Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Predicting the trajectories of surrounding agents is an essential ability for autonomous vehicles navigating through complex traffic scenes. The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure. Due to the high variability in scene structure and agent configurations, prior work has employed the attention mechanism, applied separately to the scene and agent configuration to learn the most salient parts of both cues. However, the two cues are tightly linked. The agent configuration can inform what part of the scene is most relevant to prediction. The static scene in turn can help determine the relative influence of agents on each other's motion. Moreover, the distribution of future trajectories is multimodal, with modes corresponding to the agent's intent. The agent's intent also informs what part of the scene and agent configuration is relevant to prediction. We thus propose a novel approach applying multi-head attention by considering a joint representation of the static scene and surrounding agents. We use each attention head to generate a distinct future trajectory to address multimodality of future trajectories. Our model achieves state of the art results on the nuScenes prediction benchmark and generates diverse future trajectories compliant with scene structure and agent configuration.
Review of the State-of-the-art on Bio-signal-based Brain-controlled Vehicles

Brain-controlled vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on the brain controlling vehicles, especially considering terrestrial BCV (e.g., mobile car, car simulators, real car, graphical and gaming cars) and aerial BCV, also named BCAV (e.g., real quadcopter, drone, fixed wings, graphical helicopter and aircraft) controlled using bio-signals such as electroencephalogram (EEG), electrooculogram and electromyogram. For instance, EEG-based algorithms detect patterns from motor imaginary cortex area of the brain for intention detection, patterns like event related desynchronization/event related synchronization, state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization and chaotic tug of war optimization optimization. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with the statistical analysis. The present work provides an extensive literature review of the key findings of previous ten years, indicating the future perspectives in the field.
A search for γ-ray emission from a sample of local Universe low-frequency selected radio galaxies

Radio galaxies are uncommon γ-ray emitters, and only low-redshift radio galaxies are detected with the Fermi Large Area Telescope (Fermi-LAT). However, they offer potential insights into the emission mechanisms of active galaxies, particularly as the alignment of their jets with respect to the Earth means that, unlike blazars, their emission is not necessarily jet dominated. We use the Fermi-LAT data to perform an unbiased survey of 78 radio galaxies from the Bologna complete sample in order to search for new γ-ray-emitting radio galaxies. We observe statistically significant γ-ray emission from four of the six known Fermi-LAT-detected radio galaxies included in this sample, and find some evidence for γ-ray emission spatially coincident with four previously undetected radio galaxies. As a large parameter space is searched, we calculate a probability distribution to compute the look-elsewhere effect. We find that these four spatially coincident sub-threshold γ-ray excesses are most likely a chance association, and are unlikely to be emission from the radio galaxies. Upper limits on flux are calculated for the radio galaxies from which no γ-ray emission is observed.
Book Reviews: Reasoning and Discourse Processes

This definition of synonymy provides us with a tool to relate the words in the vocabulary of a language to one another, and, hence, a tool with th,e aid of which we can define all the word-uses of each word-sign simply and in a form allowing comparisons of definitions. It can be objected that if this approach is applied to special vocabulary (technical and scientific terminology) in which synonymy is rare, the definitions of word-uses will often remain empty. The author analyzes various potential classifications of sets of synonyms (sets of rows) enabled by the synonymy definition, as well as the possibilities of their groupings. She finds a few ways of row grouping resulting in conceptual groupings similar to those found in thesauri. She made a computer experiment whose aim was to find out whether the suggested procedure of grouping is practicable for the natural language vocabulary. She selected 21 wordsigns and, on the basis of information about them contained in the Oxford English Dictionary, assigned 500 rows to them (given in her Appendix 2). With aid of the computer, she tried to establish groupings similar to those in thesauri. The results have been satisfactory in part only. After modifying classification criteria, the author intended to carry out a new experiment involving about 2,000 rows. The results of thai: experiment were not mentioned in the chapter "Twenty years later". Sparck Jones is looking for an answer to the question as to why we expect to find synonyms in natural languages. She arrives at the conclusion that synonymy is not a mere redundance and that it exists because, in the extra-linguistic world, we encounter situations that are unique, but, at the same time, similar to each other in certain aspects. Synonymy reflec'~s this fact; otherwise language would be an inadequate representation of the extra-linguistic world. She gives four models of the way linguistic symbols are set up and she claims that her Model 4 is the one that represents natural language: Model 4: a. A word-use may have more than one sign; b. Two or more word-uses may have the same sign, where these word-uses are similar. (p. 135) It remains unclear why also homonymy, which is included in her Model 2 and which does constitute a language relation, is not dealt with in Model 4, and why the word homonymy is never used in the book. The book exemplifies an excellent way the efforts made to solve practical problems in computational linguistics bring new and promising knowledge in a field of linguistic theory. The rendering of the subject has a solid, logical structure; it is clear and systematic. The text is not burdened with the artificial linguistic terminology that flooded the linguistic publications of the sixties. It can be regretted that the book was not published at the time of its origin. Even for the present time, however, it yields a number of suggestions for linguistic research. NOTE
Bayesian clustering of high-dimensional data

In many applications, it is of interest to cluster subjects based on very high-dimensional data. Although Bayesian discrete mixture models are often successful at model-based clustering, we demonstrate pitfalls in high-dimensional settings. The first key problem is a tendency for posterior sampling algorithms based on Markov chain Monte Carlo to produce a very large number of clusters that slowly decreases as sampling proceeds, indicating serious mixing problems. The second key problem is that the true posterior also has aberrant behavior but potentially in the opposite direction. In particular, we show that, for diverging dimension and fixed sample size, the true posterior either assigns each observation to a different cluster or all observations to the same cluster, depending on the kernels and prior specification. We propose a general strategy for solving these problems by basing clustering on a discrete mixture model for a low-dimensional latent variable. We refer to this class of methods as LAtent Mixtures for Bayesian (Lamb) clustering. Theoretical support is provided, and we illustrate substantial gains relative to clustering on the observed data level in simulation studies. The methods are motivated by an application to clustering of single cell RNAseq data, with the clusters corresponding to different cell types.
Approaches to Ethics Management in the Public Sector : A Proposed Extension of the Compliance-Integrity Continuum

The compliance-integrity continuum is the dominant framework for conceptualizing approaches to ethics management. This article proposes extending it in the form of a fourfold typology inspired by grid-group theory. An application to gift policies shows how the typology can support managers in their creative search for an appropriate approach. It also shows how the typology helps to avoid the deterministic thinking that is almost inherent in the use of a continuum. The article concludes with a recommendation as to how the four types should be combined in practice.
Low density growth of cells derived from Nicotiana and Petunia protoplasts: Influence of the source of protoplasts and comparison of the growth‐promoting activity of various auxins

Nicotiana tabacum (cv. Xanthi), N. plumbaginifolia Viviani, N. sylvestris Speg. and Comes and Petunia axillaris× (P. axillaris × P. hybrida) (cv. Mitchell) mesophyll protoplast-derived cells were able to grow at low densities in chemically-defined media. Protoplasts of different origins (mesophyll, epidermis, pith, suspension culture) also gave rise to protoplast-derived cells that were able to grow at low densities. When auxin requirements at low densities were compared for different sources of auxins, IAA was found to be efficient in the same range of concentrations as NAA. This was unexpected since tobacco mesophyll protoplasts cannot be induced to divide when plated at high density in the presence of IAA. Optimal 2,4-D concentrations for low density growth were higher and clearly pH-dependent. On the contrary, picloram induced low density growth over a wide range of concentrations suggesting a distinct mechanism of action. These results confirm and extend previous observation on the tobacco mesophyll protoplast and show that the low-density growth technique has a potential use for the study of the action of phytohormones.
Machine Translation with a Stochastic Grammatical Channel

We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine translation. As with the pure statistical translation model described by Wu (1996) (in which a bracketing transduction grammar models the channel), alternative hypotheses compete probabilistically, exhaustive search of the translation hypothesis space can be performed in polynomial time, and robustness heuristics arise naturally from a language-independent inversiontransduction model. However, unlike pure statistical translation models, the generated output string is guaranteed to conform to a given target grammar. The model employs only (1) a translation lexicon, (2) a context-free grammar for the target language, and (3) a bigram language model. The fact that no explicit bilingual translation roles are used makes the model easily portable to a variety of source languages. Initial experiments show that it also achieves significant speed gains over our earlier model.
Growth Response of Picea mariana Seedlings to Competition for Radiation

The growth response of 4-year-old black spruce seedlings (Picea mariana (Mill.) B.S.P.) to reduced radiation availability (DIFN) caused by herbaceous and woody competitors was investigated on a clay and on a loam site on a research plot in Ontario, Canada. Variability in black spruce growth was less explicable by reduced radiation availability caused by woody competitors than growth variability caused by herbaceous competitors. On the clay site, competition from fireweed (Epilobium angustifolium L.), Canada bluejoint grass (Calamagrostis canadensis (Michx.) Nutt. ) and red raspberry (Rubus idaeus L. var. strigosus (Michx.) Maxim. ) decreased black spruce diameter by more than 40% compared to seedlings without competition. Canada bluejoint grass and fireweed also distinctly reduced black spruce diameter performance on loam (>30%). These results are discussed in relation to the need of vegetation control.
Large molecules as anti‐adhesive compounds against pathogens

Anti‐adhesive compounds are potential prophylactic tools in alternative treatment regimes against bacterial infection, as bacterial adhesion is commonly mediated by carbohydrate‐protein interactions between surface adhesions of microorganisms and the host cell. The use of exogenous polyvalent, high‐molecular carbohydrates and tannin‐like plant‐derived compounds should antagonize the adhesive interaction. A range of carbohydrates and carbohydrate‐ and proanthocyanidin‐enriched plant extracts were screened for potential anti‐adhesive effects against Helicobacter pylori, Campylobacter jejuni, Porphyromonas gingivalis and Candida albicans in different in‐situ assays on primary tissue. The adhesion of H. pylori on human stomach tissue was effectively blocked by glucuronic acid‐enriched polysaccharides from immature okra fruits (Abelmoschus esculentus). These compounds also had strong in‐vitro effects against C. jejuni (inhibition up to 80%), but were ineffective in an in‐vivo study in infected chicken broilers due to metabolism in the gastrointestinal system. Polysaccharides from Glycyrrhizia glabra, also enriched with glucuronic acid, showed strong anti‐adhesive properties against H. pylori and P. gingivalis (inhibition 60–70%). Pelargonium sidoides extract, containing mainly polymeric proanthocyanidins, was effective against H. pylori in a dose‐dependent manner. Due to the multifunctional adhesive strategy of C. albicans, no effective compounds were detected against this yeast. Structure‐activity relationships are presented and the potential in‐vivo use of carbohydrate‐based anti‐adhesives is discussed.
Isolation and Characterization of NAD(P)-Dependent L-Sorbosone Dehydrogenase from Gluconobacter melanogenus UV10

NAD(P)-Dependent l-sorbosone dehydrogenase has been purified from the cytosol fraction of Gluconobacter melanogenus UV10. The enzyme was purified about 30-fold with an overall yield of 19% by column chromatographies on DEAE-Sepharose CL-6B, DEAE-Sephadex A-50, and Blue Sepharose CL-6B. The enzyme required NAD or NADP as a cofactor, and showed broad substrate specificity on various aldehyde compounds. Among them, l-sorbosone was the best substrate for the enzyme. In this respect, the enzyme is a kind of aldehyde dehydrogenase.
When pigeons in motion lose sight of their food: behaviour on visible displacement tasks revisited

In traditional visible displacement tasks, animals view an object as it is moved out of their sight, but either the object moves behind an occluder or an occluder moves in front of it. Here we present a more ecologically realistic visible displacement task for pigeons (Columba livia (Gmelin, 1789)) in which it was the animal that occluded the object, a food dish in this case, by virtue of its own motion. In a branched maze, pigeons had visual access to food, which they would lose from view as they moved through the maze. A within-subject design was used whereby the task was presented first in descending order of difficulty (i.e., decreasing memory load owing to the opening of (i) gaps and (ii) windows in the walls of the maze), and followed 10 months later by an ascending order. When the food was visible at all times through windows and gaps, pigeons would make turns in the maze that would bring them closer to the food (i.e., they chose the shortest route above chance levels). In general, they failed to d.
Differentiable Linear Bandit Algorithm

Upper Confidence Bound (UCB) is arguably the most commonly used method for linear multi-arm bandit problems. While conceptually and computationally simple, this method highly relies on the confidence bounds, failing to strike the optimal exploration-exploitation if these bounds are not properly set. In the literature, confidence bounds are typically derived from concentration inequalities based on assumptions on the reward distribution, e.g., sub-Gaussianity. The validity of these assumptions however is unknown in practice. In this work, we aim at learning the confidence bound in a data-driven fashion, making it adaptive to the actual problem structure. Specifically, noting that existing UCB-typed algorithms are not differentiable with respect to confidence bound, we first propose a novel differentiable linear bandit algorithm. Then, we introduce a gradient estimator, which allows the confidence bound to be learned via gradient ascent. Theoretically, we show that the proposed algorithm achieves a $\tilde{\mathcal{O}}(\hat{\beta}\sqrt{dT})$ upper bound of $T$-round regret, where $d$ is the dimension of arm features and $\hat{\beta}$ is the learned size of confidence bound. Empirical results show that $\hat{\beta}$ is significantly smaller than its theoretical upper bound and proposed algorithms outperforms baseline ones on both simulated and real-world datasets.
The why, how, and when of representations for complex systems

Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. In this work we offer basic, domain-agnostic language in order to advance towards a more cohesive vocabulary. We use this language to evaluate each step of the complex systems analysis pipeline, beginning with the system and data collected, then moving through different mathematical formalisms for encoding the observed data (i.e. graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each formalism. At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation. We discuss how dependencies may arise and how they may alter interpretation of results or the entirety of the analysis pipeline. We close with two real-world examples using coauthorship data and email communications data that illustrate how the system under study, the dependencies therein, the research question, and choice of mathematical representation influence the results. We hope this work can serve as an opportunity of reflection for experienced complexity scientists, as well as an introductory resource for new researchers.
Asymmetric Leaky Private Information Retrieval

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages from <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> non-colluding replicated databases without learning anything about the remaining <inline-formula> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>. We show that the optimal download cost of AL-PIR is upper-bounded as <inline-formula> <tex-math notation="LaTeX">$D^{*}(\epsilon,\delta)\leq 1+\frac {1}{N-1}-\frac {\delta e^{\epsilon }}{N^{K-1}-1}$ </tex-math></inline-formula>. Second, we obtain an information-theoretic lower bound on the download cost as <inline-formula> <tex-math notation="LaTeX">$D^{*}(\epsilon,\delta)\geq 1+\frac {1}{Ne^{\epsilon }-1}-\frac {\delta }{(Ne^{\epsilon })^{K-1}-1}$ </tex-math></inline-formula>. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when <inline-formula> <tex-math notation="LaTeX">$\epsilon =0$ </tex-math></inline-formula>, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of <inline-formula> <tex-math notation="LaTeX">$\frac {N-e^{-\epsilon }}{N-1}$ </tex-math></inline-formula> for any <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\delta >0$ </tex-math></inline-formula>.
Towards Lower Bit Multiplication for Convolutional Neural Network Training

Convolutional Neural Networks (CNNs) have been widely used in many fields. However, the training process costs much energy and time, in which the convolution operations consume the major part. In this paper, we propose a fixed-point training framework, in order to reduce the data bit-width for the convolution multiplications. Firstly, we propose two constrained group-wise scaling methods that can be implemented with low hardware cost. Secondly, to overcome the challenge of trading off overflow and rounding error, a shiftable fixed-point data format is used in this framework. Finally, we propose a double-width deployment technique to boost inference performance with the same bit-width hardware multiplier. The experimental results show that the input data of convolution in the training process can be quantized to 2-bit for CIFAR-10 dataset, 6-bit for ImageNet dataset, with negligible accuracy degradation. Furthermore, our fixed-point train-ing framework has the potential to save at least 75% energy of the computation in the training process.