Miracle-dz/UniFAR-FTUs huggingface dataset:
Variational Task Encoders for Model-Agnostic Meta-Learning

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on novel tasks. A critical challenge lies in the inherent uncertainty about whether new tasks can be considered similar to those observed before, and robust meta-learning methods would ideally reason about this to produce corresponding uncertainty estimates. We extend model-agnostic meta-learning with variational inference: we model the identity of new tasks as a latent random variable, which modulates the ﬁne-tuning of meta-learned neural networks. Our approach requires little additional computation and doesn’t make strong assumptions about the distribution of the neural network weights, and allows the algorithm to generalize to more divergent task distributions, resulting in better-calibrated uncertainty measures while maintaining accurate predictions
Low Transmission of Coronavirus via Aerosols during Outdoor Running Races and Athletic Events

Introduction: Outdoor contacts were reported to rarely result in transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Little is known, however, about the risk during popular outdoor running events. This study assessed the transfer of aerosols from infected runners to other race participants. Methods: In the experimental part of the study, a group of dummies was pulled at different speeds over an athletics track and field circuit. Fine aerosols were produced with a fog machine, large aerosols with a pesticide sprayer releasing food colorant, with the size matching the two size modes of human expiratory aerosols. The experimentally determined transfer rates fed a Monte Carlo simulation of different race distances, starting sequences and block sizes. Runners were modeled using start and end times of SwissCityMarathon ? Lucerne participants and a previously published distribution of virus emission strengths. The race distance was divided into 10-meter segments in which the transfer from the sources to collocated runners was calculated. Results: The experiments showed that fog and spray transfer decreased with increasing distance from the source. Increased speed was associated with decreased fog but increased spray transfer. The simulations suggest that more runners received small amounts of virus by fog-transfer. However, critical virus-transfers defined as more than 100 virus copies happened mostly by spray. The estimated rate of people getting a potentially infectious dose was in most races well below the simulated prevalence rate of virus-emitting runners, mostly about five-fold smaller. Changing from block starts to individual starts further reduced the estimated transfer. Only an artificial group running 30 km in close distance at high speed brought the rate above parity. Discussion: These findings suggest that outdoor running events are associated with a low risk for virus infection as long as runners are not trailing each other over very long distances.
2nd Workshop on Interactive Natural Language Technology for Explainable Artiﬁcial Intelligence

Modern AI systems automatically learn from data using sophisticated statistical models. Explaining how these systems work and how they make their predictions therefore increasingly involves producing descriptions of how different probabilities are weighted and which uncertainties underlie these numbers. But what is the best way to (automatically) present such probabilistic explanations, do people actually understand them, and what is the potential impact of such information on people’s wellbeing? In this talk, I adress these questions in the context of systems that automatically generate personalised health information. The emergence of large national health registeries, such as the Dutch cancer registry, now make it possible to automatically generate descriptions of treatment options for new cancer patients based on data of comparable patients, including health and quality of life predictions following different treatments. I describe a series of studies, in which our team has investigated to what extent this information is currently provided to people, and under which conditions people actually want to have access to these kind of data-driven explanations. Additionally, we have studied whether there are different profiles in information needs, and what the best way is to provide probabilistic information and the associated undertainties to people.
Intent Discovery With Or Without Labeled Data Using Dependency Parser

In dialogue applications, machine learning classiﬁcation models are often used 1 to classify user utterances into different intents that help to understand the users. 2 In real world scenarios, however, some utterances may not belong to any of the 3 anticipated intent categories. Furthermore, supervised classiﬁcation models are 4 not a viable solution when data of a new domain is introduced without the corre-5 sponding labels. In this work, we present a clustering and evaluation approach 6 that can be used in semi-supervised or unsupervised modes, depending on the 7 (non-)availability of training data for new intent discovery. This method assigns 8 meaningful intent-labels by determining the optimal number of clusters and eval-9 uating the performance of the clustering results. In addition, it assigns a TF-IDF 10 score to individual samples within a cluster
Polynomial Commitment with a One-to-Many Prover and Applications

Veriﬁable Secret Sharing (VSS) is a foundational cryptographic primitive that serves as an essential building block in multi-party computation and decentralized blockchain applications. One of the most practical ways to construct VSS is through a polynomial commitment, where the dealer commits to a random polynomial whose 0-th coefﬁcient encodes the secret to be shared, and proves the evaluation of the committed polynomial at a different point to each of N veriﬁers, i.e., the polynomial commitment is used in a “one-to-many” fashion. The recent work of Tomescu et al. (IEEE S&P 2020) was the ﬁrst to consider polynomial commitment with “one-to-many prover batching”, such that the prover can prove evaluations at N different points at the cost of (cid:101) O ( 1 ) proofs. However, their scheme is not optimal and requires a trusted setup. In this paper, we asymptotically improve polynomial commitment with one-to-many prover batching. We propose two novel schemes. First, we propose a scheme with optimal asymptotics in all dimensions in the trusted setup setting. Second, we are the ﬁrst to consider one-to-many prover batching for transparent polynomial commitments, and we propose a transparent scheme whose performance approximately matches the best-known scheme in the trusted setup setting. We implement our schemes and evaluate their performance. Our scheme in the trusted setup setting improves the proof size by 20 × and the veriﬁer time by 7 . 8 × for 2 21 parties, with a small overhead on the prover time. Our transparent polynomial commitment removes the trusted setup and further improves the prover time by 2 . 3 × .
Demonstrating the Impact of Modelling Decisions

The modelling problem involves making compromises be-tween a variety of competing factors, including planning efficiency, plan language usefulness, and real-world optimisation goals. Optimal plans in the resulting model may appear sub-optimal when executed in the world. As a consequence, a human observer might find it difficult to comprehend the apparent inefficient behaviours of the agent, which might impact on the human’s trust of the agent. In this work we consider modelling decisions, such as abstractions, and their impact on the resulting plans. Our aim is to build a general approach that can assist a user to better understand both the implications of a modelling step and provide justification to support the modelling step. As a start, we have extended an off-the-shelf plan visualisation tool to provide plan failure visualisations, to demonstrate the impact of these modelling decisions to the user.
Collaboration and Networking in Cooperative Standard Setting (cid:3)

We examine factors behind (cid:133)rms(cid:146)decisions to contribute private resources to the creation of a public good in cooperative standard setting. Our study highlights a novel expla-nation: (cid:133)rms seek to improve their positions in an inter-(cid:133)rm (social) network. The empirical analyses utilize panel data from wireless telecommunications. In the standard-setting organization we study, (cid:133)rms develop new technical speci(cid:133)cations in small committees. Our results demonstrate that social network connections to peers in(cid:135)uence (cid:133)rms(cid:146)decisions to join and thus provide resources for these committees. Additionally, standard speci(cid:133)cations tend to be produced by committees where participants complement rather than compete with one another.
ICLASS 1.0: a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation and application

This paper provides a description of ICLASS 1.0: a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer or the exchange of gases, moisture, heat and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow to assimilate various streams of observations (ﬂuxes, mixing ratios at multiple heights, .) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework 5 allows to retrieve parameters in an objective manner, and enables to estimate information that is difﬁcult to obtain directly by observations, for example free-tropospheric mixing ratios or stomatal conductances. Furthermore it allows to estimate possible biases in observations. Modelling the carbon cycle at ecosystem level is one of the main intended ﬁelds of application. The physical model around which the framework is constructed is relatively simple, yet contains the core physics to model the essentials of a well-mixed boundary layer and of land–atmosphere exchange. The model includes an explicit description of the 10 atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, used for minimisation of this function. An implemented Monte-Carlo way of running ICLASS can further help to handle non-linearity, and provides posterior statistics on the estimated parameters. The paper provides a technical description 15 of the framework, includes a validation of the adjoint code, as well as tests for the full inverse modelling For temperature, a total of 7 observation heights are included, but we only show 3 for brevity. The cost function is reduced from a value of 4702 to 126. The χ 2 goodness of ﬁt statistic has a value of 0.80 for the optimised run, indicating a slight overﬁtting (or non-optimal error speciﬁcations). For all observation streams, the root mean squared error (Sect. Table and the mean bias error (Eq. 26, not shown) are reduced after optimisation. The ratio of model and observation variance
Optical Study of Fuel Spray Penetration and Initial Combustion Location under PPC Conditions

Low temperature combustion modes, such as Homogeneous Charge Compression Ignition (HCCI) and Partially Premixed Combustion (PPC), have been researched over recent decades since the concepts show promise for high efficiency and low emissions compared to conventional diesel combustion. PPC is an intermediate combustion type ranging from HCCI-like combustion to diesel-like combustion. The purpose of this paper is to study optically how the combustion and ignition are affected by different start of injection (SOI) timings. The study is carried out in an optically accessible heavy-duty single-cylinder engine with swirl. The intake pressure was kept constant while the intake temperature was varied to keep the combustion phasing (CA50) constant at ∼3 CAD atdc during an SOI sweep. The fuel used is a mix of primary reference fuels with octane number 81. To determine where the combustion starts, high-speed combustion imaging is used to detect the natural luminosity. The liquid spray is detected by illuminating the combustion chamber by laser diodes and utilizing the Mie scattering. The results indicate that the change in ignition behavior is affected by the interaction between the fuel spray and the piston, as different fuel/air ratios are formed in the squish region and in the bowl. (Less)
Robot guided Computed Tomography −Production Monitoring in Automotive Industry 4.0

Newly emerging technologies of e-mobility together with the necessity of lightweight construction require involved industries to be capable of monitoring those new technologies and materials-both in production and development. In order to keep pace with fast development cycles we need monitoring technology to provide fast feedback on new constructions during prototype development. Only with quick access to information about these new developments, short times between an idea and the serial production of a new model can be met.
Neural networks for inverse control with system priors

Controlling a physical system to behave in a desired manner requires a prior 1 knowledge of the system, having access to a model or a collection of labeled 2 data consisting of the desired inputs-outputs of the system. When the system 3 is unknown or labeled data is not available or expensive to acquire, resorting to 4 approaches that do not rely on the use of training data is inevitable. In this work, 5 we propose an algorithm based on untrained neural networks that can be applied to 6 a physical system in its most general form to obtain the required input that would 7 result in a desired target output. We showcase the applicability of our algorithm to 8 experimental phase-retrieval problems in the complex environment of a scattering 9 medium whose input-output relation follows a nonlinear and slowly time-varying 10 setting. We show that despite partial measurements of the system, comparable 11 ﬁdelity to that of fully-observed methods or supervised networks is achievable
2D/3D Intermodel Registration of Quantitative Magnetic Resonance Images

Quantitative Magnetic Resonance Imaging ( qMRI ) is backed by extensive validation in research literature but has seen limited use in clinical practice because of long acquisition times, lack of standard-ization and no statistical models for analysis. Our research focuses on developing a novel intermodal 2D slice to 3D volumetric pipeline for an emerging qMR technology that aims to bridge the gap between research and practice. The two-part method first initializes the registration using a 3D reconstruction technique then refines it using 3D to 2D projection method. Intermediate results promise feasibility and efficacy of our proposed 2D/3D multi-dimensional intermodal registration process.
A UTO C ODER : L EVERAGING T RANSFORMERS FOR A UTOMATIC C ODE S YNTHESIS

Synthesizing programs from natural language descriptions is a challenging task. In this paper, we leverage the power of transformer-based language models for the task of program synthesis. We experiment with two variants of transformers and showcase their superior performance than the existing SOTA models. We also discuss the qualitative differences in the learned representation of these two variants. Finally, we compared both these models through the lens of " degree of memorization" and demonstrated that the vanilla transformer model has a higher affinity towards memorizing the training data than the other variant.
R ECOGNIZING AND OVERCOMING THE GREEDY NA - TURE OF LEARNING IN MULTI - MODAL DEEP NEURAL NETWORKS

We hypothesize that due to the greedy nature of learning in multi-modal deep neural networks, these models tend to rely on just one modality while under-utilizing the other modalities. We observe empirically that such behavior hurts their overall generalization. To estimate the model’s modality-wise dependency, we compute the gain on the accuracy when the model has access to an additional modality and refer to this gain as the conditional utilization rate of the added modality. In the experiments, we consistently observe an imbalance in conditional utilization rates between modalities, across multiple tasks and architectures. Since conditional utilization rate cannot be computed efficiently during training, we introduce an proxy for it based on the pace at which the model learns from each modality, which we refer to as conditional learning speed. We propose an algorithm to balance the conditional learning speed between modalities during training and demonstrate that it indeed addresses the issue of greedy learning. The proposed algorithm improves the model’s generalization on three datasets: Colored MNIST (Kim et al., 2019), Princeton ModelNet40 (Wu et al., 2015), and NVIDIA Dynamic Hand Gesture Dataset (Molchanov et al., 2016).
A survey of energy drinks consumption amongst medical students and foundation year doctors in Malta

Objectives: To explore the consumption patterns of energy drinks and associated factors, amongst medical students and foundation year doctors in Malta. Methods: Data was collected from medical students and foundation year doctors from the Faculty of Medicine and Surgery, University of Malta, by means of an electronically administered cross-sectional, self-reported survey. The response rate was 42% ( n =305, M=124, F=181). IBM SPSS 23 was used for statistical analysis. Results: Energy drinks consumption amongst the sample was 68.2% (n= 208). Most participants (60.1%) started consuming energy drinks between 16-20 years, followed by those between 11-15 years (34.6%). Males are more likely to consume energy drinks than females. Participants who consume energy drinks are more likely to also drink coffee (85.1%), alcohol (88.0%) and smoke tobacco (18.3%). Common reasons for drinking energy drinks were for mixing with alcohol (37.5%) and during studying/major projects (30.8%). The majority of the participants (52.1%) experience psychological side effects due to energy drinks, the most common being stimulating/hyperactivity (42.6%), followed by anxiety (14.8%). Over half of the participants (54.8%) claimed to suffer from physical side effects Conclusions: Energy drink consumption is common amongst this population and certain aspects of consumption are a cause for concern. Greater public education of the risks of these drinks is recommended especially to young age groups. Legislation may be required to control the marketing and sales and accessibility of these drinks.
From soil to sea: Sources and transport of organic carbon traced by tetraether lipids and sediments in the monsoonal Godavari River, India

Monsoonal rivers play an important role in the land-to-sea transport of soil-derived organic carbon (OC). However, spatial and temporal variation in the concentration, composition, and fate of OC in these rivers remains poorly understood. We investigate soil-to-sea transport of OC by the Godavari River in India using branched glycerol dialkyl glycerol tetraether 15 (brGDGT) lipids in soils, river suspended particulate matter (SPM), riverbed sediments, and in a marine sediment core from the Bay of Bengal. The abundance and composition of brGDGTs in SPM and sediments in the Godavari River differs between the dry and wet season. In the dry season, 6-methyl brGDGTs dominate SPM and riverbed sediments in the whole basin. Currently, mobilisation and transport of soils from the upper basin is limited due to deficient rainfall and damming. This promotes aquatic brGDGT production in this part of the basin, which is reflected by a high relative abundance of 6-methyl 20 brGDGTs in both seasons. In the wet season, brGDGT distributions in SPM from the lower basin closely resemble those in soils, mostly from the North and East Tributaries, corresponding to precipitation patterns. The brGDGT composition in SPM and sediments from the delta suggests that soil OC is only effectively transported to the Bay of Bengal in the wet season, when the river plume extends beyond the river mouth. The sediment geochemistry indicates that also the mineral particles exported by the Godavari River primarily originate from the lower basin, similar to the brGDGTs. River depth profiles of brGDGTs in 25 the downstream Godavari reveal no hydrodynamic sorting effect in either season, indicating that brGDGTs are not associated with certain minerals. The similarity of brGDGT distributions in bulk and fine-grained sediments (≤63μm) further confirms the absence of selective transport mechanisms. Nevertheless, the composition of brGDGTs in a Holocene, marine sediment core near the river mouth appears substantially different from that in the modern Godavari basin, suggesting that terrestrialderived brGDGTs are rapidly lost upon discharge into the Bay of Bengal and/or overprinted by marine in situ production. The 30 https://doi.org/10.5194/bg-2022-116 Preprint. Discussion started: 23 May 2022 c © Author(s) 2022. CC BY 4.0 License.
Study on direct slicing data for 3D scanning electron beam rapid prototyping

Slicing method of CAD model to be sintered for 3D scanning electron beam rapid prototyping is discussed in this paper.Different slicing methods are compared,and the direct slicing algorithm based on the second development of AutoCAD software is put forward.The slicing contour information saved in BMP format files can be collected by the direct slicing algorithm from any complicated CAD models,and the BMP files is feasible to read by corresponding LabVIEW language program which can transfer slicing data from AutoCAD to Labview data file.
Supplementary Material for: The Convolution Exponential and Generalized Sylvester Flows

Proof. Let W = Q and let fAR(x) = R̃h(Rx+ b). Indeed, any orthogonal matrix is invertible, so Q can be modelled by W. Also, note that R and R̃ are upper triangular and h is an elementwise function. The matrix product of the Jacobians is triangular, and thus R̃h(Rx+ b) has a triangular Jacobian and is therefore autoregressive. Hence, it can be modelled by fAR. Further, note that [10] bound RiiR̃ii > −1 ||h′||∞ , which ensures that the constraint ∂fAR(x)i ∂xi > −1 is satisfied. Hence, z = x +Q R̃h(RQx + b) can be written as Equation 1 when writing fAR(x) = R̃h(Rx + b) and M = Q without violating any constraints on M and fAR, and is therefore a special case. Remark 1: The increased expressitivity originates from fAR and not from W. To see why, suppose we replace Q and Q in the original formulation by W and W−1. Consider that any real square matrix W may be decomposed as QWRW. Hence, compositions W−1R̃ and RW can be written as QWR̃ ′ and RQW, where R̃′ = R−1 W R̃ and R ′ = RRW which are both still upper triangular. Hence, we have shown that even if the orthogonal matrix Q is replaced by an invertible matrix W, the transformation can still be written in terms of a shared orthogonal matrix QW and upper triangular matrices R̃′ and R′. Therefore, the source of the increased expressitivity is not the replacement of Q by W.