“No” and “net” as response tokens in English and Russian business discourse: In search of a functional equivalence

The literature on English suggests that turn-initial no fulfils a variety of discourse-pragmatic functions beyond its use as a negative response to polar questions. We cannot assume that the same range or distribution of functions is realised by its nearest Russian equivalent, net . Hence, investigating the contrasts and similarities in the nomenclature and distribution of functions of no and net should pose an important research problem for various discourses, and especially for business discourse with its focus on goal-orientation and productive interpersonal relations requiring adequate interlingual interaction. The study examines how no and net occur in two corpora of spoken business/professional discourse in order to establish their functional comparability and reveal the differences in their use. The article draws on data from the Cambridge and Nottingham Spoken Business English Corpus and the Russian National Corpus analysed using a combination of corpus linguistics, conversation analysis and discourse analytical approaches. Study results show some overlap between the functions of the response particles in English and Russian, and some differences. The findings suggest that no / net display a number of functions connected with conversational continuity, topic management, turn-taking and hedging. The distribution and functions of no/net in the English and Russian data are similar, with the Russian data showing a preference for floor-grabbing no -initiated turns. Translation equivalence is not always fully applicable between no and net . A mixed methodology generates results which suggest that fruitful insights can be gained from English and Russian corpus data. The issues of the use of no and нет in English and Russian business discourses can be further investigated using the suggested data and conclusions.

A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

Image Speckle Denoising for Securing Internet of Smart Sensors

How to improve utility performance when securing sensitive data is an important research problem in Internet of smart sensors. In this paper, we study secured image speckle denoising for networked synthetic aperture radar (SAR). Speckle noise of SAR affects image quality and has a great influence on target detection and recognition. MSTAR dataset is often used in image target recognition. In this paper, a subregion-based method is proposed in order to improve the accuracy of target recognition and better retain target information while filtering and denoising the image. The new method applies advanced encryption techniques to protect sensitive data against malicious attack. Firstly, the image is divided into marked areas and unmarked areas through edge extraction and hole filling. Secondly, we use different size windows and filtering methods to filter the image in different areas. The experimental results show that the proposed algorithm has obvious advantages over MR-NLM, SSIM-NLM, Frost, and BM3D filtering in terms of equivalent view number and preserving edge and structure.

Advanced exact synthesis of Clifford+T circuits

Quantum systems provide a new way of conducting computations based on the so-called qubits. Due to the potential for significant speed-ups, this field received significant research attention in recent years. The Clifford+T library is a very promising and popular gate library for these kinds of computations. Unlike other libraries considered so far, it consists of only a small number of gates for all of which robust, fault-tolerant realizations are known for many technologies that seem to be promising for large-scale quantum computing. As a consequence, (logic) synthesis of Clifford+T quantum circuits became an important research problem. However, previous work in this area has several drawbacks: Corresponding approaches are either only applicable to very small quantum systems or lead to circuits that are far from being optimal. The latter is mainly caused by the fact that current synthesis realizes the desired circuit by a local, i.e., column-wise, consideration of the underlying unitary transformation matrix to be synthesized. In this paper, we analyze the conceptual drawbacks of this approach and propose to overcome them by taking a global view of the matrices and perform a separation of concerns regarding individual synthesis steps. We precisely describe a corresponding algorithm as well as its efficient implementation on top of decision diagrams. Experimental results confirm the resulting benefits and show improvements of up to several orders of magnitudes in costs compared to previous work.

Topic Jerk Detector: Detection of Tweet Bursts Related to the Fukushima Daiichi Nuclear Disaster

In recent disaster situations, social media platforms, such as Twitter, played a major role in information sharing and widespread communication. These situations require efficient information sharing; therefore, it is important to understand the trends in popular topics and the underlying dynamics of information flow on social media better. Developing new methods to help us in these situations, and testing their effectiveness so that they can be used in future disasters is an important research problem. In this study, we proposed a new model, “topic jerk detector.” This model is ideal for identifying topic bursts. The main advantage of this method is that it is better fitted to sudden bursts, and accurately detects the timing of the bursts of topics compared to the existing method, topic dynamics. Our model helps capture important topics that have rapidly risen to the top of the agenda in respect of time in the study of specific social issues. It is also useful to track the transition of topics more effectively and to monitor tweets related to specific events, such as disasters. We attempted three experiments that verified its effectiveness. First, we presented a case study applied to the tweet dataset related to the Fukushima disaster to show the outcomes of the proposed method. Next, we performed a comparison experiment with the existing method. We showed that the proposed method is better fitted to sudden burst accurately detects the timing of the bursts of the topic. Finally, we received expert feedback on the validity of the results and the practicality of the methodology.

Consistent Correspondences for Shape and Image Problems

Establish consistent correspondences between different objects is a classic problem in computer science/vision. It helps to match highly similar objects in both 3D and 2D domain. Inthe 3D domain, finding consistent correspondences has been studying for more than 20 yearsand it is still a hot topic. In 2D domain, consistent correspondences can also help in puzzlesolving. However, only a few works are focused on this approach. In this thesis, we focuson finding consistent correspondences and extend to develop robust matching techniques inboth 3D shape segments and 2D puzzle solving. In the 3D domain, segment-wise matching isan important research problem that supports higher-level understanding of shapes in geometryprocessing. Many existing segment-wise matching techniques assume perfect input segmentation and would suffer from imperfect or over-segmented input. To handle this shortcoming,we propose multi-layer graphs (MLGs) to represent possible arrangements of partially mergedsegments of input shapes. We then adapt the diffusion pruning technique on the MLGs to findconsistent segment-wise matching. To obtain high-quality matching, we develop our own voting step which is able to remove inconsistent results, for finding hierarchically consistent correspondences as final output. We evaluate our technique with both quantitative and qualitativeexperiments on both man-made and deformable shapes. Experimental results demonstrate theeffectiveness of our technique when compared to two state-of-art methods. In the 2D domain,solving jigsaw puzzles is also a classic problem in computer vision with various applications.Over the past decades, many useful approaches have been introduced. Most existing worksuse edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. Weobserve that most existing techniques cannot be easily extended to puzzles with rectangularpieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. We propose new matching approaches based on sub-edges/corners, modelledusing the MatchLift or diffusion framework to solve square puzzles with cycle consistency.We demonstrate the robustness of our approaches by comparing our methods with state-of-artmethods. We also show how puzzles with rectangular pieces of arbitrary sizes, or puzzles withtriangular and square pieces can be solved by our techniques.

Od męki translacji do piekła logosu. O przekładzie w „Przekładzie” Pabla De Santisa

The article presents how the novel The Translation by Pablo De Santis reffers to the most important concepts of theory and philosophy of translation. Among these concepts the most significant are those which consider the boundaries and mechanisms of interpretation in the act of translation. These ideas are metaphorically expressed by the myth of the fall of the Tower of Babel. The interpretative context for the novel by De Santis is determined by the works of Jacques Derrida, Julia Kristeva and Hans-Georg Gadamer. An important research problem is also the relationship between the category of translation and the hybrid genre of the crime novel by De Santis.

GUILLAIN — BARRÉ SYNDROME AS A RELEVANT ISSUE OF NEUROLOGY (A LITERATURE REVIEW)

Acute infl ammatory polyneuropathy is an important research problem of modern neurology. Guillain — Barré syndrome is a severe form of acute polyneuropathy, which is based on autoimmune infl ammation of the myelin sheath of roots and peripheral nerves. Guillain — Barré syndrome is an example of one of the most severe diseases of the nervous system, in which timely diagnosis, proper therapy and qualifi ed care facilitate the achievement of the full recovery of lost functions in most patients. Following an extensive review of Russian and foreign literature, this article discusses modern concepts of Guillain — Barré syndrome, in particular questions related to its epidemiology, etiopathogenesis, classifi cation, clinical features, diagnosis, treatment and prognosis.

Control of separately excited DC motor with series multi-cells chopper using PI - Petri nets controller

The separately excited Direct Current (DC) motor is widely used in many industrial sectors. During the operation of the DC motor, the load torque and the voltage of the network can cause a destabilization of the actual speed and actual current. Thus, the need to regulate the speed and current of the DC motor is a very important research problem. In this paper, a control strategy of separately excited DC motor using a series multi-cells chopper is described. The proposed control is based on Proportional-Integral (PI) and Petri nets controllers. Specifically, the conventional PI controller is used to control the speed of DC motor. The Petri nets controller ensures the regulation of the armature current and to maintain the capacitor voltage of the multi-cells converter to its reference. The Petri nets controller also generates binary control switches. The proposed control system has been implemented using MATLAB Sim Power. Simulation results demonstrate that a series multi-cells chopper and the proposed control give a good performance and high robustness in load disturbance for the separately excited DC motor.

Variants of the organization in transport in the distribution system

Distribution occupies a significant place in the elements of the logistics chain, because its main task is to meet the expectations set by the customer. The decisions regarding the method of selling goods made in enterprises can be classified as strategic. Their direct consequence is the company's economic effects. The article analyzed the popularity of transport in the distribution system and developed variant ways of delivering goods for a specific group of goods. An important research problem is the identification of distribution channels, which gather dependent and interacting organizations involved in the process of meeting the requirements of the buyer. An unambiguous assessment of the choice of distribution system depends on many criteria, which depending on the demand may be different. The most common criterion used to select a carriage is the time and cost of the task. The specificity of distribution systems shows that full-truck and groupage systems are the most often chosen.