48 research outputs found
Detection of Fake News Using Machine Learning
For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news
Detection of Fake News Using Machine Learning
For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news
Finger-tip injuries: a study on functional outcomes of various methods of treatment
Background: Fingertip injuries are the most common injuries of the hand. Although maintenance of length, preservation of the nail, and appearance are important, the primary goal of treatment is a painless fingertip with durable and sensate skin. Restoration of original form or reconstruction of the most comfortable and functional compromise is the substance of challenge assured by the surgeon who manages the injured fingertip.Methods: This descriptive study was done to evaluate the outcomes of various management (conservative, primary closure, SSG and various flaps) of fingertip injuries of 180 patients from December 2014 to 2016.Results: Out of 180 patients, 30 dropped out, 76% were males and 24% were females. 68% were children and labor class. Index finger was involved in 55% cases. 42% injuries were due to machine injuries and door entrapment. Conservative and cross finger flap has better outcomes.Conclusions: This is a preliminary report of 150 cases of the fingertip injuries with the problem of tissue loss. Most patients were injured while working. Majority of trauma was caused by various machines. Various methods had been chosen depends on type of injuries, age and occupation
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Reinforcement learning (RL) algorithms hold the promise of enabling
autonomous skill acquisition for robotic systems. However, in practice,
real-world robotic RL typically requires time consuming data collection and
frequent human intervention to reset the environment. Moreover, robotic
policies learned with RL often fail when deployed beyond the carefully
controlled setting in which they were learned. In this work, we study how these
challenges can all be tackled by effective utilization of diverse offline
datasets collected from previously seen tasks. When faced with a new task, our
system adapts previously learned skills to quickly learn to both perform the
new task and return the environment to an initial state, effectively performing
its own environment reset. Our empirical results demonstrate that incorporating
prior data into robotic reinforcement learning enables autonomous learning,
substantially improves sample-efficiency of learning, and enables better
generalization. Project website: https://sites.google.com/view/ariel-berkeley/Comment: 17 pages, project website at
https://sites.google.com/view/ariel-berkeley
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Progress in deep learning highlights the tremendous potential of utilizing
diverse robotic datasets for attaining effective generalization and makes it
enticing to consider leveraging broad datasets for attaining robust
generalization in robotic learning as well. However, in practice, we often want
to learn a new skill in a new environment that is unlikely to be contained in
the prior data. Therefore we ask: how can we leverage existing diverse offline
datasets in combination with small amounts of task-specific data to solve new
tasks, while still enjoying the generalization benefits of training on large
amounts of data? In this paper, we demonstrate that end-to-end offline RL can
be an effective approach for doing this, without the need for any
representation learning or vision-based pre-training. We present pre-training
for robots (PTR), a framework based on offline RL that attempts to effectively
learn new tasks by combining pre-training on existing robotic datasets with
rapid fine-tuning on a new task, with as few as 10 demonstrations. PTR utilizes
an existing offline RL method, conservative Q-learning (CQL), but extends it to
include several crucial design decisions that enable PTR to actually work and
outperform a variety of prior methods. To our knowledge, PTR is the first RL
method that succeeds at learning new tasks in a new domain on a real WidowX
robot with as few as 10 task demonstrations, by effectively leveraging an
existing dataset of diverse multi-task robot data collected in a variety of toy
kitchens. We also demonstrate that PTR can enable effective autonomous
fine-tuning and improvement in a handful of trials, without needing any
demonstrations. An accompanying overview video can be found in the
supplementary material and at thi URL: https://sites.google.com/view/ptr-final
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
A compelling use case of offline reinforcement learning (RL) is to obtain a
policy initialization from existing datasets followed by fast online
fine-tuning with limited interaction. However, existing offline RL methods tend
to behave poorly during fine-tuning. In this paper, we study the fine-tuning
problem in the context of conservative offline RL methods and we devise an
approach for learning an effective initialization from offline data that also
enables fast online fine-tuning capabilities. Our approach, calibrated
Q-learning (Cal-QL), accomplishes this by learning a conservative value
function initialization that underestimates the value of the learned policy
from offline data, while also ensuring that the learned Q-values are at a
reasonable scale. We refer to this property as calibration, and define it
formally as providing a lower bound on the true value function of the learned
policy and an upper bound on the value of some other (suboptimal) reference
policy, which may simply be the behavior policy. We show that a conservative
offline RL algorithm that also learns a calibrated value function leads to
effective online fine-tuning, enabling us to take the benefits of offline
initializations in online fine-tuning. In practice, Cal-QL can be implemented
on top of the conservative Q learning (CQL) for offline RL within a one-line
code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11
fine-tuning benchmark tasks that we study in this paper. Code and video are
available at https://nakamotoo.github.io/projects/Cal-QLComment: project page: https://nakamotoo.github.io/projects/Cal-Q
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Pre-training on Internet data has proven to be a key ingredient for broad
generalization in many modern ML systems. What would it take to enable such
capabilities in robotic reinforcement learning (RL)? Offline RL methods, which
learn from datasets of robot experience, offer one way to leverage prior data
into the robotic learning pipeline. However, these methods have a "type
mismatch" with video data (such as Ego4D), the largest prior datasets available
for robotics, since video offers observation-only experience without the action
or reward annotations needed for RL methods. In this paper, we develop a system
for leveraging large-scale human video datasets in robotic offline RL, based
entirely on learning value functions via temporal-difference learning. We show
that value learning on video datasets learns representations that are more
conducive to downstream robotic offline RL than other approaches for learning
from video data. Our system, called V-PTR, combines the benefits of
pre-training on video data with robotic offline RL approaches that train on
diverse robot data, resulting in value functions and policies for manipulation
tasks that perform better, act robustly, and generalize broadly. On several
manipulation tasks on a real WidowX robot, our framework produces policies that
greatly improve over prior methods. Our video and additional details can be
found at https://dibyaghosh.com/vptr/Comment: First three authors contributed equall
Results and adverse events of personalized peptide receptor radionuclide therapy with 90Yttrium and 177Lutetium in 1048 patients with neuroendocrine neoplasms
Peptide receptor radionuclide therapy (PRRT) of patients with somatostatin receptor expressing neuroendocrine neoplasms has shown promising results in clinical trials and a recently published phase III study.In our center, 2294 patients were screened between 2004 and 2014 by 68Ga somatostatin receptor (SSTR) PET/CT. Intention to treat analysis included 1048 patients, who received at least one cycle of 90Yttrium or 177Lutetium-based PRRT. Progression free survival was determined by 68Ga SSTR-PET/CT and EORTC response criteria. Adverse events were determined by CTCAE criteria.Overall survival (95% confidence interval) of all patients was 51 months (47.0-54.9) and differed significantly according to radionuclide, grading, previous therapies, primary site and functionality. Progression free survival (based on PET/CT) of all patients was 19 months (16.9-21), which was significantly influenced by radionuclide, grading, and origin of neuroendocrine neoplasm. Progression free survival after initial progression and first and second resumption of PRRT after therapy-free intervals of more than 6 months were 11 months (9.4-12.5) and 8 months (6.4-9.5), respectively. Myelodysplastic syndrome or leukemia developed in 22 patients (2.1%) and 5 patients required hemodialysis after treatment, other adverse events were rare.PRRT is effective and overall survival is favorable in patients with neuroendocrine neoplasms depending on the radionuclide used for therapy, grading and origin of the neuroendocrine neoplasm which is not exactly mirrored in progression free survival as determined by highly sensitive 68Ga somatostatin receptor PET/CT using EORTC criteria for determining response to therapy