Miracle-dz/UniFAR-FTUs huggingface dataset:
A Sporulation-Independent Way of Life for Bacillus thuringiensis in the Late Stages of an Infection

Bacillus thuringiensis is an entomopathogenic bacterium widely used as a biopesticide. It belongs to the Bacillus cereus group, comprising the foodborne pathogen B. cereus sensu stricto and the anthrax agent Bacillus anthracis. ABSTRACT The formation of endospores has been considered the unique survival and transmission mode of sporulating Firmicutes due to the exceptional resistance and persistence of this bacterial form. However, nonsporulated bacteria (Spo–) were reported at the early stages following the death of a host infected with Bacillus thuringiensis, an entomopathogenic sporulating bacterium. Here, we investigated the characteristics of the bacterial population in the late stages of an infection in the B. thuringiensis/Galleria mellonella infection model. Using fluorescent reporters and molecular markers coupled to flow cytometry, we demonstrated that the Spo– cells persist and constitute about half of the population 2 weeks post-infection (p.i.). Protein synthesis and growth recovery assays indicated that they are in a metabolically slowed-down state. These bacteria were extremely resistant to the insect cadaver environment, which did not support growth of in vitro-grown vegetative cells and spores. A transcriptomic analysis of this subpopulation at 7 days p.i. revealed a signature profile of this state, and the expression analysis of individual genes at the cell level showed that more bacteria mount an oxidative stress response as their survival time increases, in agreement with the increase of the free radical level in the host cadaver and in the number of reactive oxygen species (ROS)-producing bacteria. Altogether, these data show for the first time that nonsporulated bacteria are able to survive for a prolonged period of time in the context of an infection and indicate that they engage in a profound adaptation process that leads to their persistence in the host cadaver. IMPORTANCE Bacillus thuringiensis is an entomopathogenic bacterium widely used as a biopesticide. It belongs to the Bacillus cereus group, comprising the foodborne pathogen B. cereus sensu stricto and the anthrax agent Bacillus anthracis. Like other Firmicutes when they encounter harsh conditions, these Gram-positive bacteria can form dormant cells called spores. Due to its highly resistant nature, the spore was considered the unique mode of long-term survival, eclipsing any other form of persistence. Breaking this paradigm, we observed that B. thuringiensis was able to persist in its host cadaver in a nonsporulated form for at least 14 days. Our results show that these bacteria survived in the cadaver environment, which proved hostile for actively growing bacteria by engaging in a profound adaptation process. Studying this facet of the life cycle of a sporulating bacterium provides new fundamental knowledge and might lead to the development of strategies to combat sporulating pathogenic species.
Spiking Pitch Black: Poisoning an Unknown Environment to Attack Unknown Reinforcement Learners

As reinforcement learning (RL) systems are deployed in various safety-critical applications, it is imperative to understand how vulnerable they are to adversarial attacks. Of these, an environmentpoisoning attack (EPA) is considered particularly insidious, since environment hyper-parameters are significant factors in determining an RL policy, yet prone to be accessed by third parties. The success of EPAs relies on comprehensive prior knowledge of the attacked RL system, including RL agent’s learning mechanism and/or its environment model. Unfortunately, such an assumption of prior knowledge creates an unrealistic attack, one that poses limited threat to real-world RL systems. In this paper, we propose a Double-Black-Box EPA framework, only assuming the attacker’s ability to alter environment hyperparameters. Considering that environment alteration comes at a cost, we seek minimal poisoning in an unknown environment and aim to force a black-box RL agent to learn an attacker-designed policy. To this end, we incorporate an inference module in our framework to capture the internal information of an unknown RL system and, accordingly, learn an adaptive strategy based on an approximation of our attack objective. We empirically show the threat posed by our attack to both tabular-RL and deep-RL algorithms, in both discrete and continuous environments.
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning

Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This challenge is amplified in multi-agent reinforcement learning (MARL) where credit assignment of these rewards needs to happen not only across time, but also across agents. We propose Agent-Time Attention (ATA), a neural network model with auxiliary losses for redistributing sparse and delayed rewards in collaborative MARL. We provide a simple example that demonstrates how providing agents with their own local redistributed rewards and shared global redistributed rewards motivate different policies. We extend several MiniGrid environments, specifically MultiRoom and DoorKey, to the multi-agent sparse delayed rewards setting. We demonstrate that ATA outperforms various baselines on many instances of these environments. Source code of the experiments is available at https://github.com/jshe/agent-time-attention.
Exploration and Communication for Partially Observable Collaborative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. The cooperative multi-agent setting introduces numerous challenges compared to the single-agent setting, such as the moving target problem and the curse of dimensionality with respect to the action space. It also aggravates the credit assignment problem, as the credit is not only spread across a sequence of actions, but also multiple agents. This setting also introduces new possibilities, such as task parallelization, specialization, and communication. The Centralized Training with Decentralized Ex-ecution paradigm has emerged as a popular strategy to mitigate some of the difficulties in MARL, while still ensuring that the policy of the agents is only conditioned on their local history. However, how to fully leverage this paradigm is still an open question. During the first year of my Ph.D., I developed a novel algorithm, Local Advantage Networks (LAN), that proposes an alternative direction to value factorization, that is more scalable, not limited in its representation and state-of-the-art. The next parts of my research will focus on multi-agent exploration and learning to communicate
Chameleon - A Framework for Developing Conversational Agents for Medical Training Purposes

Objective Clinical Structured Examination (OSCE) is used to assess multiple competencies in medical pedagogy such as efficiently eliciting relevant clinical history from a patient. Clinical interviewing is done in a question and answer fashion, making it amenable to computer simulation. We introduce Chameleon, a framework to create virtual patients in an OSCE setting using the conversational agent platform Dialogflow CX. Our framework consists of a generic chatbot that is capable of answering most questions (in a classic non-specific clinical interview) and which can be expanded to capture any clinical presentation, e.g. a patient with backpain.
Building Contrastive Explanations for Multi-Agent Team Formation

As more and more hard and complex procedures are being automated with the aid of artificial intelligence, the need for humans to understand the rationale behind AI decisions becomes imperative. Adequate explanations for decisions made by an intelligent system do not just help describing how the system works, they also earn users’ trust. In this work we focus on a general methodology for justifying why certain teams are formed and others are not by a team formation algorithm (TFA). Specifically, we introduce an algorithm that wraps up any existing TFA and builds justifications regarding the teams formed by such TFA. This is done without modifying the TFA in any way. Our algorithm offers users a collection of commonly-asked questions within a team formation scenario and builds justifications as contrastive explanations. We also report on an empirical evaluation to determine the quality of the explanations provided by our algorithm.
Concise Representations and Complexity of Combinatorial Assignment Problems

We consider the computational problem of partitioning items into bundles among alternatives to maximize social welfare. Unfortunately, many important classes of this problem are computationally hard, including well-known instances in the multi-agent systems literature. In this paper we analyze novel concise representations and restrictions that admit polynomial-time algorithms for many such combinatorial assignment problems, and prove several complexity results for them. We provide efficient approximation algorithms and non-trivial exponential-time algorithms for the hard cases
Approaching the Overbidding Puzzle in All-Pay Auctions: Explaining Human Behavior through Bayesian Optimization and Equilibrium Learning

It is an established fact in behavioral economics that in lab experiments of auctions, human subjects do not adhere to the risk-neutral Bayesian Nash equilibria of such games. Several attempts at explaining this Overbidding Puzzle focus on the bidders’ psychology and suggest they may have parametrized utility functions that differ from the risk-neutral payoff. However, analytical equilibria of the resulting modified games are generally not available. Consequently, it has been difficult to identify the specific parameters and assess the merits of these proposed modifications in explaining empirical observations. With recent advances in equilibrium learning, it has become tractable to compute approximations of Bayesian Nash equilibria. Building on these advances and Bayesian optimization, we propose a novel regression framework to infer unobserved parameters of Bayesian games from behavioral data. We apply our method to two data sets of human bidding behavior in all-pay auctions. For the first time, this makes it possible to directly compare the goodness-of-fit of several proposed qualitative explanations of overbidding.
Improving Generalization with Cross-State Behavior Matching in Deep Reinforcement Learning

Representation learning on visualized input is an essential yet challenging task for deep reinforcement learning (RL). To help the RL agent learn more general and discriminative representation among various states, we present cross-state self-constraint (CSSC). This novel technique regularizes representation learning by comparing state embedding similarities across different state-action pairs. We test our proposed method on the OpenAI Procgen benchmark with Rainbow and PPO and demonstrate significant improvement across most Procgen environments
Behavior Exploration and Team Balancing for Heterogeneous Multiagent Coordination

Diversity in behaviors is instrumental for robust team performance in many multiagent tasks which require agents to coordinate. Unfortunately, exhaustive search through the agents’ behavior spaces is often intractable. This paper introduces Behavior Exploration for Heterogeneous Teams (BEHT), a multi-level learning framework that enables agents to progressively explore regions of the behavior space that promote team coordination on diverse goals. By combining diversity search to maximize agent-specific rewards and evolutionary optimization to maximize the team-based fitness, our method effectively filters regions of the behavior space that are conducive to agent coordination. We demonstrate the diverse behaviors and synergies that are method allows agents to learn on a multiagent exploration problem.
A Path-following Polynomial Equations Systems Approach for Computing Nash Equilibria

This paper presents a path-following combinatorial framework based on systems of polynomial equations to compute a mixed Nash equilibrium in 𝑁 -person games. We provide the first detailed implementable description of Wilson’s path-following method, extending Lemke-Howson’s algorithm to N-person games and handling degenerate games. Our approach resembles, in some respects, support enumeration methods. We thus compare both approaches, theoretically and experimentally. Then, we show that the path-following approach allows to deal with a large family of succinctly expressed games: hypergraphical games, graphical games and poly-matrix games. The described algorithms have been implemented in Python, making use of Sagemath libraries to solve systems of polynomial equations, allowing an experimental comparison of the different combinatorial approaches on a large variety of games.
Residual Entropy-based Graph Generative Algorithms

Classification and clustering are crucial tasks that recognize the identities and the communities of nodes in a graph. Several methods have been proposed to reduce the accuracy of node classification and clustering through graph neural networks (GNN). Existing defense methods usually modify the model architecture and adopt countermeasure training to enhance the robustness of the node classification and clustering. However, these defense methods are model-oriented and not robust. To alleviate the problem, this paper first proposes a robust node classification metric based on residual entropy. More concretely, we prove that maximizing the residual entropy helps to improve the robustness of the classification accuracy. We them propose two graph generative algorithms to resist against two kinds of GNN-based attacks, the untargeted and the targeted attacks . Finally, experimental analysis show that the proposed algorithms outperform the existing defense works under five classic datasets. 1
Empathetic Reinforcement Learning Agents

With the increased interaction between artificial agents and humans, the need to have agents who can respond to their human counterparts appropriately will be crucial for the deployment of trustworthy systems. A key behaviour to permit this, one which humans and other living beings exhibit naturally, is empathy. In my research I explore the potential for agents to behave in ways that may be considered empathetic. Empathy is a two stage process involving the identification of the feelings or goals of the other, and having that same feeling be evoked in oneself. I began my work towards this objective by initially designing an agent who exhibits sympathy - the ability to identify the goals of another. Empathy is slightly more complex as it involves a process of projecting the state of the other back onto oneself and observing one’s own response. In my research I hope to draw inspiration from this and evoke empathy through a process of mapping the other’s goals back to oneself. By drawing upon empathetic responses, the hope is that this will lead to a faster and deeper understanding of the other.
Properties of Reputation Lag Attack Strategies

The trustors in a reputation system share trust-relevant information about trustees; their reputation. The presence of lag in the sharing mechanism can be exploited by malicious trustees to perform otherwise impermissible additional bad actions. This is the reputation lag attack. In this paper, we use simulations to explore properties of the reputation lag attack and strategies which improve the attacker’s success. We demonstrate the following key findings: Attackers in lagged systems clearly outperform attackers in lag-free systems. The attacker’s success is proportional to the rate at which they can interact with victims, plateauing at a maximum. Attackers who wait for their good reputation to disseminate outperform those who do not. Attackers who perform only good actions, wait for dissemination and then perform only bad actions outperform attackers who do not follow this ordering. This implies reputation-lag attacks are effective exit strategies. In typical social networks, smart attackers may cheat users with a low centrality, but in a homogeneous network, this strategy is ineffectual. Our findings help allow developers of reputation systems defend against a class of attacks that has not yet received a great deal of attention.
Multi-Objective Reinforcement Learning with Non-Linear Scalarization

Multi-Objective Reinforcement Learning (MORL) setup naturally arises in many places where an agent optimizes multiple objectives. We consider the problem of MORL where multiple objectives are combined using a non-linear scalarization. We combine the vector objectives with a concave scalarization function and maximize this scalar objective. To work with the non-linear scalarization, in this paper, we propose a solution using steady-state occupancy measures and long-term average rewards. We show that when the scalarization function is element-wise increasing, the optimal policy for the scalarization is also Pareto optimal. To maximize the scalarized objective, we propose a model-based posterior sampling algorithm. Using a novel Bellman error analysis for infinite horizon MDPs based proof, we show that the proposed algorithm obtains a regret bound of ˜ O ( LKDS (cid:112) A / T ) for K objectives, and L -Lipschitz continous scalarization function for MDP with S states, A actions, and diameter D . Additionally, we propose policy-gradient and actor-critic algorithms for MORL. For the policy gradient actor, we obtain the gradient using chain rule, and we learn different critics for each of the K objectives. Finally, we implement our algorithms on multiple environments including deep-sea treasure, and network scheduling setups to demonstrate that the proposed algorithms can optimize non-linear scalarization of multiple objectives.
Role of Phytochemicals in Skin Photoprotection via Regulation of Nrf2

Ethnopharmacological studies have become increasingly valuable in the development of botanical products and their bioactive phytochemicals as novel and effective preventive and therapeutic strategies for various diseases including skin photoaging and photodamage-related skin problems including abnormal pigmentation and inflammation. Exploring the roles of phytochemicals in mitigating ultraviolet radiation (UVR)-induced skin damage is thus of importance to offer insights into medicinal and ethnopharmacological potential for development of novel and effective photoprotective agents. UVR plays a role in the skin premature aging (or photoaging) or impaired skin integrity and function through triggering various biological responses of skin cells including apoptosis, oxidative stress, DNA damage and inflammation. In addition, melanin produced by epidermal melanocytes play a protective role against UVR-induced skin damage and therefore hyperpigmentation mediated by UV irradiation could reflect a sign of defensive response of the skin to stress. However, alteration in melanin synthesis may be implicated in skin damage, particularly in individuals with fair skin. Oxidative stress induced by UVR contributes to the process of skin aging and inflammation through the activation of related signaling pathways such as the mitogen-activated protein kinase (MAPK)/activator protein-1 (AP-1), the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt), the nuclear factor kappa B (NF-κB) and the signal transducer and activator of transcription (STAT) in epidermal keratinocytes and dermal fibroblasts. ROS formation induced by UVR also plays a role in regulation of melanogenesis in melanocytes via modulating MAPK, PI3K/Akt and the melanocortin 1 receptor (MC1R)-microphthalmia-associated transcription factor (MITF) signaling cascades. Additionally, nuclear factor erythroid 2-related factor 2 (Nrf2)-regulated antioxidant defenses can affect the major signaling pathways involved in regulation of photoaging, inflammation associated with skin barrier dysfunction and melanogenesis. This review thus highlights the roles of phytochemicals potentially acting as Nrf2 inducers in improving photoaging, inflammation and hyperpigmentation via regulation of cellular homeostasis involved in skin integrity and function. Taken together, understanding the role of phytochemicals targeting Nrf2 in photoprotection could provide an insight into potential development of natural products as a promising strategy to delay skin photoaging and improve skin conditions.
Frequency- and time-domain analysis of a novel UWB reconfigurable microstrip slot antenna with switchable notched bands

A novel reconfigurable microstrip slot antenna with capability of switchable frequency notched bands for ultra-wide-band (UWB) applications is presented. The feed line is tapered to provide UWB matching condition. Two parasitic elements are attached to two sides of patch having a square slot for improvement of frequency band response. An inverted Γ-shaped parasitic element is placed inside the slot in patch to produce a notch at wireless local area network (WLAN) frequency band (5.1–5.7 GHz). A rectangular split ring resonator is placed on the back side of microstrip slot antenna to produce a notch at downlink X-band for satellite communication systems (7.2–7.8 GHz). Reconfigurability of proposed antenna is obtained by placing PIN diodes at appropriate places to achieve four different operations, namely UWB coverage, UWB coverage with single band notch at WLAN, UWB coverage with single band notch at downlink X-band and UWB coverage with double band notch at WLAN and downlink X-band. The first case achieves fractional bandwidth of 123% (3.1–13 GHz). Antenna performance is studied in frequency and time domains. The bias circuits of PIN diodes are placed on antenna structure and are considered in simulations. A prototype model of proposed antenna is fabricated and measured to demonstrate its desirable features.
Is It Time to Genotype Beyond HPV16 and HPV18 for Cervical Cancer Screening?

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore, Health Economics and Outcomes Research, Becton, Dickinson and Company, Singapore, Singapore, Strategic Access, Becton, Dickinson and Company, Singapore, Singapore, Innovation and Research Department, Salud Digna A.C. , Culiacán, Mexico, Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore