Soft Robot Locomotion via Mechanical Metamaterials: Application in Pipe Inspection

Pipe inspections are performed using large robots that utilize wheels or tracks for locomotion. Due to their large size, weight and hard exterior, these robots can occasionally cause damage to the pipe interiors during inspection. In addition, these pipe inspection robots struggle with the ability to move in a congested environment and adapt to obstacles or geometry changes within the pipe. This project investigates the capabilities of auxetic and conventional metamaterials to achieve locomotion in an enclosed channel through the different metamaterials reactions to an axial force. The resulting robot is capable of both horizontal and vertical locomotion. Computer simulation is used to confirm the metamaterials effective Poissons ratio through testing deformation under applied loads at small displacements. Physical testing of the soft-bodied robot is employed to demonstrate the force needed for movement and validate the auxetic and conventional metamaterial behavior. The extensive work serves as a proof of concept of auxetic metamaterials as a viable solution for less invasive movement through enclosed channels. Further work and alterations to the soft-bodied robot body may allow for future applications in realms such as medical device development

Zika virus outbreak and the case for building effective and sustainable rapid diagnostics laboratory capacity globally

New and re-emerging pathogens with epidemic potential have threatened global health security for the past century.1 As with the recent Ebola Virus Disease (EVD) epidemic, the Zika Virus (ZIKV) outbreak has yet again surprised and overwhelmed the international health community with an unexpected event for which it might have been better prepared

High-Precision Extraction of Emerging Concepts from Scientific Literature

Identification of new concepts in scientific literature can help power faceted search, scientific trend analysis, knowledge-base construction, and more, but current methods are lacking. Manual identification cannot keep up with the torrent of new publications, while the precision of existing automatic techniques is too low for many applications. We present an unsupervised concept extraction method for scientific literature that achieves much higher precision than previous work. Our approach relies on a simple but novel intuition: each scientific concept is likely to be introduced or popularized by a single paper that is disproportionately cited by subsequent papers mentioning the concept. From a corpus of computer science papers on arXiv, we find that our method achieves a Precision@1000 of 99%, compared to 86% for prior work, and a substantially better precision-yield trade-off across the top 15,000 extractions. To stimulate research in this area, we release our code and data (https://github.com/allenai/ForeCite).Comment: Accepted to SIGIR 202

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Noroviruses subvert the core stress granule component G3BP1 to promote viral VPg-dependent translation.

Knowledge of the host factors required for norovirus replication has been hindered by the challenges associated with culturing human noroviruses. We have combined proteomic analysis of the viral translation and replication complexes with a CRISPR screen, to identify host factors required for norovirus infection. The core stress granule component G3BP1 was identified as a host factor essential for efficient human and murine norovirus infection, demonstrating a conserved function across the Norovirus genus. Furthermore, we show that G3BP1 functions in the novel paradigm of viral VPg-dependent translation initiation, contributing to the assembly of translation complexes on the VPg-linked viral positive sense RNA genome by facilitating ribosome recruitment. Our data uncovers a novel function for G3BP1 in the life cycle of positive sense RNA viruses and identifies the first host factor with pan-norovirus pro-viral activity

Epitope-positive truncating MLH1 mutation and loss of PMS2: implications for IHC-directed genetic testing for lynch syndrome

We assessed mismatch repair by immunohistochemistry (IHC) and microsatellite instability (MSI) analysis in an early onset endometrial cancer and a sister’s colon cancer. We demonstrated high-level MSI and normal expression for MLH1, MSH2 and MSH6. PMS2 failed to stain in both tumors, strongly implicating a PMS2 defect. This family did not meet clinical criteria for Lynch syndrome. However, early onset endometrial cancers in the proband and her sister, a metachronous colorectal cancer in the sister as well as MSI in endometrial and colonic tumors suggested a heritable mismatch repair defect. PCR-based direct exonic sequencing and multiplex ligation-dependent probe amplification (MLPA) were undertaken to search for PMS2 mutations in the germline DNA from the proband and her sister. No mutation was identified in the PMS2 gene. However, PMS2 exons 3, 4, 13, 14, 15 were not evaluated by MLPA and as such, rearrangements involving those exons cannot be excluded. Clinical testing for MLH1 and MSH2 mutation revealed a germline deletion of MLH1 exons 14 and 15. This MLH1 germline deletion leads to an immunodetectable stable C-terminal truncated MLH1 protein which based on the IHC staining must abrogate PMS2 stabilization. To the best of our knowledge, loss of PMS2 in MLH1 truncating mutation carriers that express MLH1 in their tumors has not been previously reported. This family points to a potential limitation of IHC-directed gene testing for suspected Lynch syndrome and the need to consider comprehensive MLH1 testing for individuals whose tumors lack PMS2 but for whom PMS2 mutations are not identified

ReluDiff: Differential Verification of Deep Neural Networks

As deep neural networks are increasingly being deployed in practice, their efficiency has become an important issue. While there are compression techniques for reducing the network's size, energy consumption and computational requirement, they only demonstrate empirically that there is no loss of accuracy, but lack formal guarantees of the compressed network, e.g., in the presence of adversarial examples. Existing verification techniques such as Reluplex, ReluVal, and DeepPoly provide formal guarantees, but they are designed for analyzing a single network instead of the relationship between two networks. To fill the gap, we develop a new method for differential verification of two closely related networks. Our method consists of a fast but approximate forward interval analysis pass followed by a backward pass that iteratively refines the approximation until the desired property is verified. We have two main innovations. During the forward pass, we exploit structural and behavioral similarities of the two networks to more accurately bound the difference between the output neurons of the two networks. Then in the backward pass, we leverage the gradient differences to more accurately compute the most beneficial refinement. Our experiments show that, compared to state-of-the-art verification tools, our method can achieve orders-of-magnitude speedup and prove many more properties than existing tools.Comment: Extended version of ICSE 2020 paper. This version includes an appendix with proofs for some of the content in section 4.