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Recent Submissions

  • Investigation of a Separated Short-Wavelength Peak in InGaN Red Light-Emitting Diodes

    Kirilenko, Pavel; Zhuang, Zhe; Iida, Daisuke; Velazquez-Rizo, Martin; Ohkawa, Kazuhiro (Crystals, MDPI AG, 2021-09-15) [Article]
    We fabricated indium gallium nitride (InGaN) red light-emitting diodes (LEDs) with a peak emission wavelength of 649 nm and investigated their electroluminescence (EL) properties. An additional separated peak in the EL spectrum of the red LEDs at 20 mA was observed at 465 nm. This additional peak also exhibits a blue-shift with increasing currents as does the main emission peak. Using high-resolution microscopy, we observed many point-like emission spots in the EL emission images at the currents below 1 mA. However, these emission spots cannot be identified at currents above 5 mA because the red emission from quantum wells (QWs) is much stronger than that emitted by these spots. Finally, we demonstrate that these emission spots are related to the defects generated in red QWs. The measured In content was lower at the vicinity of the defects, which was regarded as the reason for separated short-wavelength emission in red InGaN LEDs.
  • Capturing 3D atomic defects and phonon localization at the 2D heterostructure interface

    Tian, Xuezeng; Yan, Xingxu; Varnavides, Georgios; Yuan, Yakun; Kim, Dennis S.; Ciccarino, Christopher J.; Anikeeva, Polina; Li, Ming-yang; Li, Lain-Jong; Narang, Prineha; Pan, Xiaoqing; Miao, Jianwei (Science Advances, American Association for the Advancement of Science (AAAS), 2021-09-15) [Article]
    The three-dimensional (3D) local atomic structures and crystal defects at the interfaces of heterostructures control their electronic, magnetic, optical, catalytic, and topological quantum properties but have thus far eluded any direct experimental determination. Here, we use atomic electron tomography to determine the 3D local atomic positions at the interface of a MoS2-WSe2 heterojunction with picometer precision and correlate 3D atomic defects with localized vibrational properties at the epitaxial interface. We observe point defects, bond distortion, and atomic-scale ripples and measure the full 3D strain tensor at the heterointerface. By using the experimental 3D atomic coordinates as direct input to first-principles calculations, we reveal new phonon modes localized at the interface, which are corroborated by spatially resolved electron energy-loss spectroscopy. We expect that this work will pave the way for correlating structure-property relationships of a wide range of heterostructure interfaces at the single-atom level.
  • Two-Dimensional TiO2/TiS2 Hybrid Nanosheet Anodes for High-Rate Sodium-Ion Batteries

    Bayhan, Zahra; Huang, Gang; Yin, Jian; Xu, Xiangming; Lei, Yongjiu; Liu, Zhixiong; Alshareef, Husam N. (ACS Applied Energy Materials, American Chemical Society (ACS), 2021-09-15) [Article]
    The sodium-ion battery (NIB) is promising for next-generation energy storage systems. One promising anode material is titanium dioxide (TiO2). However, the sluggish sodiation/desodiation kinetics of TiO2 hinders its application in NIBs. Herein, we converted TiO2 into a two-dimensional (2D) TiO2/TiS2 hybrid to improve its sodium storage capability. The 2D TiO2/TiS2 hybrid nanosheet electrode provides high kinetics and excellent cycling performance for sodium-ion storage. This work provides a promising strategy to develop 2D hybrid nanomaterials for high-performance sodium storage devices.
  • High Throughput Printing of Two-Dimensional Materials into Wafer-scale Three-dimensional Architectures

    Wei, Xuan; Lin, Chia-Ching; Wu, Christine; Lu, Ang-Yu; Qaiser, Nadeem; Cai, Yichen; Fu, Jui-Han; Chiang, Yu-Hsiang; ding, lianhui; Ali, Ola; Xu, Wei; Zhang, Wenli; Kong, Jing; Chen, Han-Yi; Tung, Vincent (Research Square Platform LLC, 2021-09-15) [Preprint]
    Architected materials that actively respond to external stimuli hold tantalizing prospects for applications in energy storage, harvesting, wearable electronics and bioengineering. Transition metal dichalcogenides (TMDs) which represent the three-atom-thick, two-dimensional (2D) building blocks, are excellent candidates but have found limited success compared to metallic, inorganic, and organic counterparts due to the lack of up-scalable manufacturing. Here we report the high-throughput printing of 2D TMDs into wafer-scale 3D architectures with structural hierarchy across seven orders of magnitude between critical feature sizes. Anode made of 3D MoS2 architectures comprises the concentric vortex-like intricacy that unites technological merits from architecture, mechanical engineering, and electrochemistry not found in its bulk or exfoliated/epitaxy counterparts. The result is, contrary to expectation, the high-rate, high-capacity, and high-loading lithium (Li)-storage, surpassing those state-of-the-art anode designs while the technique offers an evaporation-like simplicity for industrial scalability.
  • Janus monolayers of magnetic transition metal dichalcogenides as an all-in-one platform for spin-orbit torque

    Smaili, Idris; Laref, Slimane; Garcia, Jose H.; Schwingenschlögl, Udo; Roche, Stephan; Manchon, Aurelien (Physical Review B, American Physical Society (APS), 2021-09-15) [Article]
    We theoretically predict that vanadium-based Janus dichalcogenide monolayers constitute an ideal platform for spin-orbit torque memories. Using first-principles calculations, we demonstrate that magnetic exchange and magnetic anisotropy energies are higher for heavier chalcogen atoms, while the broken inversion symmetry in the Janus form leads to the emergence of Rashba-like spin-orbit coupling. The spin-orbit torque efficiency is evaluated using optimized quantum transport methodology and found to be comparable to heavy nonmagnetic metals. The coexistence of magnetism and spin-orbit coupling in such materials with tunable Fermi-level opens new possibilities for monitoring magnetization dynamics in the perspective of nonvolatile magnetic random access memories.
  • The Internet of Bodies: The Human Body as an Efficient and Secure Wireless Channel

    Celik, Abdulkadir; Eltawil, Ahmed (Submitted to IEEE Communications Magazine, Submitted to IEEE, 2021-09-14) [Preprint]
    Taking a cue from the Internet of Things, the Internet of Bodies (IoB) can be defined as a network of smart objects placed in, on, and around the human body, allowing for intra- and inter-body communications. This position paper aims to provide a glimpse into the opportunities created by implantable, injectable, ingestible, and wearable IoB devices. The paper starts with a thorough discussion of application-specific design goals, technical challenges, and enabling of communication standards. We discuss the reason that the highly radiative nature of radio frequency (RF) systems results in inefficient systems due to over-extended coverage that causes interference and becomes susceptible to eavesdropping. Body channel communication (BCC) presents an attractive, alternative wireless technology by inherently coupling signals to the human body, resulting in highly secure and efficient communications. The conductive nature of body tissues yields a better channel quality, while the BCC’s operational frequency range (1-100 kHz) eliminates the need for radio front-ends. Stateof-the-art BCC transceivers can reach several tens of Mbps data rates at pJ/b energy efficiency levels that support IoB devices and applications. Furthermore, as the cyber and biological worlds meet, security risks and privacy concerns take center stage, leading to a discussion of the multi-faceted legal, societal, ethical, and political issues related to technology governance.
  • Recent Progress on Polymers of Intrinsic Microporosity and Thermally Modified Analogue Materials for Membrane-Based Fluid Separations

    Wang, Yingge; Ghanem, Bader; Ali, Zain; Hazazi, Khalid; Han, Yu; Pinnau, Ingo (Small Structures, Wiley, 2021-09-14) [Article]
    Solution-processable amorphous glassy polymers of intrinsic microporosity (PIMs) are promising microporous organic materials for membrane-based gas and liquid separations due to their high surface area and internal free volume, thermal and chemical stability, and excellent separation performance. This review provides an overview of the most recent developments in the design and transport properties of novel ladder PIM materials, polyimides of intrinsic microporosity (PIM–PIs), functionalized PIMs and PIM–PIs, PIM-derived thermally rearranged (TR), and carbon molecular sieve (CMS) membrane materials as well as PIM-based thin film composite membranes for a wide range of energy-intensive gas and liquid separations. In less than two decades, PIMs have significantly lifted the performance upper bounds in H2/N2, H2/CH4, O2/N2, CO2/N2, and CO2/CH4 separations. However, PIMs are still limited by their insufficient gas-pair selectivity to be considered as promising materials for challenging industrial separations such as olefin/paraffin separations. An optimum pore size distribution is required to further improve the selectivity of a PIM for a given application. Specific attention is given to the potential use of PIM-based CMS membranes for energy-intensive CO2/CH4, N2/CH4, C2H4/C2H6, and C3H6/C3H8 separations, and thin film composite membranes containing PIM motifs for liquid separations.
  • The time course of molecular acclimation to seawater in a euryhaline fish.

    Bonzi, Lucrezia C; Monroe, Alison; Lehmann, Robert; Berumen, Michael L.; Ravasi, Timothy; Schunter, Celia (Scientific reports, Springer Science and Business Media LLC, 2021-09-14) [Article]
    The Arabian pupfish, Aphanius dispar, is a euryhaline fish inhabiting both inland nearly-freshwater desert ponds and highly saline Red Sea coastal lagoons of the Arabian Peninsula. Desert ponds and coastal lagoons, located respectively upstream and at the mouths of dry riverbeds ("wadies"), have been found to potentially become connected during periods of intense rainfall, which could allow the fish to migrate between these different habitats. Flash floods would therefore flush Arabian pupfish out to sea, requiring a rapid acclimation to a greater than 40 ppt change in salinity. To investigate the molecular pathways of salinity acclimation during such events, a Red Sea coastal lagoon and a desert pond population were sampled, with the latter exposed to a rapid increase in water salinity. Changes in branchial gene expression were investigated via genome-wide transcriptome measurements over time from 6 h to 21 days. The two natural populations displayed basal differences in genes related to ion transport, osmoregulation and immune system functions. These mechanisms were also differentially regulated in seawater transferred fish, revealing their crucial role in long-term adaptation. Other processes were only transiently activated shortly after the salinity exposure, including cellular stress response mechanisms, such as molecular chaperone synthesis and apoptosis. Tissue remodelling processes were also identified as transient, but took place later in the timeline, suggesting their importance to long-term acclimation as they likely equip the fish with lasting adaptations to their new environment. The alterations in branchial functional pathways displayed by Arabian pupfish in response to salinity increases are diverse. These reveal a large toolkit of molecular processes important for adaptation to hyperosmolarity that allow for successful colonization to a wide variety of different habitats.
  • Comprehensive analytical approaches reveal species-specific search strategies in sympatric apex predatory sharks

    Calich, Hannah J.; Rodríguez, J. P.; Eguíluz, V. M.; Hammerschlag, Neil; Pattiaratchi, Charitha; Duarte, Carlos M.; Sequeira, Ana M.M. (Ecography, Wiley, 2021-09-14) [Article]
    Animals follow specific movement patterns and search strategies to maximize encounters with essential resources (e.g. prey, favourable habitat) while minimizing exposures to suboptimal conditions (e.g. competitors, predators). While describing spatiotemporal patterns in animal movement from tracking data is common, understanding the associated search strategies employed continues to be a key challenge in ecology. Moreover, studies in marine ecology commonly focus on singular aspects of species' movements, however using multiple analytical approaches can further enable researchers to identify ecological phenomena and resolve fundamental ecological questions relating to movement. Here, we used a set of statistical physics-based methods to analyze satellite tracking data from three co-occurring apex predators (tiger, great hammerhead and bull sharks) that predominantly inhabit productive coastal regions of the northwest Atlantic Ocean and Gulf of Mexico. We analyzed data from 96 sharks and calculated a range of metrics, including each species' displacements, turning angles, dispersion, space-use and community-wide movement patterns to characterize each species' movements and identify potential search strategies. Our comprehensive approach revealed high interspecific variability in shark movement patterns and search strategies. Tiger sharks displayed near-random movements consistent with a Brownian strategy commonly associated with movements through resource-rich habitats. Great hammerheads showed a mixed-movement strategy including Brownian and resident-type movements, suggesting adaptation to widespread and localized high resource availability. Bull sharks followed a resident movement strategy with restricted movements indicating localized high resource availability. We hypothesize that the species-specific search strategies identified here may help foster the co-existence of these sympatric apex predators. Following this comprehensive approach provided novel insights into spatial ecology and assisted with identifying unique movement and search strategies. Similar future studies of animal movement will help characterize movement patterns and also enable the identification of search strategies to help elucidate the ecological drivers of movement and to understand species' responses to environmental change.
  • Computational Imaging and Its Applications in Fluids

    Xiong, Jinhui (2021-09-13) [Dissertation]
    Advisor: Heidrich, Wolfgang
    Committee members: Ghanem, Bernard; Wonka, Peter; Schindler, Konrad
    Computational imaging di↵ers from traditional imaging system by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D- 3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color (a “rainbow”). For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. This velocity field can be used to refine the position estimate by adding physical priors that tie together all the time steps, forming a joint reconstruction scheme. In the second approach, we study the problem of reconstructing the 3D shape of underwater environments. The distortions from the moving water surface provide a changing parallax for each point on the underwater surface. We utilize this observation by jointly estimating both the underwater geometry and the dynamic shape of the water surface. To this end, we propose a novel di↵erentiable framework to tie together all parameters in an integrated image formation model. To our knowledge, this is the first solution that is capable to simultaneously retrieve the structure of dynamic water surfaces and static underwater scene geometry in the wild.
  • An Aqueous Mg 2+ -Based Dual-Ion Battery with High Power Density

    Zhu, Yunpei; Yin, Jun; Emwas, Abdul-Hamid; Mohammed, Omar F.; Alshareef, Husam N. (Advanced Functional Materials, Wiley, 2021-09-13) [Article]
    Rechargeable Mg batteries promise low-cost, safe, and high-energy alternatives to Li-ion batteries. However, the high polarization strength of Mg2+ leads to its strong interaction with electrode materials and electrolyte molecules, resulting in sluggish Mg2+ dissociation and diffusion as well as insufficient power density and cycling stability. Here an aqueous Mg2+-based dual-ion battery is reported to bypass the penalties of slow dissociation and solid-state diffusion. This battery chemistry utilizes fast redox reactions on the polymer electrodes, i.e., anion (de)doping on the polyaniline (PANI) cathode and (de)enolization upon incorporating Mg2+ on the polyimide anode. The kinetically favored and stable electrodes depend on designing a saturated aqueous electrolyte of 4.5 m Mg(NO3)2. The concentrated electrolyte suppresses the irreversible deprotonation reaction of the PANI cathode to enable excellent stability (a lifespan of over 10 000 cycles) and rate performance (33% capacity retention at 500 C) and avoids the anodic parasitic reaction of nitrate reduction to deliver the stable polyimide anode (86.2% capacity retention after 6000 cycles). The resultant full Mg2+-based dual-ion battery shows a high specific power of 10 826 W kg−1, competitive with electrochemical supercapacitors. The electrolyte and electrode chemistries elucidated in this study provide an alternative approach to developing better-performing Mg-based batteries.
  • Clean Carbon Cycle via High-Performing and Low-Cost Solar-Driven Production of Freshwater

    Mazzone, Valerio; Bonifazi, Marcella; Aegerter, Christof M.; Cruz, Aluizio M.; Fratalocchi, Andrea (Advanced Sustainable Systems, Wiley, 2021-09-12) [Article]
    While renewable power available worldwide costs increasingly less than the least expensive option based on fossil fuels, countries continue to increase their coal-fired capacity, which should conversely fall by 80% within a decade to limit global warming effects. To address the challenges to the implementation of such an aim, here, a path is explored that leverages on a previously unrecognized aspect of coal, opening to a new solar-driven carbon cycle that is environmentally friendly. By engineering the porosity matrix of coal into a suitably designed compressed volumetric structure, and by coupling it with a network of cotton fibers, it is possible to create a record performing device for freshwater production, with a desalination rate per raw material cost evaluated at 1.39 kg h −1 $ −1 at one sun intensity. This value is between two and three times higher than any other solar desalination device proposed to date. These results could envision a clean and socially sustainable cycle for carbon materials that, while enabling an enhanced water economy with global access to freshwater and sanitation, poses zero risks of reinjecting 𝐶𝑂2 into the environment through competing economies in the fossil's market.
  • Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study

    Zerrouki, Nabil; Dairi, Abdelkader; Harrou, Fouzi; Zerrouki, Yacine; Sun, Ying (Concurrency and Computation: Practice and Experience, Wiley, 2021-09-12) [Article]
    Precisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like-desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)-based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi-temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN-based detector is constructed based on desertification-free features and then employed to identify atypical events associated with desertification changes. The GAN-detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN-based desertification detector's performance has been assessed via multi-temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state-of-the-art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN-based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN-based method for the analysis and detection of desertification changes. Results also revealed that the GAN-driven desertification detection approach outperforms the state-of-the-art methods.
  • Interfacial Model Deciphering High-Voltage Electrolytes for High Energy Density, High Safety, and Fast-Charging Lithium-Ion Batteries

    Zou, Yeguo; Cao, Zhen; Zhang, Junli; Wahyudi, Wandi; Wu, Yingqiang; Liu, Gang; Li, Qian; Cheng, Haoran; Zhang, Dongyu; Park, Geon-Tae; Cavallo, Luigi; Anthopoulos, Thomas D.; Wang, Limin; Sun, Yang-Kook; Ming, Jun (Advanced Materials, Wiley, 2021-09-12) [Article]
    High-voltage lithium-ion batteries (LIBs) enabled by high-voltage electrolytes can effectively boost energy density and power density, which are critical requirements to achieve long travel distances, fast-charging, and reliable safety performance for electric vehicles. However, operating these batteries beyond the typical conditions of LIBs (4.3 V vs Li/Li+) leads to severe electrolyte decomposition, while interfacial side reactions remain elusive. These critical issues have become a bottleneck for developing electrolytes for applications in extreme conditions. Herein, an additive-free electrolyte is presented that affords high stability at high voltage (4.5 V vs Li/Li+), lithium-dendrite-free features upon fast-charging operations (e.g., 162 mAh g−1 at 3 C), and superior long-term battery performance at low temperature. More importantly, a new solvation structure-related interfacial model is presented, incorporating molecular-scale interactions between the lithium-ion, anion, and solvents at the electrolyte–electrode interfaces to help interpret battery performance. This report is a pioneering study that explores the dynamic mutual-interaction interfacial behaviors on the lithium layered oxide cathode and graphite anode simultaneously in the battery. This interfacial model enables new insights into electrode performances that differ from the known solid electrolyte interphase approach to be revealed, and sets new guidelines for the design of versatile electrolytes for metal-ion batteries.
  • Uncertain logistic and Box-Cox regression analysis with maximum likelihood estimation

    Fang, Liang; Hong, Yiping; Zhou, Zaiying; Chen, Wenhui (Communications in Statistics - Theory and Methods, Informa UK Limited, 2021-09-12) [Article]
    Although the maximum likelihood estimation (MLE) for the uncertain discrete models has long been an academic interest, it has yet to be proposed in the literature. Thus, this study proposes the uncertain MLE for discrete models in the framework of the uncertainty theory, such as the uncertain logistic regression model. We also generalize the estimation proposed by Lio and Liu and obtain the uncertain MLE for non-linear continuous models, such as the uncertain Box-Cox regression model. Our proposed methods provide a useful tool for making inferences regarding non-linear data that is precisely or imprecisely observed, especially data based on degrees of belief, such as an expert’s experimental data. We demonstrate our methodology by calculating proposed estimates and providing forecast values and confidence intervals for numerical examples. Moreover, we evaluate our proposed models via residual analysis and the cross-validation method. The study enriches the definition of the uncertain MLE, thus making it easy to construct estimation and prediction methods for general uncertainty models.
  • H2Opus: A distributed-memory multi-GPU software package for non-local operators

    Zampini, Stefano; Boukaram, Wagih Halim; Turkiyyah, George; Knio, Omar; Keyes, David E. (arXiv, 2021-09-12) [Preprint]
    Hierarchical $\mathcal{H}^2$-matrices are asymptotically optimal representations for the discretizations of non-local operators such as those arising in integral equations or from kernel functions. Their $O(N)$ complexity in both memory and operator application makes them particularly suited for large-scale problems. As a result, there is a need for software that provides support for distributed operations on these matrices to allow large-scale problems to be represented. In this paper, we present high-performance, distributed-memory GPU-accelerated algorithms and implementations for matrix-vector multiplication and matrix recompression of hierarchical matrices in the $\mathcal{H}^2$ format. The algorithms are a new module of H2Opus, a performance-oriented package that supports a broad variety of $\mathcal{H}^2$-matrix operations on CPUs and GPUs. Performance in the distributed GPU setting is achieved by marshaling the tree data of the hierarchical matrix representation to allow batched kernels to be executed on the individual GPUs. MPI is used for inter-process communication. We optimize the communication data volume and hide much of the communication cost with local compute phases of the algorithms. Results show near-ideal scalability up to 1024 NVIDIA V100 GPUs on Summit, with performance exceeding 2.3 Tflop/s/GPU for the matrix-vector multiplication, and 670 Gflops/s/GPU for matrix compression, which involves batched QR and SVD operations. We illustrate the flexibility and efficiency of the library by solving a 2D variable diffusivity integral fractional diffusion problem with an algebraic multigrid-preconditioned Krylov solver and demonstrate scalability up to 16M degrees of freedom problems on 64 GPUs.
  • Naturally Extracted Hydrophobic Solvent and Self-Assembly in Interfacial Polymerization

    Falca, Gheorghe; Musteata, Valentina-Elena; Chisca, Stefan; Hedhili, Mohamed N.; Ong, Chi Siang; Nunes, Suzana Pereira (ACS Applied Materials & Interfaces, American Chemical Society (ACS), 2021-09-12) [Article]
    Pharmaceutical, chemical, and food industries are actively implementing membrane nanofiltration modules in their processes to separate valuable products and recover solvents. Interfacial polymerization (IP) is the most widely used method to produce thin-film composite membranes for nanofiltration and reverse osmosis processes. Although membrane processes are considered green and environmentally friendly, membrane fabrication has still to be further developed in such direction. For instance, the emission of volatile solvents during membrane production in the industry has to be carefully controlled for health reasons. Greener solvents are being proposed for phase-separation membrane manufacture. For the IP organic phase, the proposition of greener alternatives is in an early stage. In this work, we demonstrate the preparation of a high-performing composite membrane employing zero vapor pressure and naturally extracted oleic acid as the IP organic phase. Its long hydrophobic chain ensures intrinsic low volatility and acid monomer dissolution, while the polar head induces a unique self-assembly structure during the film formation. Membranes prepared by this technique were selective for small molecules with a molecular weight cutoff of 650 g mol–1 and a high permeance of ∼57 L m–2 h–1 bar–1.
  • Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain

    Hammoud, Hasan Abed Al Kader; Ghanem, Bernard (arXiv, 2021-09-12) [Preprint]
    Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classification and facial recognition to medical imagery analysis and real-time object detection. As our models become more sophisticated and complex, the computational cost of training such models becomes a burden for small companies and individuals; for this reason, outsourcing the training process has been the go-to option for such users. Unfortunately, outsourcing the training process comes at the cost of vulnerability to backdoor attacks. These attacks aim at establishing hidden backdoors in the DNN such that the model performs well on benign samples but outputs a particular target label when a trigger is applied to the input. Current backdoor attacks rely on generating triggers in the image/pixel domain; however, as we show in this paper, it is not the only domain to exploit and one should always "check the other doors". In this work, we propose a complete pipeline for generating a dynamic, efficient, and invisible backdoor attack in the frequency domain. We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks through extensive experiments on various datasets and network architectures. The backdoored models are shown to break various state-of-the-art defences. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them. We conclude the work with some remarks regarding a network's learning capacity and the capability of embedding a backdoor attack in the model.
  • MovieCuts: A New Dataset and Benchmark for Cut Type Recognition

    Pardo, Alejandro; Heilbron, Fabian Caba; Alcázar, Juan León; Thabet, Ali Kassem; Ghanem, Bernard (arXiv, 2021-09-12) [Preprint]
    Understanding movies and their structural patterns is a crucial task to decode the craft of video editing. While previous works have developed tools for general analysis such as detecting characters or recognizing cinematography properties at the shot level, less effort has been devoted to understanding the most basic video edit, the Cut. This paper introduces the cut type recognition task, which requires modeling of multi-modal information. To ignite research in the new task, we construct a large-scale dataset called MovieCuts, which contains more than 170K videoclips labeled among ten cut types. We benchmark a series of audio-visual approaches, including some that deal with the problem's multi-modal and multi-label nature. Our best model achieves 45.7% mAP, which suggests that the task is challenging and that attaining highly accurate cut type recognition is an open research problem.
  • Cancer-associated mutations in the p85α N-terminal SH2 domain activate a spectrum of receptor tyrosine kinases.

    Li, Xinran; Lau, Amy Y T; Ng, Angel S N; Aldehaiman, Abdullah; Zhou, Yuan; Ng, Patrick K S; Arold, Stefan T.; Cheung, Lydia W T (Proceedings of the National Academy of Sciences of the United States of America, 2021-09-11) [Article]
    The phosphoinositide 3-kinase regulatory subunit p85α is a key regulator of kinase signaling and is frequently mutated in cancers. In the present study, we showed that in addition to weakening the inhibitory interaction between p85α and p110α, a group of driver mutations in the p85α N-terminal SH2 domain activated EGFR, HER2, HER3, c-Met, and IGF-1R in a p110α-independent manner. Cancer cells expressing these mutations exhibited the activation of p110α and the AKT pathway. Interestingly, the activation of EGFR, HER2, and c-Met was attributed to the ability of driver mutations to inhibit HER3 ubiquitination and degradation. The resulting increase in HER3 protein levels promoted its heterodimerization with EGFR, HER2, and c-Met, as well as the allosteric activation of these dimerized partners; however, HER3 silencing abolished this transactivation. Accordingly, inhibitors of either AKT or the HER family reduced the oncogenicity of driver mutations. The combination of these inhibitors resulted in marked synergy. Taken together, our findings provide mechanistic insights and suggest therapeutic strategies targeting a class of recurrent p85α mutations.

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