Fabrication of monodisperse magnetic nanorods for improving hyperthermia efficacy

Background: Hyperthermia is one of the promising cancer treatment strategies enabled by local heating with the use of tumor-targeting magnetic nanoparticles (MNP) under a non-invasive magnetic field. However, one of the remaining challenges is how to achieve therapeutic levels of heat (without causing damages to regular tissues) in tumors that cannot be effectively treated with anti-tumor drug delivery. Results: In this work, we report a facile method to fabricate magnetic nanorods for hyperthermia by one-step wet chemistry synthesis using 3-Aminopropyltrimethoxysilane (APTMS) as the shape-controlling agent and ferric and ferrous ions as precursors. By adjusting the concentration of APTMS, hydrothermal reaction time, ratios of ferric to ferrous ions, magnetic nanorods with aspect ratios ranging from 4.4 to 7.6 have been produced. At the clinically recommended field strength of 300 Oe (or less) and the frequency of 184 kHz, the specific absorption rate (SAR) of these nanorods is approximately 50 % higher than that of commercial Bionized NanoFerrite particles. Conclusions: This increase in SAR, especially at low field strengths, is crucial for treating deep tumors, such as pancreatic and rectal cancers, by avoiding the generation of harmful eddy current heating in normal tissues.[Figure not available: see fulltext.

Those Aren't Your Memories, They're Somebody Else's: Seeding Misinformation in Chat Bot Memories

One of the new developments in chit-chat bots is a long-term memory mechanism that remembers information from past conversations for increasing engagement and consistency of responses. The bot is designed to extract knowledge of personal nature from their conversation partner, e.g., stating preference for a particular color. In this paper, we show that this memory mechanism can result in unintended behavior. In particular, we found that one can combine a personal statement with an informative statement that would lead the bot to remember the informative statement alongside personal knowledge in its long term memory. This means that the bot can be tricked into remembering misinformation which it would regurgitate as statements of fact when recalling information relevant to the topic of conversation. We demonstrate this vulnerability on the BlenderBot 2 framework implemented on the ParlAI platform and provide examples on the more recent and significantly larger BlenderBot 3 model. We generate 150 examples of misinformation, of which 114 (76%) were remembered by BlenderBot 2 when combined with a personal statement. We further assessed the risk of this misinformation being recalled after intervening innocuous conversation and in response to multiple questions relevant to the injected memory. Our evaluation was performed on both the memory-only and the combination of memory and internet search modes of BlenderBot 2. From the combinations of these variables, we generated 12,890 conversations and analyzed recalled misinformation in the responses. We found that when the chat bot is questioned on the misinformation topic, it was 328% more likely to respond with the misinformation as fact when the misinformation was in the long-term memory.Comment: To be published in 21st International Conference on Applied Cryptography and Network Security, ACNS 202

Re-examining the premise of isobaric collisions and a novel method to measure the chiral magnetic effect

In these proceedings we show that the premise of the isobaric and collisions to search for the chiral magnetic effect (CME) may not hold as originally anticipated due to large uncertainties in the isobaric nuclear structures. We demonstrate this using Woods-Saxon densities and the proton and neutron densities calculated by the density functional theory. Furthermore, a novel method is proposed to gauge background and possible CME contributions in the same system, intrinsically better than the isobaric collisions of two different systems. We illustrate the method with Monte Carlo Glauber and AMPT (A Multi-Phase Transport) simulations

On the Resilience of Biometric Authentication Systems against Random Inputs

We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.Comment: Accepted by NDSS2020, 18 page