Effect of a combined Napier grass-oil palm frond feed on the in vivo and in sacco rumen fermentation and digestibility in goats

In Malaysia, the lack of high-quality pasture remains the main factor slowing down the development of the ruminant livestock industry. The oil palm fronds (OPF), abundant and readily available agricultural by-products, appear as a promising solution, though they are unsuitable to be used as a sole feed. Therefore, this study evaluated a combined Napier grass (NP) and OPF (NP+OPF) feed by monitoring the digestibility, in vivo and in sacco, as well as the changes of rumen fermentation parameters (volatile fatty acids, rumen fluid pH and total protozoal counts). Fifteen two-year-old male rumen-fistulated Kacang crossbed goats were used and divided into three groups, where Treatment 1 group was fed 50% NP + 50% concentrate, Treatment 2 group 25% NP + 25% OPF + 50% concentrate, and Treatment 3 group 50% OPF + 50% concentrate. Following dietary adaptation of 10 days, the in vivo and in sacco digestibility and rumen fermentation parameters were determined. Compared to the 50% NP diet, the combined NP-OPF feed showed a significantly lower in sacco digestibility and total volatile fatty acid production (p< 0.05). However, it produced good in vivo digestibility, rumen pH and total protozoal counts which were comparable to the 50% NP diet and significantly better than the 50% oil palm frond feed. Thus, it is concluded that the combined NP-OPF diet is suitable as a ruminant feed

In vitro evaluation of Napier grass-oil palm frond combination as ruminant feed

The effects of different combinations of Napier grass (Pennisetum purpureum) and oil palm (Elaeis guineensis Jacq) fronds on ruminal fermentation patterns in vitro in goats were investigated. Rumen liquor from three 2-year-old Kacang-crossbred goats was mixed with buffer and substrates. Four dietary treatments were compared namely 100% concentrates (CON), 50% OPF with 50% concentrates (OPF 50), 50% Napier grass with 50% concentrates (NP 50), and 25% Napier grass, 25% OPF and 50% concentrates (NP-OPF)). Incubation of the mixture was carried out at 39°C for 24 h. Total gas production (GP) was recorded after 2, 4, 6, 8, 10, 12 and 24 h of incubation. Rumen fluid pH, methane gas, total volatile fatty acids and in vitro dry matter digestibility (IVDMD) were determined at the end of incubation. Long chain fatty acid (LCFA) profiles were obtained in separate runs to determine the apparent biohydrogenation (BH) of linoleic (C18:2n-6) and α-linolenic acids (C18:3n-3). Cumulative gas production was significantly higher for the CON group (P<0.05) but not significantly different in the other groups. The NP 50 diet produced significantly higher methane (P<0.05) while other groups did not differ significantly. For IVDMD, the NP-OPF group had a significantly higher digestibility than the NP 50 and OPF 50 groups. Rumen fluid pH, total VFA and apparent BH values for all treatments were not significantly different. In conclusion, the Napier-OPF combination represents a suitable feed for the small ruminant sector in Malaysia but more studies need to be done on effects of OPF on rumen biohydrogenation

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

Unintended Memorization and Timing Attacks in Named Entity Recognition Models

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning based NER models are increasingly being applied in privacy-sensitive applications that need automatic and scalable identification of sensitive information to redact text for data sharing. In this paper, we study the setting when NER models are available as a black-box service for identifying sensitive information in user documents and show that these models are vulnerable to membership inference on their training datasets. With updated pre-trained NER models from spaCy, we demonstrate two distinct membership attacks on these models. Our first attack capitalizes on unintended memorization in the NER's underlying neural network, a phenomenon NNs are known to be vulnerable to. Our second attack leverages a timing side-channel to target NER models that maintain vocabularies constructed from the training data. We show that different functional paths of words within the training dataset in contrast to words not previously seen have measurable differences in execution time. Revealing membership status of training samples has clear privacy implications, e.g., in text redaction, sensitive words or phrases to be found and removed, are at risk of being detected in the training dataset. Our experimental evaluation includes the redaction of both password and health data, presenting both security risks and privacy/regulatory issues. This is exacerbated by results that show memorization with only a single phrase. We achieved 70% AUC in our first attack on a text redaction use-case. We also show overwhelming success in the timing attack with 99.23% AUC. Finally we discuss potential mitigation approaches to realize the safe use of NER models in light of the privacy and security implications of membership inference attacks.Comment: This is the full version of the paper with the same title accepted for publication in the Proceedings of the 23rd Privacy Enhancing Technologies Symposium, PETS 202

SBSI:an extensible distributed software infrastructure for parameter estimation in systems biology

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI’s use of standard data formats

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

Planets Across Space and Time (PAST) IV: The Occurrence and Architecture of Kepler Planetary Systems as a Function of Kinematic Age Revealed by the LAMOST-Gaia-Kepler Sample

One of the fundamental questions in astronomy is how planetary systems form and evolve. Measuring the planetary occurrence and architecture as a function of time directly addresses this question. In the fourth paper of the Planets Across Space and Time (PAST) series, we investigate the occurrence and architecture of Kepler planetary systems as a function of kinematic age by using the LAMOST-Gaia-Kepler sample. To isolate the age effect, other stellar properties (e.g., metallicity) have been controlled. We find the following results. (1) The fraction of stars with Kepler-like planets ($F_{\text{Kep}}$) is about 50% for all stars; no significant trend is found between $F_{\text{Kep}}$ and age. (2) The average planet multiplicity ($\bar{N}_p$) exhibits a decreasing trend (~2$\sigma$ significance) with age. It decreases from $\bar{N}_p$~3 for stars younger than 1 Gyr to $\bar{N}_p$~1.8 for stars about 8 Gyr. (3) The number of planets per star ($\eta=F_{\text{Kep}}\times\bar{N}_p$) also shows a decreasing trend (~2-3$\sigma$ significance). It decreases from $\eta$~1.6-1.7 for young stars to $\eta$~1.0 for old stars. (4) The mutual orbital inclination of the planets ($\sigma_{i,k}$) increases from $1.2^{+1.4}_{-0.5}$ to $3.5^{+8.1}_{-2.3}$ as stars aging from 0.5 to 8 Gyr with a best fit of $\log{\sigma_{i,k}}=0.2+0.4\times\log{\frac{\text{Age}}{\text{1Gyr}}}$. Interestingly, the Solar System also fits such a trend. The nearly independence of $F_{\text{Kep}}$~50% on age implies that planet formation is robust and stable across the Galaxy history. The age dependence of $\bar{N}_p$ and $\sigma_{i,k}$ demonstrates planetary architecture is evolving, and planetary systems generally become dynamically hotter with fewer planets as they age.Comment: 27 pages, 20 figures, 4tables, accepted for publication in A