From 8241d010486f54e5336e296e5a3d855c00477e8c Mon Sep 17 00:00:00 2001 From: cfli <545999961@qq.com> Date: Fri, 22 Nov 2024 19:31:59 +0800 Subject: [PATCH] update examples --- .../bge-en-icl/MTEB/AmazonReviewsClassification.json | 10 +++++----- .../examples/bge-en-icl/MTEB/BiorxivClusteringP2P.json | 2 +- .../examples/bge-en-icl/MTEB/MedrxivClusteringP2P.json | 6 +++--- .../examples/bge-en-icl/MTEB/STS22.json | 8 ++++---- .../bge-en-icl/MTEB/StackExchangeClusteringP2P.json | 4 ++-- 5 files changed, 15 insertions(+), 15 deletions(-) diff --git a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/AmazonReviewsClassification.json b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/AmazonReviewsClassification.json index 38dffcdd..ec10eb8e 100644 --- a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/AmazonReviewsClassification.json +++ b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/AmazonReviewsClassification.json @@ -1,22 +1,22 @@ [ { - "query": "DO NOT ORDER THIS\n\nThis isn't what's described at all. Taking it out of the package lace was cut upon arrival, wig was cut to like 14 inch, not curly, and smelled like cigarettes. I obviously was sent what someone returned, disgusting.Not what I ordered at all, not pleased at all. I want my money back DO NOT ORDER", + "query": "DO NOT ORDER THIS\\n\\nThis isn't what's described at all. Taking it out of the package lace was cut upon arrival, wig was cut to like 14 inch, not curly, and smelled like cigarettes. I obviously was sent what someone returned, disgusting.Not what I ordered at all, not pleased at all. I want my money back DO NOT ORDER", "response": "1 star" }, { - "query": "And I can\u2019t return it\n\nThis product seemed like good quality but it does not stay stuck to the soles at all. You walk a few steps and then you find the black shoe grip somewhere on the floor.", + "query": "And I can\u2019t return it\\n\\nThis product seemed like good quality but it does not stay stuck to the soles at all. You walk a few steps and then you find the black shoe grip somewhere on the floor.", "response": "2 star" }, { - "query": "Three Stars\n\nnew yearly subscription plan is horrible, but the product still works as it did in the past", + "query": "Three Stars\\n\\nnew yearly subscription plan is horrible, but the product still works as it did in the past", "response": "3 star" }, { - "query": "I like how it has lots of pockets to put stuff ...\n\nI like how it has lots of pockets to put stuff in. I would have liked to have a shorter securing strap so it would not slide around so much. Good product.", + "query": "I like how it has lots of pockets to put stuff ...\\n\\nI like how it has lots of pockets to put stuff in. I would have liked to have a shorter securing strap so it would not slide around so much. Good product.", "response": "4 star" }, { - "query": "Great\n\nIt is really good. That's my favorite. THANK YOU", + "query": "Great\\n\\nIt is really good. That's my favorite. THANK YOU", "response": "5 star" } ] \ No newline at end of file diff --git a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/BiorxivClusteringP2P.json b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/BiorxivClusteringP2P.json index 26ff6596..398a6b9c 100644 --- a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/BiorxivClusteringP2P.json +++ b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/BiorxivClusteringP2P.json @@ -8,7 +8,7 @@ "response": "microbiology" }, { - "query": "Deep Learning Approaches for Predicting Protein-Protein Interactions from Sequence Data\nProtein-protein interactions (PPIs) are fundamental to numerous biological processes, and understanding these interactions is critical for uncovering cellular mechanisms and developing therapeutic strategies. Traditional experimental methods for identifying PPIs are labor-intensive and time-consuming, highlighting the need for computational approaches. In this study, we present DeepPPI, a deep learning-based framework designed to predict PPIs directly from protein sequence data. DeepPPI employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both local and global sequence features. We trained DeepPPI on a comprehensive dataset of known PPIs and benchmarked its performance against existing methods, demonstrating superior accuracy and generalizability. Additionally, we applied DeepPPI to predict novel interactions in the human proteome and validated a subset of these predictions experimentally. Our results indicate that DeepPPI not only achieves high prediction accuracy but also provides insights into the structural and functional basis of protein interactions, making it a valuable tool for the bioinformatics community.", + "query": "Deep Learning Approaches for Predicting Protein-Protein Interactions from Sequence Data\\nProtein-protein interactions (PPIs) are fundamental to numerous biological processes, and understanding these interactions is critical for uncovering cellular mechanisms and developing therapeutic strategies. Traditional experimental methods for identifying PPIs are labor-intensive and time-consuming, highlighting the need for computational approaches. In this study, we present DeepPPI, a deep learning-based framework designed to predict PPIs directly from protein sequence data. DeepPPI employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both local and global sequence features. We trained DeepPPI on a comprehensive dataset of known PPIs and benchmarked its performance against existing methods, demonstrating superior accuracy and generalizability. Additionally, we applied DeepPPI to predict novel interactions in the human proteome and validated a subset of these predictions experimentally. Our results indicate that DeepPPI not only achieves high prediction accuracy but also provides insights into the structural and functional basis of protein interactions, making it a valuable tool for the bioinformatics community.", "response": "bioinformatics" }, { diff --git a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/MedrxivClusteringP2P.json b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/MedrxivClusteringP2P.json index db359577..aaddd38e 100644 --- a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/MedrxivClusteringP2P.json +++ b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/MedrxivClusteringP2P.json @@ -1,10 +1,10 @@ [ { - "query": "Socioeconomic Disparities in COVID-19 Transmission Risk: A Population-Based Study from Norway\nObjective: Explore socioeconomic disparities in COVID-19 transmission risk across occupational categories in Norway.\nMethods: Analyzed data from 3,559,694 residents aged 20-70 using the International Standard Classification of Occupations (ISCO-08). Logistic regression models adjusted for various factors examined the association between occupation and SARS-CoV-2 infection risk and hospitalization during different pandemic phases.\nResults: Occupations with varying socioeconomic statuses showed different COVID-19 infection risks. Healthcare professionals had higher odds during the initial wave, while service workers had increased odds during later waves. Teachers and administrative personnel also had moderate risk increases. Occupation had limited association with hospitalization after adjusting for confounders.\nConclusion: Socioeconomic factors significantly influence COVID-19 transmission in occupational settings. Targeted public health interventions addressing workplace conditions, testing accessibility, and socioeconomic vulnerability are essential for mitigating future pandemic impacts and developing equitable pandemic preparedness strategies.\nKeywords: COVID-19, Socioeconomic Disparities, Occupational Risk, Pandemic Preparedness, Public Health, Norway, ISCO-08, SARS-CoV-2", + "query": "Socioeconomic Disparities in COVID-19 Transmission Risk: A Population-Based Study from Norway\\nObjective: Explore socioeconomic disparities in COVID-19 transmission risk across occupational categories in Norway.\\nMethods: Analyzed data from 3,559,694 residents aged 20-70 using the International Standard Classification of Occupations (ISCO-08). Logistic regression models adjusted for various factors examined the association between occupation and SARS-CoV-2 infection risk and hospitalization during different pandemic phases.\\nResults: Occupations with varying socioeconomic statuses showed different COVID-19 infection risks. Healthcare professionals had higher odds during the initial wave, while service workers had increased odds during later waves. Teachers and administrative personnel also had moderate risk increases. Occupation had limited association with hospitalization after adjusting for confounders.\\nConclusion: Socioeconomic factors significantly influence COVID-19 transmission in occupational settings. Targeted public health interventions addressing workplace conditions, testing accessibility, and socioeconomic vulnerability are essential for mitigating future pandemic impacts and developing equitable pandemic preparedness strategies.\\nKeywords: COVID-19, Socioeconomic Disparities, Occupational Risk, Pandemic Preparedness, Public Health, Norway, ISCO-08, SARS-CoV-2", "response": "infectious diseases" }, { - "query": "Assessing Socioeconomic Determinants of Infectious Disease Spread: A Cross-National Analysis Using Machine Learning Approaches\nBackground: Understanding socioeconomic factors influencing infectious disease transmission is crucial for targeted public health interventions.\nMethods: This study uses machine learning techniques and Bayesian optimization to analyze the impact of socioeconomic variables such as income, education, and healthcare access on disease dynamics. It integrates datasets on disease transmission and socio-demographic characteristics.\nResults: Significant associations between socioeconomic indicators and infectious disease spread were found, highlighting disparities in vulnerability and transmission rates.\nConclusion: Advanced analytical techniques provide nuanced insights into the socioeconomic determinants of disease transmission, aiding evidence-based policymaking to reduce health disparities and enhance epidemic preparedness.\nKeywords: Socioeconomic Determinants, Infectious Disease, Machine Learning, Public Health, Epidemiology, Health Disparities, Bayesian Optimization", + "query": "Assessing Socioeconomic Determinants of Infectious Disease Spread: A Cross-National Analysis Using Machine Learning Approaches\\nBackground: Understanding socioeconomic factors influencing infectious disease transmission is crucial for targeted public health interventions.\\nMethods: This study uses machine learning techniques and Bayesian optimization to analyze the impact of socioeconomic variables such as income, education, and healthcare access on disease dynamics. It integrates datasets on disease transmission and socio-demographic characteristics.\\nResults: Significant associations between socioeconomic indicators and infectious disease spread were found, highlighting disparities in vulnerability and transmission rates.\\nConclusion: Advanced analytical techniques provide nuanced insights into the socioeconomic determinants of disease transmission, aiding evidence-based policymaking to reduce health disparities and enhance epidemic preparedness.\\nKeywords: Socioeconomic Determinants, Infectious Disease, Machine Learning, Public Health, Epidemiology, Health Disparities, Bayesian Optimization", "response": "epidemiology" }, { @@ -16,7 +16,7 @@ "response": "genetic and genomic medicine" }, { - "query": "Longitudinal Analysis of Sleep Disturbances and Cognitive Decline in Older Adults: A 5-Year Prospective Cohort Study Background: Sleep disturbances in older adults are a recognized risk factor for cognitive decline. This study examines their impact on cognitive function over five years.\nMethods: 3,200 participants aged 60+ from Karnataka, India, were assessed annually using sleep questionnaires and cognitive tests. Exclusions included major neuropsychiatric disorders.\nResults: 25% reported sleep disturbances at baseline; 30% developed mild cognitive impairment, and 15% progressed to dementia. Insomnia and sleep apnea significantly accelerated cognitive decline. CPAP for sleep apnea showed modest protective effects.\nConclusion: Addressing sleep disturbances is crucial for mitigating cognitive decline in older adults.", + "query": "Longitudinal Analysis of Sleep Disturbances and Cognitive Decline in Older Adults: A 5-Year Prospective Cohort Study Background: Sleep disturbances in older adults are a recognized risk factor for cognitive decline. This study examines their impact on cognitive function over five years.\\nMethods: 3,200 participants aged 60+ from Karnataka, India, were assessed annually using sleep questionnaires and cognitive tests. Exclusions included major neuropsychiatric disorders.\\nResults: 25% reported sleep disturbances at baseline; 30% developed mild cognitive impairment, and 15% progressed to dementia. Insomnia and sleep apnea significantly accelerated cognitive decline. CPAP for sleep apnea showed modest protective effects.\\nConclusion: Addressing sleep disturbances is crucial for mitigating cognitive decline in older adults.", "response": "neurology" } ] \ No newline at end of file diff --git a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/STS22.json b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/STS22.json index 641e5361..85447263 100644 --- a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/STS22.json +++ b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/STS22.json @@ -1,10 +1,10 @@ [ { - "query": "The court said the ruling has stayed till January 18.\n\nThe Prevention of Money Laundering Act (PMLA) court in Mumbai which deals with offences related to money laundering has allowed banks which had lent money to fugitive liquor baron Vijay Mallya to utilise the seized assets, Enforcement Directorate (ED) sources said on Wednesday.\n\nThe court said the ruling has been stayed till January 18, until which the parties affected by the order could appeal to the Bombay High Court. According to sources, the seized assets mainly comprise of financial securities, such as shares.\n\nIn February last year, the ED had told the special PMLA court that it had no objection to the liquidation of confiscated assets by a consortium of banks, led by the State Bank of India (SBI).\n\nThe lenders want to liquidate the assets to claim Rs 6,203.35 crore along with interest of 11.5 per cent per annum payable since 2013.\n\nA special PMLA court had on January 5 last year declared Mallya a fugitive economic offender and directed that his properties be confiscated.\n\nHe had fled the country in March 2016 and has been living in the United Kingdom since then.", - "response": "A special court here has permitted a consortium of 15 banks led by the State Bank of India (SBI) to utilise movable assets of former liquor baron Vijay Mallya towards repayment of his debt.\n\nThe assets, comprising financial securities like shares of the United Breweries Holdings Ltd (UBHL), were attached by the special Prevention of Money Laundering Act (PMLA) court in 2016 when it declared Mallya a proclaimed offender.\n\nUnder provisions of the Criminal Procedure Code, a court orders attachment of a person\u2019s movable assets after he or she has been declared a proclaimed offender.\n\nA person against whom a warrant has been issued can be declared a proclaimed offender if the court believes that he or she has absconded or is evading execution of warrant.\n\nThe consortium of banks earlier filed an application before the special court, seeking release of Mallya\u2019s movable assets to utilise them for repayment of loans given to him.\n\nSenior counsel Rajeev Patil, appearing for the consortium, said the special court on Tuesday lifted the attachment on the movable assets.\n\nThe court has, however, stayed its order till January 18 to enable the parties concerned to approach the Bombay High Court in appeal.\n\nSenior counsel Amit Desai, appearing for Mallya, said the court has ordered lifting of attachment of assets, which are UBHL shares.\n\n\u201cHowever, we do not know if the court has ordered for the assets to be restored to SBI or the consortium. We are waiting for the order copy for further clarity,\u201d Mr. Desai said.\n\nMallya, who is accused of money laundering by the Enforcement Directorate, fled India in March 2016 and is now based in London.\n\nThe lenders in their application said they want to liquidate assets to claim over \u20b96,000 crore." + "query": "The court said the ruling has stayed till January 18.\\n\\nThe Prevention of Money Laundering Act (PMLA) court in Mumbai which deals with offences related to money laundering has allowed banks which had lent money to fugitive liquor baron Vijay Mallya to utilise the seized assets, Enforcement Directorate (ED) sources said on Wednesday.\\n\\nThe court said the ruling has been stayed till January 18, until which the parties affected by the order could appeal to the Bombay High Court. According to sources, the seized assets mainly comprise of financial securities, such as shares.\\n\\nIn February last year, the ED had told the special PMLA court that it had no objection to the liquidation of confiscated assets by a consortium of banks, led by the State Bank of India (SBI).\\n\\nThe lenders want to liquidate the assets to claim Rs 6,203.35 crore along with interest of 11.5 per cent per annum payable since 2013.\\n\\nA special PMLA court had on January 5 last year declared Mallya a fugitive economic offender and directed that his properties be confiscated.\\n\\nHe had fled the country in March 2016 and has been living in the United Kingdom since then.", + "response": "A special court here has permitted a consortium of 15 banks led by the State Bank of India (SBI) to utilise movable assets of former liquor baron Vijay Mallya towards repayment of his debt.\\n\\nThe assets, comprising financial securities like shares of the United Breweries Holdings Ltd (UBHL), were attached by the special Prevention of Money Laundering Act (PMLA) court in 2016 when it declared Mallya a proclaimed offender.\\n\\nUnder provisions of the Criminal Procedure Code, a court orders attachment of a person\u2019s movable assets after he or she has been declared a proclaimed offender.\\n\\nA person against whom a warrant has been issued can be declared a proclaimed offender if the court believes that he or she has absconded or is evading execution of warrant.\\n\\nThe consortium of banks earlier filed an application before the special court, seeking release of Mallya\u2019s movable assets to utilise them for repayment of loans given to him.\\n\\nSenior counsel Rajeev Patil, appearing for the consortium, said the special court on Tuesday lifted the attachment on the movable assets.\\n\\nThe court has, however, stayed its order till January 18 to enable the parties concerned to approach the Bombay High Court in appeal.\\n\\nSenior counsel Amit Desai, appearing for Mallya, said the court has ordered lifting of attachment of assets, which are UBHL shares.\\n\\n\u201cHowever, we do not know if the court has ordered for the assets to be restored to SBI or the consortium. We are waiting for the order copy for further clarity,\u201d Mr. Desai said.\\n\\nMallya, who is accused of money laundering by the Enforcement Directorate, fled India in March 2016 and is now based in London.\\n\\nThe lenders in their application said they want to liquidate assets to claim over \u20b96,000 crore." }, { - "query": "A fire in a south-end Halifax apartment building on Wednesday afternoon is being labelled as arson.\n\nIn a news release, Halifax Regional Police said fire crews and police were called to an apartment building on the 5500 block of Victoria Road at 4:23 p.m. after multiple callers said they saw smoke in the building. Fire crews quickly put out the fire.\n\nTenants were temporarily evacuated from the building, but have since returned.\n\nNo injuries have been reported.\n\nPolice are asking anyone with information about the fire to call police at 902-490-5016 or contact Crime Stoppers online or by phone at 1-800-222-TIPS (8477).\n\nMORE TOP STORIES", - "response": "Halifax police have launched an arson investigating following a structure fire in an apartment building.\n\nHalifax Regional Police say several callers reported smoke in the Victoria Road apartment building on Wednesday afternoon.\n\nNo one was hurt and tenants were temporarily evacuated as firefighters extinguished the blaze. Police say fire investigators confirmed the fire was intentionally set and handed the probe over to officers.\n\nThe arson investigation is ongoing. Police are asking anyone with information to come forward.\n\nGet more of today's top stories in your inbox Begin your day with a briefing of Halifax's biggest stories in our Morning Headlines email newsletter. Sign Up Now\n\nRead more about:" + "query": "A fire in a south-end Halifax apartment building on Wednesday afternoon is being labelled as arson.\\n\\nIn a news release, Halifax Regional Police said fire crews and police were called to an apartment building on the 5500 block of Victoria Road at 4:23 p.m. after multiple callers said they saw smoke in the building. Fire crews quickly put out the fire.\\n\\nTenants were temporarily evacuated from the building, but have since returned.\\n\\nNo injuries have been reported.\\n\\nPolice are asking anyone with information about the fire to call police at 902-490-5016 or contact Crime Stoppers online or by phone at 1-800-222-TIPS (8477).\\n\\nMORE TOP STORIES", + "response": "Halifax police have launched an arson investigating following a structure fire in an apartment building.\\n\\nHalifax Regional Police say several callers reported smoke in the Victoria Road apartment building on Wednesday afternoon.\\n\\nNo one was hurt and tenants were temporarily evacuated as firefighters extinguished the blaze. Police say fire investigators confirmed the fire was intentionally set and handed the probe over to officers.\\n\\nThe arson investigation is ongoing. Police are asking anyone with information to come forward.\\n\\nGet more of today's top stories in your inbox Begin your day with a briefing of Halifax's biggest stories in our Morning Headlines email newsletter. Sign Up Now\\n\\nRead more about:" } ] \ No newline at end of file diff --git a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/StackExchangeClusteringP2P.json b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/StackExchangeClusteringP2P.json index 7e41066b..75ea7db9 100644 --- a/research/llm_dense_retriever/examples/bge-en-icl/MTEB/StackExchangeClusteringP2P.json +++ b/research/llm_dense_retriever/examples/bge-en-icl/MTEB/StackExchangeClusteringP2P.json @@ -4,7 +4,7 @@ "response": "unity" }, { - "query": "In OpenGL, implementing shadow mapping is a challenging yet highly rewarding task. I'm currently working on achieving this through shadow mapping, but I've encountered some issues. Specifically, my shadow mapping appears somewhat blurry, and there are noticeable flickering artifacts when moving the light source or the camera. I've reviewed my implementation, including the transformation from light space to clip space and the generation of the depth texture, but the issue persists.\nHere are some key snippets of my code:// Setting up the light space to clip space transformation during depth map rendering\nglm::mat4 lightProjection = glm::ortho(-10.0f, 10.0f, -10.0f, 10.0f, 1.0f, 20.0f);\nglm::mat4 lightView = glm::lookAt(lightPos, glm::vec3(0.0f), glm::vec3(0.0, 1.0, 0.0));\nglm::mat4 lightSpaceMatrix = lightProjection * lightView;\n// Calculating shadows in the fragment shader\nfloat shadow = ShadowCalculation(lightSpaceMatrix * fragPosLightSpace);\n// Applying shadows\nvec3 lighting = CalculateLighting(...);\nfragColor = vec4(lighting * (1.0 - shadow), 1.0);\nDespite attempting adjustments such as modifying the range of the projection matrix and increasing the resolution of the depth texture, the problem persists. I suspect it might be related to depth bias, but I'm not certain yet.Any advice or possible solutions would be greatly appreciated!", + "query": "In OpenGL, implementing shadow mapping is a challenging yet highly rewarding task. I'm currently working on achieving this through shadow mapping, but I've encountered some issues. Specifically, my shadow mapping appears somewhat blurry, and there are noticeable flickering artifacts when moving the light source or the camera. I've reviewed my implementation, including the transformation from light space to clip space and the generation of the depth texture, but the issue persists.\\nHere are some key snippets of my code:// Setting up the light space to clip space transformation during depth map rendering\\nglm::mat4 lightProjection = glm::ortho(-10.0f, 10.0f, -10.0f, 10.0f, 1.0f, 20.0f);\\nglm::mat4 lightView = glm::lookAt(lightPos, glm::vec3(0.0f), glm::vec3(0.0, 1.0, 0.0));\\nglm::mat4 lightSpaceMatrix = lightProjection * lightView;\\n// Calculating shadows in the fragment shader\\nfloat shadow = ShadowCalculation(lightSpaceMatrix * fragPosLightSpace);\\n// Applying shadows\\nvec3 lighting = CalculateLighting(...);\\nfragColor = vec4(lighting * (1.0 - shadow), 1.0);\\nDespite attempting adjustments such as modifying the range of the projection matrix and increasing the resolution of the depth texture, the problem persists. I suspect it might be related to depth bias, but I'm not certain yet.Any advice or possible solutions would be greatly appreciated!", "response": "opengl" }, { @@ -12,7 +12,7 @@ "response": "c++" }, { - "query": "How to implement smooth character movement in a platformer game? I'm working on a platformer game in XNA and struggling to achieve smooth character movement. Currently, my character moves in a somewhat jerky manner, especially when changing directions or jumping. I've implemented basic movement using keyboard input and updating the character's position accordingly. Here's a snippet of what I have:\nKeyboardState newState = Keyboard.GetState();\nVector2 movement = Vector2.Zero;\nif (newState.IsKeyDown(Keys.Right))\n{\nmovement.X = MoveSpeed;\n}\nelse if (newState.IsKeyDown(Keys.Left))\n{\nmovement.X = -MoveSpeed;\n}\nif (IsOnGround() &&\nnewState.IsKeyDown(Keys.Space))\n{\nJump();\n}\n// Apply movement to character position\nPosition += movement;\nDespite this implementation, the character's movement feels rigid. I've tried adjusting the MoveSpeed and ensuring that the position updates smoothly, but there's still a noticeable jerkiness.\nI've considered using interpolation or velocity-based movement, but I'm unsure how to implement these effectively in XNA. Could someone provide guidance or a better approach to achieve smooth character movement in my platformer game?\nAny help or example code would be greatly appreciated!", + "query": "How to implement smooth character movement in a platformer game? I'm working on a platformer game in XNA and struggling to achieve smooth character movement. Currently, my character moves in a somewhat jerky manner, especially when changing directions or jumping. I've implemented basic movement using keyboard input and updating the character's position accordingly. Here's a snippet of what I have:\\nKeyboardState newState = Keyboard.GetState();\\nVector2 movement = Vector2.Zero;\\nif (newState.IsKeyDown(Keys.Right))\\n{\\nmovement.X = MoveSpeed;\\n}\\nelse if (newState.IsKeyDown(Keys.Left))\\n{\\nmovement.X = -MoveSpeed;\\n}\\nif (IsOnGround() &&\\nnewState.IsKeyDown(Keys.Space))\\n{\\nJump();\\n}\\n// Apply movement to character position\\nPosition += movement;\\nDespite this implementation, the character's movement feels rigid. I've tried adjusting the MoveSpeed and ensuring that the position updates smoothly, but there's still a noticeable jerkiness.\\nI've considered using interpolation or velocity-based movement, but I'm unsure how to implement these effectively in XNA. Could someone provide guidance or a better approach to achieve smooth character movement in my platformer game?\\nAny help or example code would be greatly appreciated!", "response": "xna" }, {