The best thing is to avoid them in order to save. Sometimes, you don&#x27;t even realize the harm you&#x27;re doing.

Three mistakes you make when washing clothes - you're wasting time and money

<strong>TEMPO</strong>.CO, Jakarta - The Meteorology, Climatology, and Geophysics Agency or BMKG released the weather forecast in Greater Jakarta today, Monday, April 29, 2024. In the morning, the entirety of Jakarta will be greeted by a partly cloudy sky. The weather will last throughout the afternoon in Central ...

BMKG Forecasts Partly Cloudy Morning in Greater Jakarta - Sci En.tempo.co

The latest findings support the existence of the mysterious Planet 9, based on detailed analysis of trans-Neptunian object clustering

A group of astronomers finds new evidence of the possible existence of the mysterious Planet 9 in the solar system

Tempo

tempo

DUBAI, United Arab Emirates, December 13th, 2024/Chainwire/--Bybit, the world’s second-largest cryptocurrency exchange by trading volume, continues to reinforce its commitment to transparency and regulatory compliance. In response to evolving regulations, Bybit has made the difficult but necessary decision to temporarily adjust the availability of its products and services within the European Economic Area (EEA). Paving the Way with MiCAR Compliance Bybit is actively pursuing a Markets in Crypto-Assets Regulation (MiCAR) license in Austria, a cornerstone of its compliance-first approach. This effort underscores Bybit’s dedication to aligning with stringent European regulatory standards, enhancing user protection, and delivering a secure trading environment. “Obtaining the MiCAR license will be a strategic milestone for Bybit,” said Ben Zhou, Co-founder and CEO of Bybit. “Our proactive stance ensures that we meet and exceed the expectations of both regulators and our users, as we work toward a future where innovation and compliance coexist seamlessly.” Temporary Adjustments to EEA Operations To ensure ongoing compliance with applicable regulatory laws, particularly regarding reverse solicitation, Bybit has made the difficult decision to generally cease all communication with the EEA region. This measure is intended to avoid any potential breach of the strict reverse solicitation principle. Existing customers' access to their crypto assets remains uninterrupted. Although this was a challenging decision, it was necessary for Bybit to maintain its compliance-first approach. Bybit is actively working towards obtaining a MiCAR license in Austria to become one of the first players in the EEA. Once the appropriate licensing is secured, Bybit will start engagement with its EEA clients in accordance with applicable laws. Balancing Innovation with Compliance “As cryptocurrency adoption continues to grow globally, it is Bybit's mission to deliver secure and reliable trading experiences underpinned by robust regulatory framework to all crypto communities," said Ben Zhou, Co-founder and CEO of Bybit. "To ensure we meet the highest standards and pave the way for a safer and more sustainable future for the industry, we've made the proactive decision to temporarily adjust our operations in the EEA. This allows us to focus on obtaining the necessary licenses and ensuring a fully compliant platform focused on serving EEA clients." Commitment to the EEA Market Bybit will be deeply committed to serving its EEA clients once it receives a MiCAR license. The company is actively engaging with regulatory authorities to expedite the licensing process and start full operations in the region. Bybit appreciates the support of its users as it navigates this pivotal regulatory journey. For questions or assistance, users are encouraged to contact Bybit’s Customer Support team via Live Chat. #Bybit / #TheCryptoArk About Bybit Bybit is the world’s second-largest cryptocurrency exchange by trading volume, serving over 50 million users. Established in 2018, Bybit provides a professional platform where crypto investors and traders can find an ultra-fast matching engine, 24/7 customer service, and multilingual community support. Bybit is a proud partner of Formula One’s reigning Constructors’ and Drivers’ champions: the Oracle Red Bull Racing team. For media inquiries, please contact: media@bybit.com Contact Head of PR Tony Au Bybit tony.au@bybit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, United Arab Emirates, December 13th, 2024/Chainwire/--Bybit, the world’s second-largest cryptocurrency exchange by trading volume, continues to reinforce its commitment to transparency and regulatory compliance. In response to evolving regulations, Bybit has made the difficult but necessary decision to temporarily adjust the availability of its products and services within the European Economic Area (EEA). Paving the Way with MiCAR Compliance Bybit is actively pursuing a Markets in Crypto-Assets Regulation (MiCAR) license in Austria, a cornerstone of its compliance-first approach. This effort underscores Bybit’s dedication to aligning with stringent European regulatory standards, enhancing user protection, and delivering a secure trading environment. “Obtaining the MiCAR license will be a strategic milestone for Bybit,” said Ben Zhou, Co-founder and CEO of Bybit. “Our proactive stance ensures that we meet and exceed the expectations of both regulators and our users, as we work toward a future where innovation and compliance coexist seamlessly.” “Obtaining the MiCAR license will be a strategic milestone for Bybit,” said Ben Zhou, Co-founder and CEO of Bybit. “Our proactive stance ensures that we meet and exceed the expectations of both regulators and our users, as we work toward a future where innovation and compliance coexist seamlessly.” Temporary Adjustments to EEA Operations To ensure ongoing compliance with applicable regulatory laws, particularly regarding reverse solicitation, Bybit has made the difficult decision to generally cease all communication with the EEA region. This measure is intended to avoid any potential breach of the strict reverse solicitation principle. Existing customers' access to their crypto assets remains uninterrupted. Although this was a challenging decision, it was necessary for Bybit to maintain its compliance-first approach. Bybit is actively working towards obtaining a MiCAR license in Austria to become one of the first players in the EEA. Once the appropriate licensing is secured, Bybit will start engagement with its EEA clients in accordance with applicable laws. Balancing Innovation with Compliance “As cryptocurrency adoption continues to grow globally, it is Bybit's mission to deliver secure and reliable trading experiences underpinned by robust regulatory framework to all crypto communities," said Ben Zhou, Co-founder and CEO of Bybit. “As cryptocurrency adoption continues to grow globally, it is Bybit's mission to deliver secure and reliable trading experiences underpinned by robust regulatory framework to all crypto communities," said Ben Zhou, Co-founder and CEO of Bybit. "To ensure we meet the highest standards and pave the way for a safer and more sustainable future for the industry, we've made the proactive decision to temporarily adjust our operations in the EEA. This allows us to focus on obtaining the necessary licenses and ensuring a fully compliant platform focused on serving EEA clients." "To ensure we meet the highest standards and pave the way for a safer and more sustainable future for the industry, we've made the proactive decision to temporarily adjust our operations in the EEA. This allows us to focus on obtaining the necessary licenses and ensuring a fully compliant platform focused on serving EEA clients." Commitment to the EEA Market Bybit will be deeply committed to serving its EEA clients once it receives a MiCAR license. The company is actively engaging with regulatory authorities to expedite the licensing process and start full operations in the region. Bybit appreciates the support of its users as it navigates this pivotal regulatory journey. For questions or assistance, users are encouraged to contact Bybit’s Customer Support team via Live Chat. #Bybit / #TheCryptoArk About Bybit Bybit is the world’s second-largest cryptocurrency exchange by trading volume, serving over 50 million users. Established in 2018, Bybit provides a professional platform where crypto investors and traders can find an ultra-fast matching engine, 24/7 customer service, and multilingual community support. Bybit is a proud partner of Formula One’s reigning Constructors’ and Drivers’ champions: the Oracle Red Bull Racing team. For media inquiries, please contact: media@bybit.com Contact Head of PR Tony Au Bybit tony.au@bybit.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Bybit Advances Regulatory Compliance, Temporarily Adjusts EEA Operations

Bybit прасоўвае адпаведнасць нарматыўным патрабаванням, часова карэктуе дзейнасць ЕЭЗ

Bybit avanza en el cumplimiento normativo y ajusta temporalmente sus operaciones en el EEE

Bybit-ը բարելավում է կանոնակարգման համապատասխանությունը, ժամանակավորապես կարգավորում է ԵՏՀ գործառնությունները

Bybit、規制遵守を推進し、EEAの業務を一時的に調整

„Bybit“ pagerina teisės aktų laikymąsi, laikinai koreguoja EEE operacijas

Bybit uzlabo atbilstību normatīvajiem aktiem, īslaicīgi pielāgo EEZ darbības

Bybit зохицуулалтын нийцлийг сайжруулж, EEA-ийн үйл ажиллагааг түр зохицуулдаг

Bybit e Tšwetša Pele Kobamelo ya Taolo, e Beakanya Mešomo ya EEA ka Nakwana

Bybit zwiększa zgodność z przepisami, tymczasowo dostosowuje operacje w EOG

Bybit zlepšuje súlad s predpismi, dočasne upravuje operácie v EHP

Bybit Advances Regulatory Compliance, Nenguva Inogadzirisa EEA Operations

Bybit ส่งเสริมการปฏิบัติตามกฎระเบียบ ปรับการดำเนินงาน EEA ชั่วคราว

Author:
(1) David Staines. Table of Links Abstract 1 Introduction 2 Mathematical Arguments 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 5 Existing Solutions 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.3 Literature Comparison 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.3 Discussion 9 Bifurcation Analysis 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 11 Policy Rule 12 Conclusions and References Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.3 De Rham Cohomology F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.3 Trend Inflation Volatility C.2 Extending No Persistence This subsection generalizes lack of persistence in two directions. The first is to show that in the absence of price dispersion, it extends to all approximations from ZINSS. Similar results are well-known for Rotemberg. The second shows that even when the output gap is adjusted to allow technology shocks, inflation still responds to demand shocks only on impact. C.2.1 Higher Order Dynamics C.2.2 Dynamics with Unobserved Natural Rate Although no persistence breaks down, propagation problems still arise, in particular, with respect to inflation and aggregate demand shocks. The destruction of endogenous persistence when shock persistence takes limiting values is a theme common to the whole paper. This analysis has appeared before. The task at hand is to diagonalize (240) and substitute into the forward solution of (239) and then compute the persistence. First solve (241)-(243), this yields The relevant inverse matrix is using the forward solution and several rounds of substitution yields the following expression for inflation Deploying the persistent properties of the errors, communicated in Subsection 4.7, this yields It is intuitive that a positive supply shock drives down inflation. Away from the two limiting cases where it vanishes, as with Divine Coincidence, there is positive persistence, measured by the product moment correlation coefficient It is still pathological to Keynesian intuition that inflation does not respond to demand shocks. For output the expression is Again, the desired limiting cases arise. There can be overshooting or undershooting with respect to the efficient benchmark.110 However, the underlying message is that this system cannot generate persistent responses from inflation to demand shocks. This paper is available on arxiv under CC 4.0 license. Author: (1) David Staines. Author: Author: (1) David Staines. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Mathematical Arguments 2 Mathematical Arguments 3 Outline and Preview 3 Outline and Preview 4 Calvo Framework and 4.1 Household’s Problem 4 Calvo Framework and 4.1 Household’s Problem 4.2 Preferences 4.2 Preferences 4.3 Household Equilibrium Conditions 4.3 Household Equilibrium Conditions 4.4 Price-Setting Problem 4.4 Price-Setting Problem 4.5 Nominal Equilibrium Conditions 4.5 Nominal Equilibrium Conditions 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.6 Real Equilibrium Conditions and 4.7 Shocks 4.8 Recursive Equilibrium 4.8 Recursive Equilibrium 5 Existing Solutions 5 Existing Solutions 5.1 Singular Phillips Curve 5.1 Singular Phillips Curve 5.2 Persistence and Policy Puzzles 5.2 Persistence and Policy Puzzles 5.3 Two Comparison Models 5.3 Two Comparison Models 5.4 Lucas Critique 5.4 Lucas Critique 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems 6.2 Equilibrium Construction 6.2 Equilibrium Construction 6.3 Literature Comparison 6.3 Literature Comparison 6.4 Equilibrium Analysis 6.4 Equilibrium Analysis 7 General Linearized Phillips Curve 7 General Linearized Phillips Curve 7.1 Slope Coefficients 7.1 Slope Coefficients 7.2 Error Coefficients 7.2 Error Coefficients 8 Existence Results and 8.1 Main Results 8 Existence Results and 8.1 Main Results 8.2 Key Proofs 8.2 Key Proofs 8.3 Discussion 8.3 Discussion 9 Bifurcation Analysis 9 Bifurcation Analysis 9.1 Analytic Aspects 9.1 Analytic Aspects 9.2 Algebraic Aspects (I) Singularities and Covers 9.2 Algebraic Aspects (I) Singularities and Covers 9.3 Algebraic Aspects (II) Homology 9.3 Algebraic Aspects (II) Homology 9.4 Algebraic Aspects (III) Schemes 9.4 Algebraic Aspects (III) Schemes 9.5 Wider Economic Interpretations 9.5 Wider Economic Interpretations 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs 10.2 Econometric Duality 10.2 Econometric Duality 10.3 Coefficient Properties 10.3 Coefficient Properties 10.4 Microeconomic Interpretation 10.4 Microeconomic Interpretation 11 Policy Rule 11 Policy Rule 12 Conclusions and References 12 Conclusions and References Appendices Appendices A Proof of Theorem 2 and A.1 Proof of Part (i) A Proof of Theorem 2 and A.1 Proof of Part (i) A.2 Behaviour of ∆ A.2 Behaviour of ∆ A.3 Proof Part (iii) A.3 Proof Part (iii) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B Proofs from Section 4 and B.1 Individual Product Demand (4.2) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.2 Flexible Price Equilibrium and ZINSS (4.4) B.3 Price Dispersion (4.5) B.3 Price Dispersion (4.5) B.4 Cost Minimization (4.6) and (10.4) B.4 Cost Minimization (4.6) and (10.4) B.5 Consolidation (4.8) B.5 Consolidation (4.8) C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence C.2 Extending No Persistence C.2 Extending No Persistence D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium D.2 Profits and Long-Run Growth D.2 Profits and Long-Run Growth E Slopes and Eigenvalues and E.1 Slope Coefficients E Slopes and Eigenvalues and E.1 Slope Coefficients E.2 Linearized DSGE Solution E.2 Linearized DSGE Solution E.3 Eigenvalue Conditions E.3 Eigenvalue Conditions E.4 Rouche’s Theorem Conditions E.4 Rouche’s Theorem Conditions F Abstract Algebra and F.1 Homology Groups F Abstract Algebra and F.1 Homology Groups F.2 Basic Categories F.2 Basic Categories F.3 De Rham Cohomology F.3 De Rham Cohomology F.4 Marginal Costs and Inflation F.4 Marginal Costs and Inflation G Further Keynesian Models and G.1 Taylor Pricing G Further Keynesian Models and G.1 Taylor Pricing G.2 Calvo Wage Phillips Curve G.2 Calvo Wage Phillips Curve G.3 Unconventional Policy Settings G.3 Unconventional Policy Settings H Empirical Robustness and H.1 Parameter Selection H Empirical Robustness and H.1 Parameter Selection H.2 Phillips Curve H.2 Phillips Curve I Additional Evidence and I.1 Other Structural Parameters I Additional Evidence and I.1 Other Structural Parameters I.2 Lucas Critique I.2 Lucas Critique I.3 Trend Inflation Volatility I.3 Trend Inflation Volatility C.2 Extending No Persistence This subsection generalizes lack of persistence in two directions. The first is to show that in the absence of price dispersion, it extends to all approximations from ZINSS. Similar results are well-known for Rotemberg. The second shows that even when the output gap is adjusted to allow technology shocks, inflation still responds to demand shocks only on impact. C.2.1 Higher Order Dynamics C.2.1 Higher Order Dynamics C.2.2 Dynamics with Unobserved Natural Rate C.2.2 Dynamics with Unobserved Natural Rate Although no persistence breaks down, propagation problems still arise, in particular, with respect to inflation and aggregate demand shocks. The destruction of endogenous persistence when shock persistence takes limiting values is a theme common to the whole paper. This analysis has appeared before. The task at hand is to diagonalize (240) and substitute into the forward solution of (239) and then compute the persistence. First solve (241)-(243), this yields The relevant inverse matrix is using the forward solution and several rounds of substitution yields the following expression for inflation Deploying the persistent properties of the errors, communicated in Subsection 4.7, this yields It is intuitive that a positive supply shock drives down inflation. Away from the two limiting cases where it vanishes, as with Divine Coincidence, there is positive persistence, measured by the product moment correlation coefficient It is still pathological to Keynesian intuition that inflation does not respond to demand shocks. For output the expression is Again, the desired limiting cases arise. There can be overshooting or undershooting with respect to the efficient benchmark.110 However, the underlying message is that this system cannot generate persistent responses from inflation to demand shocks. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Why Inflation Only Responds Temporarily to Economic Changes

The TechBeat: Building a Data Analytics Platform to Streamline the Temporary Labor Sector (12/8/2024)

The TechBeat: Building a Data Analytics Platform to Streamline the Temporary Labor Sector (12/6/2024)

Building a Data Analytics Platform to Streamline the Temporary Labor Sector

Стварэнне платформы аналітыкі даных для ўпарадкавання сектара часовай працы

Creación de una plataforma de análisis de datos para optimizar el sector del trabajo temporal

Création d'une plateforme d'analyse de données pour rationaliser le secteur du travail temporaire

अस्थायी श्रम क्षेत्र को कारगर बनाने के लिए डेटा एनालिटिक्स प्लेटफ़ॉर्म का निर्माण

派遣労働部門の効率化に向けたデータ分析プラットフォームの構築

ការកសាងវេទិកាវិភាគទិន្នន័យ ដើម្បីពង្រឹងវិស័យការងារបណ្តោះអាសន្ន

임시 노동 부문을 간소화하기 위한 데이터 분석 플랫폼 구축

Construindo uma plataforma de análise de dados para otimizar o setor de trabalho temporário

තාවකාලික කම්කරු අංශය විධිමත් කිරීම සඳහා දත්ත විශ්ලේෂණ වේදිකාවක් ගොඩනැගීම

Kuunda Jukwaa la Uchanganuzi wa Data ili Kuhuisha Sekta ya Kazi ya Muda

การสร้างแพลตฟอร์มการวิเคราะห์ข้อมูลเพื่อปรับปรุงภาคส่วนแรงงานชั่วคราว

Створення платформи аналізу даних для оптимізації сектору тимчасової праці

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 4.2 LEARN MOTION PRIORS WITH MOTION MODULE To model motion dynamics along the temporal dimension on top of a pre-trained T2I, we must 1) inflate the 2-dimensional diffusion model to deal with 3-dimensional video data and 2) design a sub-module to enable efficient information exchange along the temporal axis. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References 4.2 LEARN MOTION PRIORS WITH MOTION MODULE To model motion dynamics along the temporal dimension on top of a pre-trained T2I, we must 1) inflate the 2-dimensional diffusion model to deal with 3-dimensional video data and 2) design a sub-module to enable efficient information exchange along the temporal axis. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Mastering Motion Dynamics in Animation with Temporal Transformers

(1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu);
(2) Bichen Wu, Meta GenAI and Corresponding author;
(3) Jialiang Wang, Meta GenAI;
(4) Licheng Yu, Meta GenAI;
(5) Kunpeng Li, Meta GenAI;
(6) Yinan Zhao, Meta GenAI;
(7) Ishan Misra, Meta GenAI;
(8) Jia-Bin Huang, Meta GenAI;
(9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com);
(10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com);
(11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction
2. Related Work
3. Preliminary
4. FlowVid
4.1. Inflating image U-Net to accommodate video
4.2. Training with joint spatial-temporal conditions
4.3. Generation: edit the first frame then propagate


Experiments


5.1. Settings
5.2. Qualitative results
5.3. Quantitative results
5.4. Ablation study and 5.5. Limitations
Conclusion, Acknowledgments and References
A. Webpage Demo and B. Quantitative comparisons 4.2. Training with joint spatial-temporal conditions Upon expanding the image model, a straightforward method might be to train the video model using paired depth-video data. Yet, our empirical analysis indicates that this leads to sub-optimal results, as detailed in the ablation study in Section 5.4. We hypothesize that this method neglects the temporal clue within the video, making the frame consistency hard to maintain. While some studies, such as Rerender [49] and CoDeF [32], incorporate optical flow in video synthesis, they typically apply it as a rigid constraint. In contrast, our approach uses flow as a soft condition, allowing us to manage the imperfections commonly found in flow estimation. This paper is available on arxiv under CC 4.0 license. (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction 2. Related Work 3. Preliminary 4. FlowVid 4.1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate Experiments 5.1. Settings 5.2. Qualitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons Abstract and Introduction Abstract and Introduction 2. Related Work 2. Related Work 3. Preliminary 3. Preliminary 4. FlowVid 4. FlowVid 4 .1. Inflating image U-Net to accommodate video .1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate 4.3. Generation: edit the first frame then propagate Experiments Experiments Experiments 5.1. Settings 5.1. Settings 5.2. Qualitative results 5.2. Qualitative results 5.3. Quantitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons A. Webpage Demo and B. Quantitative comparisons 4.2. Training with joint spatial-temporal conditions Upon expanding the image model, a straightforward method might be to train the video model using paired depth-video data. Yet, our empirical analysis indicates that this leads to sub-optimal results, as detailed in the ablation study in Section 5.4. We hypothesize that this method neglects the temporal clue within the video, making the frame consistency hard to maintain. While some studies, such as Rerender [49] and CoDeF [32], incorporate optical flow in video synthesis, they typically apply it as a rigid constraint. In contrast, our approach uses flow as a soft condition, allowing us to manage the imperfections commonly found in flow estimation. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

FlowVid: Taming Imperfect Optical Flows: Training With Joint Spatial-temporal Conditions

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Related Work Temporal Dynamics in Linguistic Polarization Scholars have measured linguistic polarization through stance (Dash et al. 2022), toxicity (Sap et al. 2019), sentiment detection (Yang et al. 2017), topic modeling (Ebeling et al. 2022), and lexicon-dictionaries (Polignano et al. 2022). Such methods typically capture an aggregated snapshot of polarization across large textual corpora, providing a static quantification of media bias based on preset criteria. As a result, such approaches seldom capture temporal fluctuations in semantic polarity. Only a few research to date quantify linguistic polarization over time. For example, researchers have measured partisan trends in congressional speech by defining polarization as the expected posterior probability that a neutral observer would infer a speaker’s political party from a randomly selected utterance (Gentzkow, Shapiro, and Taddy 2019) across various issues. Demszky et al. use this Bayesian approach to capture how social media discussions on mass shootings become increasingly divisive (2020). Such methods, however, capture linguistic polarization as a general metric across nearly all aggregated words within a textual corpus. Given the powerful salience of certain topical keywords that are inseparable from American politics, unlike prior work, we focus on how specific keywords polarize over time. We depart from previous approaches by devising a framework that captures semantic polarity as a function of how two different entities semantically diverge across time in their contextual use of identical words that are central to the American public discourse. Specifically, we measure linguistic polarization by capturing the diachronic shifts in how two news stations use the same topical words on their news programs over an 11-year period. Further, in effort to understand the linguistic drivers behind how and why semantic polarization evolves, we aim to identify the source of diachronic shifts based on how two different entities use identical, politically contentious keywords. Few research has endeavored to detect causal factors underlying how the meaning of words changes over time, which remains an open challenge in NLP scholarship (Kutuzov et al. 2018). Hamilton et al. show how word meanings change between consecutive decades due to cultural shifts (e.g., change in the meaning of “cell” driven by technological advancement: prison cell vs. cell phone) (2016). Yet such research typically makes a priori assumptions that the language between two textual corpora of comparison are significantly related without statistically demonstrating how so. On the other hand, research that do provide evidence of statistical relations between two separate textual data (Dutta, Ma, and Choudhury 2018), generally do not explain what words drive the direction of semantic influence from one corpus to another. We overcome these limitations: first, we statistically validate the existence of a significant semantic relationship between two textual corpora, TV news language and social media discourse, through Grangercausality (Study 3). Then, we strategically separate our data by lag-times associated with significant Granger-causal relations and apply a deep learning interpretation technique to demonstrate which contextual words from TV news language influences Twitter discussions around specific topical keywords (and vice-versa) over time. Who Influences Who? Setting a Media Agenda On any given day, newsrooms select news on a wide range of topics that cover a variety of issues. According to Agenda Setting Theory, this selection process leads audiences to see certain issues as more significant than others (McCombs 1997). Agenda Setting was particularly useful in decades past when media was much more consolidated. In the U.S. for example, three major networks (NBC, ABC, and CBS) provided the bulk of broadcast news content, and so their news agendas had broad influence on what audiences saw as important. However, today’s media landscape is much more fragmented. People receive their news from any number of sources including television, newspapers, radio, online websites, and social media. In such an environment, Agenda Setting has certain limitations as the theory assumes that audiences are relatively passive and simply receive information from the media. This was true when news flowed one-way from the media to the mass public. By contrast nowadays, the two-way form of communication afforded by the internet and social media makes it possible for the public to both influence media agendas and be influenced by them (Papacharissi 2009; Barnard 2018). As a result, new scholarship has argued through Intermedia Agenda Setting Theory that media agendas do not come entirely from within their own organizations, but from two other sources: other media and the mass audience. In the latter case, for example, audiences themselves can influence media agendas through online affordances (e.g., retweeting, commenting, sharing), to raise certain issues to prominence online (Rogstad 2016). In this work, we explore these new emerging dynamics with an interest in not only understanding how news agendas are constructed, but how the possible adoption of different agendas might influence agenda dynamics as media interact with the public online. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Related Work Temporal Dynamics in Linguistic Polarization Scholars have measured linguistic polarization through stance (Dash et al. 2022), toxicity (Sap et al. 2019), sentiment detection (Yang et al. 2017), topic modeling (Ebeling et al. 2022), and lexicon-dictionaries (Polignano et al. 2022). Such methods typically capture an aggregated snapshot of polarization across large textual corpora, providing a static quantification of media bias based on preset criteria. As a result, such approaches seldom capture temporal fluctuations in semantic polarity. Only a few research to date quantify linguistic polarization over time. For example, researchers have measured partisan trends in congressional speech by defining polarization as the expected posterior probability that a neutral observer would infer a speaker’s political party from a randomly selected utterance (Gentzkow, Shapiro, and Taddy 2019) across various issues. Demszky et al. use this Bayesian approach to capture how social media discussions on mass shootings become increasingly divisive (2020). Such methods, however, capture linguistic polarization as a general metric across nearly all aggregated words within a textual corpus. Given the powerful salience of certain topical keywords that are inseparable from American politics, unlike prior work, we focus on how specific keywords polarize over time. We depart from previous approaches by devising a framework that captures semantic polarity as a function of how two different entities semantically diverge across time in their contextual use of identical words that are central to the American public discourse. Specifically, we measure linguistic polarization by capturing the diachronic shifts in how two news stations use the same topical words on their news programs over an 11-year period. Further, in effort to understand the linguistic drivers behind how and why semantic polarization evolves, we aim to identify the source of diachronic shifts based on how two different entities use identical, politically contentious keywords. Few research has endeavored to detect causal factors underlying how the meaning of words changes over time, which remains an open challenge in NLP scholarship (Kutuzov et al. 2018). Hamilton et al. show how word meanings change between consecutive decades due to cultural shifts (e.g., change in the meaning of “cell” driven by technological advancement: prison cell vs. cell phone) (2016). Yet such research typically makes a priori assumptions that the language between two textual corpora of comparison are significantly related without statistically demonstrating how so. On the other hand, research that do provide evidence of statistical relations between two separate textual data (Dutta, Ma, and Choudhury 2018), generally do not explain what words drive the direction of semantic influence from one corpus to another. We overcome these limitations: first, we statistically validate the existence of a significant semantic relationship between two textual corpora, TV news language and social media discourse, through Grangercausality (Study 3). Then, we strategically separate our data by lag-times associated with significant Granger-causal relations and apply a deep learning interpretation technique to demonstrate which contextual words from TV news language influences Twitter discussions around specific topical keywords (and vice-versa) over time. Who Influences Who? Setting a Media Agenda On any given day, newsrooms select news on a wide range of topics that cover a variety of issues. According to Agenda Setting Theory, this selection process leads audiences to see certain issues as more significant than others (McCombs 1997). Agenda Setting was particularly useful in decades past when media was much more consolidated. In the U.S. for example, three major networks (NBC, ABC, and CBS) provided the bulk of broadcast news content, and so their news agendas had broad influence on what audiences saw as important. However, today’s media landscape is much more fragmented. People receive their news from any number of sources including television, newspapers, radio, online websites, and social media. In such an environment, Agenda Setting has certain limitations as the theory assumes that audiences are relatively passive and simply receive information from the media. This was true when news flowed one-way from the media to the mass public. By contrast nowadays, the two-way form of communication afforded by the internet and social media makes it possible for the public to both influence media agendas and be influenced by them (Papacharissi 2009; Barnard 2018). As a result, new scholarship has argued through Intermedia Agenda Setting Theory that media agendas do not come entirely from within their own organizations, but from two other sources: other media and the mass audience. In the latter case, for example, audiences themselves can influence media agendas through online affordances (e.g., retweeting, commenting, sharing), to raise certain issues to prominence online (Rogstad 2016). In this work, we explore these new emerging dynamics with an interest in not only understanding how news agendas are constructed, but how the possible adoption of different agendas might influence agenda dynamics as media interact with the public online. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Temporal Dynamics in Linguistic Polarization

Temporary Lifetime Extension: Complicated Cases

Author:
(1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu);
(2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Related Works Preliminaries Optimal Prebunking Problem Deterministic Baseline Policies Temporally Equidistant Prebunking Numerical Results Conclusion and References VI. TEMPORALLY EQUIDISTANT PREBUNKING Thus, by Corollary 3, the optimal policy delivers prebunks evenly across all time steps with equal time gaps in-between. Intuitively, this means that the optimal policy tries to maximize the time gap between each consecutive prebunk delivery. We emphasize that Corollary 3 only holds exactly when we have the restricted problem 24, without the additional constraints. We can approximate this equal time gap policy in the general problem 12. The goal of the policy π in this case is to determine which Pk to issue at time t. Clearly, it is not optimal to provide a prebunk Pk to user c more than once, since doing so does not change the feasibility of problem and cannot reduce the maximal cost C(π). Thus, we can assume without loss of optimality that each Pk is in at most one At ∈ A. Hence, in its decision, the policy π only needs to take into account the misinformations Mk, for which it has not delivered any prebunk Pk yet. Without loss of generality, we assume that this set of misinformations is the finite set {M1, . . . , MK} as Assumption 2 guarantees that at each time t there are finitely many Mk for which Ik ̸= ∅. In our experiments, we use a Monte Carlo method and estimate Xk by simulating misinformation propagations in the local network Gc, and obtaining a frequentist estimation of how long it takes for the misinformation to reach from any node in the network to the central user c. Clearly, this estimation method does not scale to large networks. However, in our tests, for small choices of m the local network around user c remains small enough to permit this approach. It is possible that there are other estimation methods that admit more accurate estimations or scale better to large networks, which might provide better results. This paper is available on arxiv under CC 4.0 license. Author: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Author: Author: (1) Yigit Ege Bayiz, Electrical and Computer Engineering The University of Texas at Austin Austin, Texas, USA (Email: egebayiz@utexas.edu); (2) Ufuk Topcu, Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, Texas, USA (Email: utopcu@utexas.edu). Table of Links Abstract and Introduction Abstract and Introduction Related Works Related Works Preliminaries Preliminaries Optimal Prebunking Problem Optimal Prebunking Problem Deterministic Baseline Policies Deterministic Baseline Policies Temporally Equidistant Prebunking Temporally Equidistant Prebunking Numerical Results Numerical Results Conclusion and References Conclusion and References VI. TEMPORALLY EQUIDISTANT PREBUNKING Thus, by Corollary 3, the optimal policy delivers prebunks evenly across all time steps with equal time gaps in-between. Intuitively, this means that the optimal policy tries to maximize the time gap between each consecutive prebunk delivery. We emphasize that Corollary 3 only holds exactly when we have the restricted problem 24, without the additional constraints. We can approximate this equal time gap policy in the general problem 12. The goal of the policy π in this case is to determine which Pk to issue at time t. Clearly, it is not optimal to provide a prebunk Pk to user c more than once, since doing so does not change the feasibility of problem and cannot reduce the maximal cost C(π). Thus, we can assume without loss of optimality that each Pk is in at most one At ∈ A. Hence, in its decision, the policy π only needs to take into account the misinformations Mk, for which it has not delivered any prebunk Pk yet. Without loss of generality, we assume that this set of misinformations is the finite set {M1, . . . , MK} as Assumption 2 guarantees that at each time t there are finitely many Mk for which Ik ̸= ∅. In our experiments, we use a Monte Carlo method and estimate Xk by simulating misinformation propagations in the local network Gc, and obtaining a frequentist estimation of how long it takes for the misinformation to reach from any node in the network to the central user c. Clearly, this estimation method does not scale to large networks. However, in our tests, for small choices of m the local network around user c remains small enough to permit this approach. It is possible that there are other estimation methods that admit more accurate estimations or scale better to large networks, which might provide better results. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Temporally Equidistant Prebunking

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