29 research outputs found
Learning to Generate Facial Depth Maps
In this paper, an adversarial architecture for facial depth map estimation from monocular intensity images is presented.
By following an image-to-image approach, we combine the advantages of supervised learning and adversarial training, proposing a conditional Generative Adversarial Network that effectively learns to translate intensity face images into the corresponding depth maps. Two public datasets, namely Biwi database and Pandora dataset, are exploited to demonstrate that the proposed model generates high-quality synthetic depth images, both in terms of visual appearance and informative content. Furthermore, we show that the model is capable of predicting distinctive facial details by testing the generated depth maps through a deep model trained on authentic depth maps for the face verification task
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
In protein biophysics, the separation between the functionally important
residues (forming the active site or binding surface) and those that create the
overall structure (the fold) is a well-established and fundamental concept.
Identifying and modifying those functional sites is critical for protein
engineering but computationally non-trivial, and requires significant domain
knowledge. To automate this process from a data-driven perspective, we propose
a disentangled Wasserstein autoencoder with an auxiliary classifier, which
isolates the function-related patterns from the rest with theoretical
guarantees. This enables one-pass protein sequence editing and improves the
understanding of the resulting sequences and editing actions involved. To
demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a
well-studied structure-function case. We show that our method can be used to
alter the function of TCRs without changing the structural backbone,
outperforming several competing methods in generation quality and efficiency,
and requiring only 10% of the running time needed by baseline models. To our
knowledge, this is the first approach that utilizes disentangled
representations for TCR engineering
Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis
The tremendous hype around autonomous driving is eagerly calling for emerging
and novel technologies to support advanced mobility use cases. As car
manufactures keep developing SAE level 3+ systems to improve the safety and
comfort of passengers, traffic authorities need to establish new procedures to
manage the transition from human-driven to fully-autonomous vehicles while
providing a feedback-loop mechanism to fine-tune envisioned autonomous systems.
Thus, a way to automatically profile autonomous vehicles and differentiate
those from human-driven ones is a must. In this paper, we present a
fully-fledged framework that monitors active vehicles using camera images and
state information in order to determine whether vehicles are autonomous,
without requiring any active notification from the vehicles themselves.
Essentially, it builds on the cooperation among vehicles, which share their
data acquired on the road feeding a machine learning model to identify
autonomous cars. We extensively tested our solution and created the NexusStreet
dataset, by means of the CARLA simulator, employing an autonomous driving
control agent and a steering wheel maneuvered by licensed drivers. Experiments
show it is possible to discriminate the two behaviors by analyzing video clips
with an accuracy of 80%, which improves up to 93% when the target state
information is available. Lastly, we deliberately degraded the state to observe
how the framework performs under non-ideal data collection conditions
On TCR binding predictors failing to generalize to unseen peptides
Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training set. In this work, we investigate how state-of-the-art deep learning models for TCR-peptide/-pMHC binding prediction generalize to unseen peptides. We create a dataset including positive samples from IEDB, VDJdb, McPAS-TCR, and the MIRA set, as well as negative samples from both randomization and 10X Genomics assays. We name this collection of samples TChard. We propose the hard split, a simple heuristic for training/test split, which ensures that test samples exclusively present peptides that do not belong to the training set. We investigate the effect of different training/test splitting techniques on the models’ test performance, as well as the effect of training and testing the models using mismatched negative samples generated randomly, in addition to the negative samples derived from assays. Our results show that modern deep learning methods fail to generalize to unseen peptides. We provide an explanation why this happens and verify our hypothesis on the TChard dataset. We then conclude that robust prediction of TCR recognition is still far for being solved
Adrenocortical Carcinoma and CT Assessment of Therapy Response: The Value of Combining Multiple Criteria
We evaluated tumor response at Computed Tomography (CT) according to three radiologic criteria: RECIST 1.1, CHOI and tumor volume in 34 patients with metastatic adrenocortical carcinoma (ACC) submitted to standard chemotherapy. These three criteria agreed in defining partial response, stable or progressive disease in 24 patients (70.5%). Partial response (PR) was observed in 29.4%, 29.4% and 41.2% of patients according to RECIST 1.1, CHOI and tumor volume, respectively. It was associated with a favorable prognosis, regardless of the criterion adopted. The concordance of all the 3 criteria in defining the disease response identified 8 patients (23.5%) which displayed a very good prognosis: median progression free survival (PFS) and overall survival (OS) 14.9 and 37.7 months, respectively. Seven patients (20.6%) with PR assessed by one or two criteria, however, still had a better prognosis than non-responding patients, both in terms of PFS: median 12.3 versus 9.9 months and OS: 21 versus 12.2, respectively. In conclusions, the CT assessment of disease response of ACC patients to chemotherapy with 3 different criteria is feasible and allows the identification of a patient subset with a more favorable outcome. PR with at least one criterion can be useful to early identify patients that deserve continuing the therapy
Face Verification from Depth using Privileged Information
In this paper, a deep Siamese architecture for depth-based face verification is presented.
The proposed approach efficiently verifies if two face images belong to the same
person while handling a great variety of head poses and occlusions. The architecture,
namely JanusNet, consists in a combination of a depth, a RGB and a hybrid Siamese
network. During the training phase, the hybrid network learns to extract complementary
mid-level convolutional features which mimic the features of the RGB network, simultaneously
leveraging on the light invariance of depth images. At testing time, the model,
relying only on depth data, achieves state-of-art results and real time performance, despite
the lack of deep-oriented depth-based datasets