN hamartoma tumor syndrome. Although the domain structure of PTEN and the functional impact of a number of its most common tumor-linked mutations have been characterized, there is a lack of information about many recently identified clinical variants. To address this challenge, we developed a cell-based assay that utilizes a premalignant phenotype of normal mammary epithelial cells lacking PTEN. We measured the ability of PTEN variants to rescue the spheroid formation phenotype of PTEN−/− MCF10A cells maintained in suspension. As proof of concept, we functionalized 47 missense variants using this assay, only 19 of which have clear classifications in ClinVar. We utilized a machine learning model trained with annotated genotypic data to classify variants as benign or pathogenic based on our functional scores. Our model predicted with high accuracy that loss of PTEN function was indicative of pathogenicity. We also determined that the pathogenicity of certain variants may have arisen from reduced stability of the protein product. Overall, this assay outperformed computational predictions, was scalable, and had a short run time, serving as an ideal alternative for annotating the clinical significance of cancer-associated PTEN variants.

Significance:

Combined three-dimensional tumor spheroid modeling and machine learning classifies PTEN missense variants, over 70% of which are currently listed as variants of uncertain significance.
The green puzzle Stichococcus (Trebouxiophyceae, Chlorophyta): New generic and species concept among this widely distributed genus

Authors: PRÖSCHOLD THOMAS , DARIENKO TATYANA
Published: May 2020

Phylogenetic analyses have revealed that the traditional order Prasiolales, which contains filamentous and pseudoparenchymatous genera Prasiola and Rosenvingiella with complex life cycle, also contains taxa of more simple morphology such as coccoids like Pseudochlorella and Edaphochlorella or rod-like organisms like Stichococcus and Pseudostichococcus (called Prasiola clade of the Trebouxiophyceae). Recent studies have shown a high biodiversity among these organisms and questioned the traditional generic and species concept. We studied 34 strains assigned as Stichococcus, Pseudostichococcus, Diplosphaera and Desmocococcus. Phylogenetic analyses using a multigene approach revealed that these strains belong to eight independent lineages within the Prasiola clade of the Trebouxiophyceae. For testing if these lineages represent genera, we studied the secondary structures of SSU and ITS rDNA sequences to find genetic synapomorphies. The secondary structure of the V9 region of SSU is diagnostic to support the proposal for separation of eight genera. The complex taxonomic history was summarized and revised. The ITS-2/CBC approach was used for species delimitation. Considering all these results, we revised the genera Stichococcus, Pseudostichococcus, Diplosphaera and Desmococcus and proposed four new genera and four new species for the science community. The usage of the V9 region and the ITS-2 barcodes discovered potential new species among the Stichococcus-like organisms in culture-independent studies.
Spatially Distant but Emotionally Close: A Personal Reflection on Psychoanalytic Distance Training
Authors: Moshtagh Nahaleh
Published: May 2020

Psychoanalytic training in countries with no accredited training program or official link to the international psychoanalytic community is possible only through technology. Serious controversies surround the legitimacy of distance training, based primarily on doubts about the efficacy of distance analysis. Curiously, in a discipline that prides itself on being a “talking cure,” an emphasis on the embodied presence of the analytic couple emerges in the literature over and over again. In fact, though, it’s through the use of language—and not through sight—that we achieve the aim of analysis. The information the analyst gathers from bodily presence is important, but even then the analyst’s job is to hear the patient’s unconscious communications. If an analyst, for whatever reason, is unable to hear the patient’s voice and unconscious communications, the analysis is bound to fail whether the patient is in the room or on the screen/phone. Being spatially distant poses no contradictions to psychoanalysis, as long as both parties—patient and analyst, supervisee and supervisor, candidate and instructor—are willing to listen, hear, and be heard emotionally.
Combined Training of Candidates, Scholars, And Psychotherapists: A Model of Psychoanalytic Education for the Twenty-First Century
Authors: Fritsch Richard C. , Winer Robert
Published: May 2020

A new model of psychoanalytic education is proposed that will meet the challenges of educating candidates in a new century. Prospective candidates have varying opinions about the value of analytic training, opinions that reflect economic and cultural conditions different from those facing previous generations. Overall, today’s graduate-level students hold less favorable attitudes toward psychoanalysis than did their counterparts in the past. The proposed model calls for combining analytic candidates, psychotherapy students, and academic scholars for two years in a Psychoanalytic Studies Program (PSP), after which candidates take their subsequent years of training in a cohort made up exclusively of analytic candidates. A curriculum that focuses on the core concepts in psychoanalysis allows students in all three categories to learn the foundational knowledge of psychoanalysis that once was widely taught in graduate mental health programs. The philosophy that underlies the model and the structure and orientation of the course sequences are presented. Implementatiion of the model having shown positive results, its strengths and limitations are evaluated against the traditional model, in which candidates and psychotherapy students are educated separately.