Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive numerical solution of Kadanoff-Baym equations

A time-stepping scheme with adaptivity in both the step size and the integration order is presented in the context of non-equilibrium dynamics described via Kadanoff-Baym equations. The accuracy and effectiveness of the algorithm are analysed by obtaining numerical solutions of exactly solvable models. We find a significant reduction in the number of time-steps compared to fixed-step methods. Due to the at least quadratic scaling of Kadanoff-Baym equations, reducing the amount of steps can dramatically increase the accessible integration time, opening the door for the study of long-time dynamics in interacting systems. A selection of illustrative examples is provided, among them interacting and open quantum systems as well as classical stochastic processes. An open-source implementation of our algorithm in the scientific-computing language Julia is made available.
Neural Algorithmic Reasoners are Implicit Planners

Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit planners inspired by value iteration, an algorithm that is guaranteed to yield perfect policies in fully-specified tabular environments. We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer"local neighbourhoods"of states to run value iteration over -- for which we discover an algorithmic bottleneck effect. This effect is caused by explicitly running the planning algorithm based on scalar predictions in every state, which can be harmful to data efficiency if such scalars are improperly predicted. We propose eXecuted Latent Value Iteration Networks (XLVINs), which alleviate the above limitations. Our method performs all planning computations in a high-dimensional latent space, breaking the algorithmic bottleneck. It maintains alignment with value iteration by carefully leveraging neural graph-algorithmic reasoning and contrastive self-supervised learning. Across eight low-data settings -- including classical control, navigation and Atari -- XLVINs provide significant improvements to data efficiency against value iteration-based implicit planners, as well as relevant model-free baselines. Lastly, we empirically verify that XLVINs can closely align with value iteration.
Controllable Recommenders using Deep Generative Models and Disentanglement

In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. While deep models have shown improved performance for collaborative filtering, they are generally not amenable to fine grained control by a user, leading to the development of methods like deep language critiquing. We propose an alternate view, where instead of keyphrase based critiques, a user is provided 'knobs' in a disentangled latent space, with each knob corresponding to an item aspect. Disentanglement here refers to a latent space where generative factors (here, a preference towards an item category like genre) are captured independently in their respective dimensions, thereby enabling predictable manipulations, otherwise not possible in an entangled space. We propose using a (semi-)supervised disentanglement objective for this purpose, as well as multiple metrics to evaluate the controllability and the degree of personalization of controlled recommendations. We show that by updating the disentangled latent space based on user feedback, and by exploiting the generative nature of the recommender, controlled and personalized recommendations can be produced. Through experiments on two widely used collaborative filtering datasets, we demonstrate that a controllable recommender can be trained with a slight reduction in recommender performance, provided enough supervision is provided. The recommendations produced by these models appear to both conform to a user's current preference and remain personalized.
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance based on a variant of Shapley values. Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social interaction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality
Chemical Composition of Cleome amblyocarpa Barr. & Murb. Essential Oils Under Different Irrigation Levels in Sandy Soils with Antioxidant Activity

Abstract The essential oil of the whole plant of Cleome amblyocarpa growing in sandy soils of the United Arab Emirates (UAE) open field pot plant experiment was extracted by hydrodistillation. The influences of three soil matric potentials were created (pF levels: 1.7, 1.9, 2.1) by applying three irrigation schedules and measured by Stevens pF Sensors. The oil yield ranged from 0.00715% (at pF level: 1.9) to 0.02669% (at pF level: 2.1). The qualitative analysis of the extracted essential oils was conducted for the first time by a combination of gas chromatograph (GC) and gas chromatograph/mass spectrometer (GC/MS). Forty six (46) volatiles have been identified, showing a significant change in the chemical profile of the extracted essential oils under different pF levels. At 1.7 pF level, 27 volatile components were identified and the oil contained isobornyl formate (43.9%), tetrahydro-linalool acetate (7.45%), neo-menthyl acetate (5.8%), 1-dodecene (4.82%), and γ-elemene (4.8%). At 1.9 pF level, 20 volatiles were identified and the components were trans-chrysanthenyl acetate (38.6%), tetrahydro-linalool acetate (6.32%), verbenone (5.86%) and γ-elemene (5.66%). While at 2.1 pF level, 17 volatiles were identified and the major components were (e)-ocimenone (39.66%), γ-elemene (8.3%), methyl eugenol (7.41%), cis-chrysanthenyl acetate (7.12%) and tetrahydro-linalool acetate (7.1%). Scanning electron microscope (SEM) study showed variations in glandular trichomes of the leaf epidermis at pF 2.1. Results of DPPH, FRAP and ABTS assays (in vitro) showed that C. amblyocarpa EO (isolated from seeds and whole plant) is a good resource of natural bioactive antioxidants.
Gradual Federated Learning With Simulated Annealing

Federated averaging (FedAvg) is a popular federated learning (FL) technique that updates the global model by averaging local models and then transmits the updated global model to devices for their local model update. One main limitation of FedAvg is that the average-based global model is not necessarily better than local models in the early stage of the training process so that FedAvg might diverge in realistic scenarios, especially when the data is non-identically distributed across devices and the number of data samples varies significantly from device to device. In this paper, we propose a new FL technique based on simulated annealing. The key idea of the proposed technique, henceforth referred to as simulated annealing-based FL (SAFL), is to allow a device to choose its local model when the global model is immature. Specifically, by exploiting the simulated annealing strategy, we make each device choose its local model with high probability in early iterations when the global model is immature. From extensive numerical experiments using various benchmark datasets, we demonstrate that SAFL outperforms the conventional FedAvg technique in terms of the convergence speed and the classification accuracy.
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

Current Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module, where the retriever selects potentially relevant passages from open-source documents for a given question, and the reader produces an answer based on the retrieved passages. The recently proposed Fusion-in-Decoder (FiD) framework is a representative example, which is built on top of a dense passage retriever and a generative reader, achieving the state-of-the-art performance. In this paper we further improve the FiD approach by introducing a knowledge-enhanced version, namely KG-FiD. Our new model uses a knowledge graph to establish the structural relationship among the retrieved passages, and a graph neural network (GNN) to re-rank the passages and select only a top few for further processing. Our experiments on common ODQA benchmark datasets (Natural Questions and TriviaQA) demonstrate that KG-FiD can achieve comparable or better performance in answer prediction than FiD, with less than 40% of the computation cost.
Spoki: Unveiling a New Wave of Scanners through a Reactive Network Telescope

Large-scale Internet scans are a common method to identify victims of a specific attack. Stateless scanning like in ZMap has been established as an efficient approach to probing at Internet scale. Stateless scans, however, need a second phase to perform the attack, which remains invisible to network telescopes that only capture the first incoming packet and is not observed as a related event by honeypots. In this work, we examine Internet-wide scan traffic through Spoki, a reactive network telescope operating in real-time that we design and implement. Spoki responds to asynchronous TCP SYN packets and engages in TCP handshakes initiated in the second phase of two-phase scans. Because it is extremely lightweight it scales to large prefixes where it has the unique opportunity to record the first data sequence submitted within the TCP handshake ACK. We analyze two-phase scanners during a three months period using globally deployed Spoki reactive telescopes as well as flow data sets from IXPs and ISPs. We find that a predominant fraction of TCP SYNs on the Internet has irregular characteristics. Our findings also provide a clear signature of today's scans as: (i) highly targeted, (ii) scanning activities notably vary between regional vantage points, and (iii) a significant share originates from malicious sources.
Clustering Human Trust Dynamics for Customized Real-Time Prediction

Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively estimate human trust in real-time. Although multiple trust models exist, these models have limited predictive performance partly due to individual differences in trust dynamics. A personalized model for each person can address this issue, but it requires a significant amount of data for each user. We present a methodology to develop customized model by clustering humans based on their trust dynamics. The clustering-based method addresses the individual differences in trust dynamics while requiring significantly less data than personalized model. We show that our clustering-based customized models not only outperform the general model based on entire population, but also outperform simple demographic factor-based customized models. Specifically, we propose that two models based on “con-fident” and “skeptical” group of participants, respectively, can represent the trust behavior of the population. The “confident” participants, as compared to the “skeptical” participants, have higher initial trust levels, lose trust slower when they encounter low reliability operations, and have higher trust levels during trust-repair after the low reliability operations. In summary, clustering-based customized models improve trust prediction performance for further trust calibration considerations.
A Multi-Resolution Front-End for End-to-End Speech Anti-Spoofing

The choice of an optimal time-frequency resolution is usually a difficult but important step in tasks involving speech signal classification, e.g., speech anti-spoofing. The variations of the performance with different choices of timefrequency resolutions can be as large as those with different model architectures, which makes it difficult to judge what the improvement actually comes from when a new network architecture is invented and introduced as the classifier. In this paper, we propose a multi-resolution front-end for feature extraction in an end-to-end classification framework. Optimal weighted combinations of multiple time-frequency resolutions will be learned automatically given the objective of a classification task. Features extracted with different time-frequency resolutions are weighted and concatenated as inputs to the successive networks, where the weights are predicted by a learnable neural network inspired by the weighting block in squeeze-and-excitation networks (SENet). Furthermore, the refinement of the chosen timefrequency resolutions is investigated by pruning the ones with relatively low importance, which reduces the complexity and size of the model. The proposed method is evaluated on the tasks of speech anti-spoofing in ASVSpoof 2019 and its superiority has been justified by comparing with similar baselines.
Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning

Current methods in continual learning (CL) tend to focus on alleviating catastrophic forgetting of previous tasks. This hinders balancing other CL desiderata such as forward transfer, and memory and computation efﬁciency. CL desiderata become much more competing when a model operates on class-Incremental Learning (class-IL) without accessing past data since it is prone to ambiguities between old and new classes. In this paper, we present Knowledge-Aware coNtinual learner (KAN) that attempts to study the stability-plasticity dilemma to balance CL desiderata in class-IL. In particular, KAN is a new task-speciﬁc components method based on dynamic sparse training that introduces reusability and selective transfer of past knowledge in this class of meth-ods. KAN selectively reuses relevant knowledge while addressing class ambiguity, preserves old knowledge, and utilizes the model capacity efﬁ-ciently. Experiments show the effectiveness of KAN in providing models with multiple CL desirable properties, outperforming state-of-the-art methods on various challenging benchmarks.
Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects

Articulated objects exist widely in the real world. However, previous 3D generative methods for unsupervised part decomposition are unsuitable for such objects, because they assume a spatially fixed part location, resulting in inconsistent part parsing. In this paper, we propose PPD (unsupervised Pose-aware Part Decomposition) to address a novel setting that explicitly targets man-made articulated objects with mechanical joints, considering the part poses. We show that category-common prior learning for both part shapes and poses facilitates the unsupervised learning of (1) part decomposition with non-primitive-based implicit representation, and (2) part pose as joint parameters under single-frame shape supervision. We evaluate our method on synthetic and real datasets, and we show that it outperforms previous works in consistent part parsing of the articulated objects based on comparable part pose estimation performance to the supervised baseline.
Widen The Backdoor To Let More Attackers In

As collaborative learning and the outsourcing of data collection become more common, malicious actors (or agents) which attempt to manipulate the learning process face an additional obstacle as they compete with each other. In backdoor attacks, where an adversary attempts to poison a model by introducing malicious samples into the training data, adversaries have to consider that the presence of additional backdoor attackers may hamper the success of their own backdoor. In this paper, we investigate the scenario of a multi-agent backdoor attack, where multiple non-colluding attackers craft and insert triggered samples in a shared dataset which is used by a model (a defender) to learn a task. We discover a clear backfiring phenomenon: increasing the number of attackers shrinks each attacker's attack success rate (ASR). We then exploit this phenomenon to minimize the collective ASR of attackers and maximize defender's robustness accuracy by (i) artificially augmenting the number of attackers, and (ii) indexing to remove the attacker's sub-dataset from the model for inference, hence proposing 2 defenses.
Human Factors Considerations in Satellite Operation's Human-Computer Interaction Technologies: A Review of Current Applications and Theory

Satellite operations are a subset of remote operations that draw similarities with remotely piloted aircraft (RPA) and uncrewed aerial vehicle (UAV) operations. Increased research into boredom, complacency, habituation, and vigilance as they relate to satellite operations is required due to a lack of prevalence in the literature. Circadian rhythms, crew resource management, and shift work dynamics may exacerbate complacency-driven automation bias and social loafing errors in satellite operations. This overview of theory and applications aims to specifically focus on satellite operations literature within human factors research to identify areas requiring an expansion of knowledge. The human-in-the-loop commonality enables human factors lessons to be passed to satellite operations from unrelated sectors to mitigate catastrophic human error potentially. As such, this literature review details the need for increased research in satellite operations human factors.
Unsupervised Neural Machine Translation with Generative Language Models Only

We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models. Our method consists of three steps: few-shot amplification, distillation, and backtranslation. We first use the zero-shot translation ability of large pre-trained language models to generate translations for a small set of unlabeled sentences. We then amplify these zero-shot translations by using them as few-shot demonstrations for sampling a larger synthetic dataset. This dataset is distilled by discarding the few-shot demonstrations and then fine-tuning. During backtranslation, we repeatedly generate translations for a set of inputs and then fine-tune a single language model on both directions of the translation task at once, ensuring cycle-consistency by swapping the roles of gold monotext and generated translations when fine-tuning. By using our method to leverage GPT-3's zero-shot translation capability, we achieve a new state-of-the-art in unsupervised translation on the WMT14 English-French benchmark, attaining a BLEU score of 42.1.
Frustrated Ising Model on D-wave Quantum Annealing Machine

We study the frustrated Ising model on the two-dimensional L × L square lattice with ferromagnetic (FM) nearest-neighbor and antiferromagnetic diagonal-neighbor interactions using the D-wave quantum annealing machine (D-QAM) with 5000+ qubits composed on structure of the Pegasus graph. As the former Monte Carlo and mean ﬁeld results, we ﬁnd the FM to stripe order phase transition, through observations of the magnetization M , energy, magnetic susceptibility and structure factor. We also analyze probability which occurs any M at a given interaction for many quantum annealing shots to estimate the shape of objective function f . The only one value of M with speciﬁc phase is observed in the regions far from phase transition for many quantum annealing shots, while several values of M with diﬀerent possibilities are appeared in the regimes of phase transition. We guess that f in the regimes of phase transition retains the multi-modal structure with several local minimums, due to the strong degeneracies caused by frustrations. Finally, we discuss fail of the quantum annealing simulations, through analysis of the number of the chains, deﬁned as the same variable with N -qubits, as a function of L .
Unsupervised Source Separation via Bayesian Inference in the Latent Domain

State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for training these models without any direct supervision are typically high-demanding in terms of memory and time requirements, and remain impractical to be used at inference time. We aim to tackle these limitations by proposing a simple yet effective unsupervised separation algorithm, which operates directly on a latent representation of time-domain signals. Our algorithm relies on deep Bayesian priors in the form of pre-trained autoregressive networks to model the probability distributions of each source. We leverage the low cardinality of the discrete latent space, trained with a novel loss term imposing a precise arithmetic structure on it, to perform exact Bayesian inference without relying on an approximation strategy. We validate our approach on the Slakh dataset arXiv:1909.08494, demonstrating results in line with state of the art supervised approaches while requiring fewer resources with respect to other unsupervised methods.
The phytochemical and anti-microbial potential of the extracts from the fruit pulp of Landlphia owariensis

The phytochemical composition and antimicrobial potential of extracts of fruit pulp of Landolphia owariensis was investigated. L. owariensis mesocarp flour was obtained by oven drying at 50 0 C and pulverized while microwave assisted extraction was used to obtain hexane, chloroform, ethyl acetate and methanol extracts, which were concentrated in vacuo using microwave and thereafter air dried at ambient temperature. Antimicrobial sensitivity tests on fourteen (14) human pathogenic microorganisms were performed using diffusion method while minimum inhibitory concentration (MIC) and minimum bacterial/fungal concentration (MBC/MFC) tests were performed using dilution method. The oven dried L. owariensis fruit pulp flour contained 12.45±0.03 mg/100 g tannins and 6.39±2.78 mg/100 g total flavonoids but low levels of anthraquinones, alkaloids, cyanogenic glycosides, saponins, steroids and terpenes. The phytosterols consisted of cholesterol, cholestanol, campesterol, stigmasterol, beta sitosterol and tocopherol with stigmasterol being the most abundant (1.06±0.21 mg/100 g). The flavonoids constituents included orientin, isovitexin and keampferol. Microbiological analysis of hexane, chloroform, ethyl acetate and methanol extracts of L. owariensis fruit pulp showed antimicrobial activities against S. aureus , S. pyogenes , S. typhi , S. dysenteriae , K.pneumoniea , C. albicans , C. krusei , C. stellatoidea and M. rubrum . Chloroform extract exhibited the highest zones of inhibition, followed by hexane and ethyl acetate extracts while methanol extract had least diameter of zones of inhibition. Also ethyl acetate extract was more potent (2.5-5 mg/mL), followed by chloroform and methanol extracts while hexane extract had the least potency (5-10 mg/mL) level. Similarly, ethyl acetate extract was most effective (5-10 mg/mL) in inhibiting bacterial/fungal growth. L. owariensis fruit pulp flour and extracts contained important secondary metabolites that may account for its antimicrobial activities. Keywords: Landolphia owariensis , phytochemicals, anti-microbial activities, flavonoids, phytosterols