Miracle-dz/UniFAR-FTUs huggingface dataset:
How do supermarkets and discounters communicate about sustainability? A comparative analysis of sustainability reports and in-store communication

Purpose Commonly, supermarkets are perceived as more sustainable than discount stores, which are accused of following an aggressive price and no-frills approach. Therefore, the purpose of this paper is to investigate whether supermarkets and discounters differ substantially in their sustainability communication. Design/methodology/approach Sustainability reports and in-store communication are two important channels for retailers’ sustainability communication. To analyze both communication channels, the authors use a multi-method approach with data triangulation, analyzing sustainability reports and store observations of eight German retailers (two supermarket chains, six discount chains). Findings The study reveals no major differences between supermarkets and discounters regarding the readability of sustainability reports and the number of key figures on sustainability presented. However, supermarkets perform significantly better in translating sustainability to the store level than discounters. Furthermore, the results indicate that poor quality in the readability analysis is reflected in fewer concrete data provided in the sustainability reports and poorer translation of sustainability to the retail store. Originality/value This paper presents an empirical analysis of how well German retailers communicate about sustainability on both the report and the store level for the interest of academia and retail managers. It reveals different performance qualities among retail chains and retail formats and identifies the shortcomings within current reporting legislation with a clear indication toward policy makers.
The Association between Impression Management and Financial Performance in the Boards of Directors’ Annual Reports for Egyptian listed companies: The Moderating Effect of the CEO’s Power

This study extends the previous study about the disclosure narrative field through examining the bidirectional association between Disclosure Tone (DP) and Financial Performance (FP) in respect of the Egyptian EGX 100 listed companies during the period from 2013 to 2017. Further, this study seeks to explore the expected effect of the CEO’s power on the association between DT and FP in the Egyptian context. This study ran twelve Ordinary Least Square (OLS) models to test the research hypotheses. Further, it calculated the number of the statements, related to the disclosure tone in the board of directors’ annual reports, through manual analysis of their content. In addition, this study used Tobin’s Q method to measure FP. CEO power was captured by using an aggregated index which included six CEO power variables in the Egyptian environment. The findings reveal that there is a bidirectional relationship between DT and FP. Moreover, CEO power has a significant effect on the association between DT and FP. Due to few studies having examined the bidirectional between DT and FP in the developing countries, this study contributes to the accounting field by examining such a construct in the Egyptian context.
The Development of An Integrated Decision Model for Public Investment Projects in Thailand

Research and empirical evidence show that public investments, particularly in developing countries, often struggle or fail to satisfy their objectives and to response to the needs of local community. A main reason for that failure is the way how the decision-making process is carried out to select public investment projects. Despite the comprehensive discipline of decision-making, the current procedure in practice remains lacking strong foundation in its approach. In this research, the current state of public investment decision was investigated in Thailand as a representative case of public investment in developing countries. The analysis shows that the project evaluation and selection procedure in Thailand is unstructured and opaque. Moreover the decisions are mostly made based on a one-sided economic basis and hence neglect other important objectives that public projects supposed to meet. This research proposes an integrated decision model (IDM) for evaluating and selecting public investment projects which addresses those shortcomings and consequently improves the process. The IDM integrates various evaluation and decision-making approaches, suitable for addressing not only economic, but also social, environmental and project support attributes, into one decision model. The IDM and its methodology is designed to be applied to any type of projects and organisations. With this function, two existing investment projects in Thailand have been applied through the entire methodology to test the adaptability and flexibility. The final results of the model have been reviewed by using sensitivity analysis to test the robustness of the selected choice. Moreover opinions from relevant experts in various fields and organisations were collected for further implementation.
Carry-on Baggage Fee, Airline Pricing and Subcontracting

In 2010, Spirit airlines announced that it would start charging passengers for carry-on baggage. Using a vector of route level characteristics, we construct a matched group consisting of routes which best match those served by Spirit (treated group). We then run a diff-in-diff estimation using the treated and matched group, and examine the impact of Spirit's baggage fee policy on its rivals' ticket prices. Our results show that Spirit's rivals reduce their prices by about 5.8% after Spirit charges carry-on baggage fee. We also take into account the subcontracting status of Spirit's rivals, i.e., whether they subcontract operations to regional airlines. The results suggest that price reduction mainly comes from major carriers which subtract to regional carriers. In particular, price reduction by non-subcontracting carriers is small and insignificant, and price reduction by subcontracting carriers is large and remains significant. We also discuss how the significant price reduction on subcontracting routes may negatively impact regional carriers.
The Effects of the 1930s HOLC 'Redlining' Maps

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment. (JEL G21, J15, N32, N42, N92, R23, R31)
A Survey on Time Series Online Sequential Learning Algorithms

Time-series analysis is extremely important in various fields. In this survey paper, an overview of existing Online Sequential Learning Algorithms for time series modeling and analysis will be provided. Because of their good capability of approximation, researchers are looking to work forward with Artificial Neural Networks. Time series is a very important part of data sets which are temporal in nature and they are found abundantly in various applications. We provide different existing Online Sequential algorithms and for each category, we identify the advantages and disadvantages of the techniques in that category. We have done a survey on the time series algorithms by including various recent papers. In this survey paper, we are also going to show the Multiple Model Switching and Tuning approach (MMST) description since it increases the accuracy of the model.
Annual Report Commentary on the Value Creation Process

We measure annual report commentary articulating entities’ business model and strategy, and then examine the capital market effects of enhancing such disclosure. Our empirical disclosure proxy is based on n-grams drawn from popular strategy textbooks and the academic strategy literature. Validation tests confirm that our score: (a) correlates with manual classifications of the quality of strategy-focused disclosures produced by domain experts; (b) covaries predictably with firm-level drivers of strategy-focused disclosures identified by prior research; and (c) captures the structural break in reporting associated with the regulatory mandate for a subset of London Stock Exchange firms to explain their strategy and business model. Tests using this exogenous and measurable increase in strategy-focused disclosure show that enhanced commentary on strategy and business model is associated with lower investor uncertainty. We also find support for an increase in the speed at which information is incorporated into stock price following the annual report release.
Syntactic and semantic features for statistical and neural machine translation

Machine Translation (MT) for language pairs with long distance dependencies and word reordering, such as German–English, is prone to producing output that is lexically or syntactically incoherent. Statistical MT (SMT) models used explicit or latent syntax to improve reordering, however failed at capturing other long distance dependencies. This thesis explores how explicit sentence-level syntactic information can improve translation for such complex linguistic phenomena. In particular, we work at the level of the syntactic-semantic interface with representations conveying the predicateargument structures. These are essential to preserving semantics in translation and SMT systems have long struggled to model them. String-to-tree SMT systems use explicit target syntax to handle long-distance reordering, but make strong independence assumptions which lead to inconsistent lexical choices. To address this, we propose a Selectional Preferences feature which models the semantic affinities between target predicates and their argument fillers using the target dependency relations available in the decoder. We found that our feature is not effective in a string-to-tree system for German→English and that often the conditioning context is wrong because of mistranslated verbs. To improve verb translation, we proposed a Neural Verb Lexicon Model (NVLM) incorporating sentence-level syntactic context from the source which carries relevant semantic information for verb disambiguation. When used as an extra feature for reranking the output of a German→English string-to-tree system, the NVLM improved verb translation precision by up to 2.7% and recall by up to 7.4%. While the NVLM improved some aspects of translation, other syntactic and lexical inconsistencies are not being addressed by a linear combination of independent models. In contrast to SMT, neural machine translation (NMT) avoids strong independence assumptions thus generating more fluent translations and capturing some long-distance dependencies. Still, incorporating additional linguistic information can improve translation quality. We proposed a method for tightly coupling target words and syntax in the NMT decoder. To represent syntax explicitly, we used CCG supertags, which encode subcategorization information, capturing long distance dependencies and attachments. Our method improved translation quality on several difficult linguistic constructs, including prepositional phrases which are the most frequent type of predicate arguments. These improvements over a strong baseline NMT system were consistent across two language pairs: 0.9 BLEU for German→English and 1.2 BLEU for Romanian→English.
Global loudness processing of time-varying sounds

The global loudness evaluation of a time-varying sound is based on the transformation of our momentary loudness sensations into a single scalar value that reflects our overall impression a posteriori. The aim of this thesis is to deepen the understanding of this dynamical process in the case of specific stimuli that rise or fall in level over a few seconds. Starting from the observation that rising tones are perceived louder than falling tones while both contain the same energy over the same duration, this thesis is structured around four experimental chapters that scrutinize the origins of this perceptual asymmetry from various perspectives. Chapter 1 explores the extent to which this asymmetry relies on contextual or methodological factors. Various measurement procedures and contexts of presentation yielding comparable results, evidence is provided that a steady and robust mechanism is at the basis of this perceptual asymmetry. In Chapter 2, the influence of the sound spectral structure is examined and it is shown that asymmetries are significantly diminished when broadband noises rather than tonal stimuli are used. Chapter 3 examines the effects of the temporal profile characteristics of the stimuli (slope, duration, dynamics) on their global loudness judgments. Results indicate that listeners’ judgments rely on a temporal integration of the loudest portion but that other mechanisms are involved; asymmetries whose magnitude depends on the profile characteristics and the level of the sounds in a complex and nonlinear manner are found. In Chapter 4, the global loudness evaluation process is examined locally in a loudness discrimination task. The respective contributions of the different temporal portions of rising and falling-intensity stimuli as well as their interactions are derived from reverse-correlation analyses. First-order analyses show that listeners exclusively focus on the loudest portions of these sounds and that similar temporal weighting patterns apply to rising and falling profiles; second-order analyses indicate the presence of nonlinear components in the integration process that have never been reported so far. Current loud- ness models cannot directly account for the effects highlighted throughout these studies, which supports the idea that the mechanism(s) under study might take place in high auditory or decisional stages not considered in these models. From the use of very basic stimuli, this work brings to light key temporal mechanisms in global loudness processing that might be shared with other sensory attributes. This research offers new perspectives to investigate the coding and the processing of time-varying stimuli by sensory systems from identified perceptual asymmetries.
A heuristics with simulative approach for the determination of the optimal offsetting replenishment cycles to reduce the warehouse space

An important issue in a supply chain is to manage carefully replenishment cycles of stored items because of its strong impact both on production and stock management costs. The literature offers several papers debating this issue with a recent focus on the possibility to offset the items inventory cycles in order to reduce the maximum required space. A genetic algorithm (GA) is proposed in order to determine the optimal offsetting inventory cycles of items stored in the same warehouse. The heuristics was validated through the comparison with the most adopted and cited methodology, showing the effectiveness of the GA, able to provide better results than those previously presented in the literature. The GA was finally applied to a realistic case of a production system, showing a minimization of the maximum peak in the time horizon without relevant additional costs, together with a higher and more regular saturation of the warehouse.
Development of technology for state registration signs of the vehicles recognition in conditions of low resolution on the basis of gradient busting and convolutional neural networks

This paper presents the technology of characters on the vehicle license plate recognition. The Viola-Jones method is used for distinguishing the area of the license plate on the basis of pre-trained Haar cascades. Convolutional neural networks analyzing the selected frames are used for recognition. Training procedure of neural networks is carried out through the created synthetic database containing characters with various types of distortion. The efficiency of neural network training was achieved due to the possibility of a sampling formation with the almost unlimited dimension, modeling of distortions which are rarely found in a real sampling due to its limitations, the possibility of forming a mixed sampling including both synthetic and real data. The necessary methods of augmentation and preprocessing for synthetic and real data are implemented, based on the resolution of the video camera. The results obtained in this work can be used for character recognition in conditions of low resolution of the video camera, distortions and lack of initial data.
Optimizing Information Freshness in Computing-Enabled IoT Networks

Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually need to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this article, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing-enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min–max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and the effectiveness of the proposed algorithm are verified with extensive simulation results.
Lower large deviations for geometric functionals

This work develops a methodology for analyzing large-deviation lower tails associated with geometric functionals computed on a homogeneous Poisson point process. The technique applies to characteristics expressed in terms of stabilizing score functions exhibiting suitable monotonicity properties. We apply our results to clique counts in the random geometric graph, intrinsic volumes of Poisson-Voronoi cells, as well as power-weighted edge lengths in the random geometric, $k$-nearest neighbor and relative neighborhood graph.
Characterizing Deep Learning Training Workloads on Alibaba-PAI

Modern deep learning models have been exploited in various domains, including computer vision (CV), natural language processing (NLP), search and recommendation. In practical AI clusters, workloads training these models are run using software frameworks such as TensorFlow, Caffe, PyTorch and CNTK. One critical issue for efficiently operating practical AI clouds, is to characterize the computing and data transfer demands of these workloads, and more importantly, the training performance given the underlying software framework and hardware configurations. In this paper, we characterize deep learning training workloads from Platform of Artificial Intelligence (PAI) in Alibaba. We establish an analytical framework to investigate detailed execution time breakdown of various workloads using different training architectures, to identify performance bottleneck. Results show that weight/gradient communication during training takes almost 62% of the total execution time among all our workloads on average. The computation part, involving both GPU computing and memory access, are not the biggest bottleneck based on collective behavior of the workloads. We further evaluate attainable performance of the workloads on various potential software/hardware mappings, and explore implications on software architecture selection and hardware configurations. We identify that 60% of PS/Worker workloads can be potentially sped up when ported to the AllReduce architecture exploiting the high-speed NVLink for GPU interconnect, and on average 1.7X speedup can be achieved when Ethernet bandwidth is upgraded from 25 Gbps to 100 Gbps.
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM

The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference systems. Due to the increasing memory intensity of most DNN workloads, main memory can dominate the system's energy consumption and stall time. One effective way to reduce the energy consumption and increase the performance of DNN inference systems is by using approximate memory, which operates with reduced supply voltage and reduced access latency parameters that violate standard specifications. Using approximate memory reduces reliability, leading to higher bit error rates. Fortunately, neural networks have an intrinsic capacity to tolerate increased bit errors. This can enable energy-efficient and high-performance neural network inference using approximate DRAM devices. Based on this observation, we propose EDEN, the first general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy. EDEN relies on two key ideas: 1) retraining the DNN for a target approximate DRAM device to increase the DNN's error tolerance, and 2) efficient mapping of the error tolerance of each individual DNN data type to a corresponding approximate DRAM partition in a way that meets the user-specified DNN accuracy requirements. We evaluate EDEN on multi-core CPUs, GPUs, and DNN accelerators with error models obtained from real approximate DRAM devices. We show that EDEN's DNN retraining technique reliably improves the error resiliency of the DNN by an order of magnitude. For a target accuracy within 1% of the original DNN, our results show that EDEN enables 1) an average DRAM energy reduction of 21%, 37%, 31%, and 32% in CPU, GPU, and two different DNN accelerator architectures, respectively, across a variety of state-of-the-art networks, and 2) an average (maximum) speedup of 8% (17%) and 2.7% (5.5%) in CPU and GPU architectures, respectively, when evaluating latency-bound neural networks.
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays

We propose a new algorithm for adversarial multi-armed bandits with unrestricted delays. The algorithm is based on a novel hybrid regularizer applied in the Follow the Regularized Leader (FTRL) framework. It achieves $\mathcal{O}(\sqrt{kn}+\sqrt{D\log(k)})$ regret guarantee, where $k$ is the number of arms, $n$ is the number of rounds, and $D$ is the total delay. The result matches the lower bound within constants and requires no prior knowledge of $n$ or $D$. Additionally, we propose a refined tuning of the algorithm, which achieves $\mathcal{O}(\sqrt{kn}+\min_{S}|S|+\sqrt{D_{\bar S}\log(k)})$ regret guarantee, where $S$ is a set of rounds excluded from delay counting, $\bar S = [n]\setminus S$ are the counted rounds, and $D_{\bar S}$ is the total delay in the counted rounds. If the delays are highly unbalanced, the latter regret guarantee can be significantly tighter than the former. The result requires no advance knowledge of the delays and resolves an open problem of Thune et al. (2019). The new FTRL algorithm and its refined tuning are anytime and require no doubling, which resolves another open problem of Thune et al. (2019).