Modelling, analysis and comparison of heatsink designs with improved natural convection

The paper presents FEM based study of various heatsink designs. The main aim of the study is to determine and evaluate solutions with improved heat dissipation by utilization of natural convection. Seventeen different cases both classical and proposed by the authors are studied, where each case is examined under three different heat source (in the case with the proposed study a transistor) powers. Results for temperature of the power source and velocity magnitudes in the studied volume are presented and comparted. Experimental verification of the modeling is presented for selected cases

EMPATHY IN HEALTHCARE PROFESSIONALS DURING THE CORONAVIRUS PANDEMIC

Background: Empathy is important for successful interactions. The aim of the present study was to determine whether the cognitive component (Perspective taking) and affective components (Empathic concern and Personal distress) of empathy in health professionals were related to the degree of perceived threat of coronavirus, difficulties in doing work, difficulties in getting along with people, the health condition (current or past coronavirus disease), as well as with some socio-demographic characteristics. Fantasy as the cognitive component of empathy was not the focus of the present study as more irrelevant to clinical practice. Subjects and methods: A study of 296 health care workers through the Interpersonal Reactivity Index and a survey on perceived coronavirus threat, difficulties in work and getting along with people found that perceiving coronavirus as a stronger threat reduced both the cognitive component of empathy Perspective taking and the affective component of empathy Personal distress. Results: As the affective components of empathy Empathic concern and Personal distress increased, the reported work difficulties were reduced. As the cognitive component of empathy Perspective taking and the affective component of empathy Personal distress increased, the reported difficulties in having a good relationship with other people were reduced. Some socio-demographic differences in the components of empathy in health workers were also established. Conclusion: These findings revealed the importance of Personal Distress (experienced anxiety, worry, discomfort, and apprehension when observing another person\u27s negative experiences), as well as the joint manifestation of several aspects of empathy for successful work and maintaining good relationships in health care. Emotionality is a normal part of human interactions, so manifestations of cognitive empathy should not be only considered as appropriate, and emotional empathy should not be ruled out as unnecessary in clinical practice during the coronavirus pandemic

The Shape of Learning: Anisotropy and Intrinsic Dimensions in Transformer-Based Models

In this study, we present an investigation into the anisotropy dynamics and intrinsic dimension of embeddings in transformer architectures, focusing on the dichotomy between encoders and decoders. Our findings reveal that the anisotropy profile in transformer decoders exhibits a distinct bell-shaped curve, with the highest anisotropy concentrations in the middle layers. This pattern diverges from the more uniformly distributed anisotropy observed in encoders. In addition, we found that the intrinsic dimension of embeddings increases in the initial phases of training, indicating an expansion into higher-dimensional space. Which is then followed by a compression phase towards the end of training with dimensionality decrease, suggesting a refinement into more compact representations. Our results provide fresh insights to the understanding of encoders and decoders embedding properties.Comment: Submitted to EACL-202

Your Transformer is Secretly Linear

This paper reveals a novel linear characteristic exclusive to transformer decoders, including models such as GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering a near-perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decreases when the residual component is removed due to a consistently low output norm of the transformer layer. Our experiments show that removing or linearly approximating some of the most linear blocks of transformers does not affect significantly the loss or model performance. Moreover, in our pretraining experiments on smaller models we introduce a cosine-similarity-based regularization, aimed at reducing layer linearity. This regularization improves performance metrics on benchmarks like Tiny Stories and SuperGLUE and as well successfully decreases the linearity of the models. This study challenges the existing understanding of transformer architectures, suggesting that their operation may be more linear than previously assumed.Comment: 9 pages, 9 figure

System size and centrality dependence of the balance function in A + A collisions at sqrt s NN = 17.2 GeV

Electric charge correlations were studied for p+p, C+C, Si+Si and centrality selected Pb+Pb collisions at sqrt s_NN = 17.2$ GeV with the NA49 large acceptance detector at the CERN-SPS. In particular, long range pseudo-rapidity correlations of oppositely charged particles were measured using the Balance Function method. The width of the Balance Function decreases with increasing system size and centrality of the reactions. This decrease could be related to an increasing delay of hadronization in central Pb+Pb collisions

Modelling, analysis and comparison of heat sink designs with improved natural convection

The paper presents FEM based study of various heatsink designs. The main aim of the study is to determine and evaluate solutions with improved heat dissipation by utilization of natural convection. Seventeen different cases both classical and proposed by the authors are studied, where each case is examined under three different heat source (in the case with the proposed study a transistor) powers. Results for temperature of the power source and velocity magnitudes in the studied volume are presented and comparted. Experimental verification of the modeling is presented for selected cases

System size and centrality dependence of the balance function in A+A collisions at sqrt[sNN]=17.2 GeV

Electric charge correlations were studied for p+p, C+C, Si+Si, and centrality selected Pb+Pb collisions at sqrt[sNN]=17.2 GeV with the NA49 large acceptance detector at the CERN SPS. In particular, long-range pseudorapidity correlations of oppositely charged particles were measured using the balance function method. The width of the balance function decreases with increasing system size and centrality of the reactions. This decrease could be related to an increasing delay of hadronization in central Pb+Pb collisions

Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture

Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The Fusion Brain Challenge combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants' submissions on it. Moreover, we have collected and made publicly available a new handwritten dataset in both English and Russian, which consists of 94,128 pairs of images and texts. We also propose a multimodal and multitask architecture - a baseline solution, in the center of which is a frozen foundation model and which has been trained in Fusion mode along with Single-task mode. The proposed Fusion approach proves to be competitive and more energy-efficient compared to the task-specific one