NAMES OF PENALTIES IN REPUBLIC OF KOREA’S PENAL CODE IN KOREAN-POLISH TRANSLATION

W niniejszym artykule podjęto się próby tłumaczenia prawniczego w odniesieniu do koreańsko-polskiej pary językowej w zakresie prawa karnego materialnego, a dokładniej mówiąc – w zakresie nazewnictwa kar kryminalnych. Posłużono się tekstem obecnie obowiązującego koreańskiego kodeksu karnego, a tłumaczenie polegało na porównaniu tekstów paralelnych, czyli takich, które istnieją niezależnie od siebie nawzajem (jeden nie jest tłumaczeniem drugiego), ale pełnią te same funkcje, z tym że służą użytkownikom różnych języków (Neubert i Shreve 1992, 89). Kodeks Karny Republiki Korei i polska ustawa Kodeks Karny to teksty paralelne, gdyż pełnią tę samą rolę w swojej kulturze prawnej i nie są jednocześnie tłumaczeniami. Przy pracy nad tłumaczeniem okazało się przydatne także posiłkowanie się innymi polskimi ustawami – Kodeksem Wykroczeń, a także nieobowiązującym już Kodeksem Karnym z 1969 roku. Celem artykułu nie było jedynie dokonanie tłumaczenia, ale także przedstawienie chociażby w małym stopniu informacji na temat współczesnego prawa karnego w Korei Południowej. Uznano tę kwestię za istotną ze względu na fakt, że na próżno szukać polskich opracowań dotyczących tej tematyki. W artykule, w całości napisanym w języku polskim, koniecznym było jednak posługiwanie się oryginalnymi terminami koreańskimi, które zostały przedstawione zarówno w pisowni oryginalnej (hangul), jak i za pomocą transkrypcji, ze względu na czytelnika nie znającego języka koreańskiego. Zastosowana transkrypcja to transkrypcja McCune'a – Reischauera (Ogarek – Czoj, 2007, 30). Artykuł jest de facto streszczeniem mojej pracy licencjackiej o tym samym tytule, obronionej w 2013 roku na Wydziale Neofilologii Uniwersytetu im. Adama Mickiewicza w Poznaniu.The article was written with the aim of translating the names of criminal penalties in the Republic of Korea’s Penal Code to the Polish language. The text of the current Korean Penal Code has been compared with the pararell text of the Polish Penal Code. Although both Penal Codes have similar functions, the scope of application is slightly different, so the Polish Code of Petty Crimes and the previous Polish Penal Code (from the year 1969) were used subsidiary. However, the article was written not only for translatory purposes, but also to initially present an image of South Korean penal law to the Polish readers since Poland is deficient in such papers. Even though the article is written in Polish, it was essential to use original Korean names of criminal penalties. Due to Koreans using a different alphabet, the names had to be transcripted according to the rules of McCune-Reischauer (Ogarek-Czoj, 2007, 30), to make the Korean words legible for non-Korean readers. The paper is, de facto, a shortened version of my BA paper of the same title

Finding the Optimal Network Depth in Classification Tasks

We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads. By allowing the model to determine the importance of each head and rewarding the choice of a single shallow classifier, we are able to detect and remove unneeded components of the network. This operation, which can be seen as finding the optimal depth of the model, significantly reduces the number of parameters and accelerates inference across different hardware processing units, which is not the case for many standard pruning methods. We show the performance of our method on multiple network architectures and datasets, analyze its optimization properties, and conduct ablation studies

Exploiting Transformer Activation Sparsity with Dynamic Inference

Transformer models, despite their impressive performance, often face practical limitations due to their high computational requirements. At the same time, previous studies have revealed significant activation sparsity in these models, indicating the presence of redundant computations. In this paper, we propose Dynamic Sparsified Transformer Inference (DSTI), a method that radically reduces the inference cost of Transformer models by enforcing activation sparsity and subsequently transforming a dense model into its sparse Mixture of Experts (MoE) version. We demonstrate that it is possible to train small gating networks that successfully predict the relative contribution of each expert during inference. Furthermore, we introduce a mechanism that dynamically determines the number of executed experts individually for each token. DSTI can be applied to any Transformer-based architecture and has negligible impact on the accuracy. For the BERT-base classification model, we reduce inference cost by almost 60%

Application of Black-Bridge Satellite Imagery for the Spatial Distribution of Salvage Cutting in Stands Damaged by Wind

Salvage logging is performed to remove the fallen and damaged trees after a natural disturbance, e.g., fire or windstorm. From an economic point of view, it is desirable to remove the most valuable merchantable timber, but usually, the process depends mainly on topography and distance to forest roads. The objective of this study was to evaluate the suitability of the Black-Bridge satellite imagery for the spatial distribution of salvage cutting in southern Poland after the severe windstorm in July 2015. In particular, this study aimed to determine which factors influence the spatial distribution of salvage cutting. The area of windthrow and the distribution of salvage cutting (July–August 2015 and August 2015–May 2016) were delineated using Black-Bridge satellite imagery. The distribution of the polygons (representing windthrow and salvage cutting) was verified with maps of aspect, elevation and slope, derived from the Digital Terrain Model and the distance to forest roads, obtained from the Digital Forest Map. The analysis included statistical modelling of the relationships between the process of salvage cutting and selected geographical and spatial features. It was found that the higher the elevation and the steeper the slope, the lower the probability of salvage cutting. Exposure was also found to be a relevant factor (however, it was difficult to interpret) as opposed to the distance to forest roads

Application of Black-Bridge Satellite Imagery for the Spatial Distribution of Salvage Cutting in Stands Damaged by Wind

Salvage logging is performed to remove the fallen and damaged trees after a natural disturbance, e.g., fire or windstorm. From an economic point of view, it is desirable to remove the most valuable merchantable timber, but usually, the process depends mainly on topography and distance to forest roads. The objective of this study was to evaluate the suitability of the Black-Bridge satellite imagery for the spatial distribution of salvage cutting in southern Poland after the severe windstorm in July 2015. In particular, this study aimed to determine which factors influence the spatial distribution of salvage cutting. The area of windthrow and the distribution of salvage cutting (July–August 2015 and August 2015–May 2016) were delineated using Black-Bridge satellite imagery. The distribution of the polygons (representing windthrow and salvage cutting) was verified with maps of aspect, elevation and slope, derived from the Digital Terrain Model and the distance to forest roads, obtained from the Digital Forest Map. The analysis included statistical modelling of the relationships between the process of salvage cutting and selected geographical and spatial features. It was found that the higher the elevation and the steeper the slope, the lower the probability of salvage cutting. Exposure was also found to be a relevant factor (however, it was difficult to interpret) as opposed to the distance to forest roads

Direction is what you need: Improving Word Embedding Compression in Large Language Models

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of these models to improve their inference time and memory footprint. This paper presents a novel loss objective to compress token embeddings in the Transformer-based models by leveraging an AutoEncoder architecture. More specifically, we emphasize the importance of the direction of compressed embeddings with respect to original uncompressed embeddings. The proposed method is task-agnostic and does not require further language modeling pre-training. Our method significantly outperforms the commonly used SVD-based matrix-factorization approach in terms of initial language model Perplexity. Moreover, we evaluate our proposed approach over SQuAD v1.1 dataset and several downstream tasks from the GLUE benchmark, where we also outperform the baseline in most scenarios. Our code is public

Shallow-Water Scavengers of Polar Night and Day – An Arctic Time-Lapse Photography Study

Until recently, polar night constituted truly a “mare incognitum” of our times. Yet, the first records from this very little-explored period showcased a surprisingly rich and active ecosystem. This investigation aims to reveal the level of scavenger activity during both Arctic polar night and day. It compares the shallow-water scavenging fauna observed during two contrasting seasons (winter vs. summer) in a high Arctic fjord (Kongsfjorden, 79° N, Spitsbergen, Svalbard Archipelago). In each of January and July 2015, two different bait types – Atlantic cod (Gadus morhua) and a bird carcass (chicken meat) were deployed at a depth of 12 m. Fauna were monitored remotely using time-lapse cameras equipped with bait traps, with photographs taken every 15 min over a period of 4 days. Thirty taxa were recorded at baits, dominated by lysianassid amphipods (Onisimus sp. 88%, Anonyx sp. 2%, but only during winter), and buccinid gastropods (B. undatum 5%, B. glaciale 1%, Buccinum sp. 3%, in both seasons). In most cases, buccinids were the first animals to appear at bait. The total number of recorded taxa, mean species richness per sampling unit, total abundance and associations among taxa were higher, on average, in winter than in summer deployments, while Pielou’s evenness index showed the opposite pattern. Scavenger assemblages differed significantly between the two seasons and also in response to the two different bait types, with seasonal effects being strongest. Contrary to expectations, bait consumption rates differed very little between the two seasons, being slow in general and only slightly faster in summer (0.05 g of cod bait consumed in 1 min) compared to winter (0.04 g min–1), yielding novel insights into ecological interactions and functions in shallow marine ecosystems during Arctic polar nights

Zero Time Waste: Recycling Predictions in Early Exit Neural Networks

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of a neural network. ICs can quickly return predictions for easy examples and, as a result, reduce the average inference time of the whole model. However, if a particular IC does not decide to return an answer early, its predictions are discarded, with its computations effectively being wasted. To solve this issue, we introduce Zero Time Waste (ZTW), a novel approach in which each IC reuses predictions returned by its predecessors by (1) adding direct connections between ICs and (2) combining previous outputs in an ensemble-like manner. We conduct extensive experiments across various datasets and architectures to demonstrate that ZTW achieves a significantly better accuracy vs. inference time trade-off than other recently proposed early exit methods.Comment: Accepted at NeurIPS 202

Health and safety concerns related to CNT and graphene products, and related composites

The use of Carbon Nanotubes (CNT) and Graphene increased in the last decade and it is likely to keep increasing in the near future. The attractiveness of their properties, particularly the possibility to enhance the composites performance using a tailor made methodology, brings new materials, processes and products for highly demanding industrial applications and to the market. However, there are quite a lot of health/safety issues, as well as lack of understanding and standards to evaluate their effects. This paper starts with a general description of materials, processes and products dealing with CNT and graphene. Then, an overview of concerns related to the health and safety when handling, researching, producing and using products that include these materials is presented. It follows a risk management approach with respect to simulation and evaluation tools, and considering the consensual limits already existing for research, industry and consumers. A general discussion integrating the relevant aspects of health and safety with respect to CNT and graphene is also presented. A proactive view is presented with the intention to contribute with some guidelines on installation, maintenance, evaluation, personal protection equipment (PPE) and personnel training to deal with these carbon-based nanomaterials in research, manufacture, and use with composite materials.This work has received funding from the European Union's Horizon 2020 research and innovation program SMARTFAN under grant agreement No. 760779. IPC authors wish to acknowledge "National Funds through FCT-Portuguese Foundation for Science and Technology, References UIDB/05256/2020 e UIDP/05256/2020; Tania Peixoto acknowledges the financial support from FCT, through the PhD Grant PD/BD/143035/2018