Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated System for Mobile Image-Based Dietary Assessment

Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning techniques coupled with widely available mobile devices present new opportunity to improve the accuracy of dietary assessment that is cost-effective, convenient and timely. However, the quality and quantity of datasets are essential for achieving good performance for automated image analysis. Building a large image dataset with high quality groundtruth annotation is a challenging problem, especially for food images as the associated nutrition information needs to be provided or verified by trained dietitians with domain knowledge. In this paper, we present the design and development of an mobile, image-based dietary assessment system to capture and analyze dietary intake, which has been deployed in both controlled-feeding and community-dwelling dietary studies. Our system is capable of collect high quality food images in naturalistic settings and provide groudtruth annotations for developing new computational approaches.
Evaluating the impact of local tracing partnerships on the performance of contact tracing for COVID-19 in England

Assessing the impact of an intervention using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. In this paper, we present a novel method to estimate intervention effects in such a setting by generalising existing approaches based on the factor analysis model and developing a Bayesian algorithm for inference. Our method is one of the few that can simultaneously: deal with outcomes of mixed type (continuous, binomial, count);increase efficiency in the estimates of the causal effects by jointly modelling multiple outcomes affected by the intervention;easily provide uncertainty quantification for all causal estimands of interest. We use the proposed approach to evaluate the impact that local tracing partnerships (LTP) had on the effectiveness of England's Test and Trace (TT) programme for COVID-19. Our analyses suggest that, overall, LTPs had a small positive impact on TT. However, there is considerable heterogeneity in the estimates of the causal effects over units and time.
Structured Prediction in NLP - A survey

Over the last several years, the field of Structured prediction in NLP has had seen huge advancements with sophisticated probabilistic graphical models, energy-based networks, and its combination with deep learning-based approaches. This survey provides a brief of major techniques in structured prediction and its applications in the NLP domains like parsing, sequence labeling, text generation, and sequence to sequence tasks. We also deep-dived into energy-based and attention-based techniques in structured prediction, identified some relevant open issues and gaps in the current state-of-the-art research, and have come up with some detailed ideas for future research in these fields.
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning

While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This is a limiting factor for real-world applications of RL. Although the research community continuously aims at improving both robustness and generalization of RL algorithms, unfortunately it still lacks an open-source set of well-defined benchmark problems based on a consistent theoretical framework, which allows comparing different approaches in a fair, reliable and reproducibleway. To fill this gap, we propose CARL, a collection of well-known RL environments extended to contextual RL problems to study generalization. We show the urgent need of such benchmarks by demonstrating that even simple toy environments become challenging for commonly used approaches if different contextual instances of this task have to be considered. Furthermore, CARL allows us to provide first evidence that disentangling representation learning of the states from the policy learning with the context facilitates better generalization. By providing variations of diverse benchmarks from classic control, physical simulations, games and a real-world application of RNA design, CARL will allow the community to derive many more such insights on a solid empirical foundation.
Characterization of moderately thermostable α-amylase-producing Bacillus licheniformis from decaying potatoes and sweet potatoes

Bacillus licheniformis is an endospore-forming bacterium that is commonly present in soil. The aim of the present study was to isolate and characterize local strains of α-amylase producing B. licheniformis. Soil samples were collected from the decaying surfaces of potatoes and sweet potatoes. The samples were identified by Gram staining, spore staining, and motility testing under aerobic conditions. Twenty-three isolates were found to be from the Bacillus genus and six of those (26%) were found to be B. licheniformis. Two representative samples were run on API 20E and API 50 CH biochemical kits, and 16S rDNA polymerase chain reaction. The isolates were confirmed to be B. licheniformis by the API-web program and molecular detection. Partially purified α-amylase was characterized to determine the effect of the incubation period, temperature tolerance, and pH stability. The activity peaked at 740 mU/mL after 42 h of culturing. The relative activity reached a maximum at 55 °C and a pH of 8.0. The decaying surfaces of potatoes and sweet potatoes are promising sources of α-amylase-producing strains of B. licheniformis that can tolerate both a high temperature and drastic pH level.
Thermometry of Gaussian quantum systems using Gaussian measurements

We study the problem of estimating the temperature of Gaussian systems with feasible measurements, namely Gaussian and photo-detection-like measurements. For Gaussian measurements, we develop a general method to identify the optimal measurement numerically, and derive the analytical solutions in some relevant cases. For a class of single-mode states that includes thermal ones, the optimal Gaussian measurement is either Heterodyne or Homodyne, depending on the temperature regime. This is in contrast to the general setting, in which a projective measurement in the eigenbasis of the Hamiltonian is optimal regardless of temperature. In the general multi-mode case, and unlike the general unrestricted scenario where joint measurements are not helpful for thermometry (nor for any parameter estimation task), it is open whether joint Gaussian measurements provide an advantage over local ones. We conjecture that they are not useful for thermal systems, supported by partial analytical and numerical evidence. We further show that Gaussian measurements become optimal in the limit of large temperatures, while {on/off} photo-detection-like measurements do it for when the temperature tends to zero. Our results therefore pave the way for effective thermometry of Gaussian quantum systems using experimentally realizable measurements.
Fully Self-Supervised Class Awareness in Dense Object Descriptors

We address the problem of inferring self-supervised dense semantic correspondences between objects in multi-object scenes. The method introduces learning of class-aware dense object descriptors by providing either unsupervised discrete labels or confidence in object similarities. We quantitatively and qualitatively show that the introduced method outperforms previous techniques with more robust pixel-to-pixel matches. An example robotic application is also shown~- grasping of objects in clutter based on corresponding points.
Oscillating non-progressing flows induce directed cell motion

We present a deformation-dependent propulsion phenomenon for soft particles such as cells in microchannels. It is based on a broken time reversal symmetry generated by a fast forward and slow backward motion of a fluid which does not progress on average. In both sections, soft particles deform differently and thus progress relatively to the liquid. We demonstrate this by using LatticeBoltzmann simulations of ubiquitous red blood cells in microchannels, as well as simulations for capsules and minimal soft tissue models in unbounded Poiseuille flows. The propulsion of the soft particles depends besides the oscillation asymmetry on their size, deformation type and elasticity. This is also demonstrated by analytical calculations for a minimal model. Our findings may stimulate a rethinking of particle sorting methods. For example, healthy and malignant cells often differ in their elasticity. With the proposed method, several cell types with different deformability can be separated simultaneously without labeling or obstacles in a microfluidic device.
Extracting Major Topics of COVID-19 Related Tweets

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period; however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the "reopening", "death cases", "telecommuting", "protests", "anger expression", "masking", "medication", "social distance", "second wave", and "peak of the disease" titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time.
Quantified Facial Expressiveness for Affective Behavior Analytics

The quantified measurement of facial expressiveness is crucial to analyze human affective behavior at scale. Unfortunately, methods for expressiveness quantification at the video frame-level are largely unexplored, unlike the study of discrete expression. In this work, we propose an algorithm that quantifies facial expressiveness using a bounded, continuous expressiveness score using multimodal facial features, such as action units (AUs), landmarks, head pose, and gaze. The proposed algorithm more heavily weights AUs with high intensities and large temporal changes. The proposed algorithm can compute the expressiveness in terms of discrete expression, and can be used to perform tasks including facial behavior tracking and subjectivity quantification in context. Our results on benchmark datasets show the proposed algorithm is effective in terms of capturing temporal changes and expressiveness, measuring subjective differences in context, and extracting useful insight.
Highly Efficient Synthesis of a Ladder‐Type BN‐Heteroacene and Polyheteroacene

A ladder-type BN-heteroacene and its polymer derivative were obtained in high yields by using a Pd0-catalyzed Suzuki reaction followed by ring closure with phenylboron dichloride. The BN-heteroacene and polyheteroacene show good solubility in commonly used solvents, high thermal stability and strong blue and green light emission, respectively, in tetrahydrofuran. These properties indicate that the BN-heteroacene and polyheteroacene have great potential for application in organic electronics.
Pilot Decontamination Processing in Cell-Free Massive MIMO

This letter focuses on the pilot contamination problem in the uplink and downlink of cell-free massive multiple-input multiple-output networks with different degrees of cooperation between access points. The optimum minimum mean square error processing can take advantage of large-scale fading coefficients for canceling the interference of pilot-sharing user-equipments and thus achieves asymptotically unbounded capacity. However, it is computationally demanding and can only be implemented in a fully centralized network. Here, sub-optimal schemes are derived that provide unbounded capacity with linear-growing complexity and using only local channel estimates but global channel statistics. This makes them suited for both centralized and distributed networks. In this latter case, the best performance is achieved with a generalized maximum ratio combiner that maximizes a capacity bound based on channel statistics only.
Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations

This paper mainly studies the gradient-based Jacobi-type algorithms to maximize two classes of homogeneous polynomials with orthogonality constraints, and establish their convergence properties. For the first class of homogeneous polynomials subject to a constraint on a Stiefel manifold, we reformulate it as an optimization problem on a unitary group, which makes it possible to apply the gradient-based Jacobi-type (Jacobi-G) algorithm. Then, if the subproblem can always be represented as a quadratic form, we establish the global convergence of Jacobi-G under any one of three conditions. The convergence result for the first condition is an easy extension of the result in [Usevich et al. SIOPT 2020], while other two conditions are new ones. This algorithm and the convergence properties apply to the well-known joint approximate symmetric tensor diagonalization. For the second class of homogeneous polynomials subject to constraints on the product of Stiefel manifolds, we reformulate it as an optimization problem on the product of unitary groups, and then develop a new gradient-based multi-block Jacobi-type (Jacobi-MG) algorithm to solve it. We establish the global convergence of Jacobi-MG under any one of the above three conditions, if the subproblem can always be represented as a quadratic form. This algorithm and the convergence properties are suitable to the well-known joint approximate tensor diagonalization. As the proximal variants of Jacobi-G and Jacobi-MG, we also propose the Jacobi-GP and Jacobi-MGP algorithms, and establish their global convergence without any further condition. Some numerical results are provided indicating the efficiency of the proposed algorithms.
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning

Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online1.
Rearrangement hopane: Molecular tracers for filling pathway in oil reservoirs

ABSTRACT Based on the analysis of C30*/C30H thermal stability, C30*/C30H feasibility in hydrocarbon-charging migration, the results show that C30*/C30H might not only show a good response to thermal maturity in mature to high mature oil. Comparing C30*/C30H parameters with methyl dibenzothiophene ratio (MDR), exposure/shielding alkyl dibenzothiophenes, found that the C30*/C30H parameters and two other parameters have great positive correlation, indicating that the C30*/C30H parameters are not only good thermal maturity parameters but also good in hydrocarbon-charging migration. Using C30*/C30H parameter tracer, Chang 7 oil reservoir group of hydrocarbon-charging migration direction in Longdong area appears as the main hydrocarbon charging in Hua Chi area. The oil migration directions are southwest and northeast, and hydrocarbon charging occurs by the wave surface filling migration; also, the oil has the characteristics of continuous filling. Preliminary results show that C30*/C30H parameter is an effective parameter for tracer oil filling way.
Short-and-Sparse Deconvolution Via Rank-One Constrained Optimization (Roco)

Short-and-sparse deconvolution (SaSD) aims to recover a short kernel and a long and sparse signal from their convolution. In the literature, formulation of blind deconvolution is either a convex programming via a matrix lifting of convolution, or a bilinear Lasso. Optimization solvers are typically based on bilinear factorizations. In this paper, we formulate SaSD as a non-convex optimization with a rank-one matrix constraint, hence referred to as Rank-One Constrained Optimization (ROCO). The solver is based on alternating direction method of multipliers (ADMM). It operates on the full rank-one matrix rather than bilinear factorizations. Closed form updates are derived for the efficiency of ADMM. Simulations include both synthetic data and real images. Results show substantial improvements in recovery accuracy (at least 19dB in PSNR for real images) and comparable runtime compared with benchmark algorithms based on bilinear factorization.
Connect Everywhere: Wireless Connectivity in Protected Areas

Mobile connectivity has become more important, especially for visitors to parks and protected areas. However, governments' policies prohibit cable wiring in these areas in order to preserve the beauty and protect the historical interest of the landscape. Through observed practices from other countries, mobile network operators and other licensed service providers can cooperate with the administrators of such protected areas in order to provide mobile connectivity without disturbing the environment. However, the most pervasive problem is the high energy consumption of the wireless systems and it becomes expensive to power them using the electricity grid. One attractive solution is to make use of green energy to power the communication systems and then share the base station (BS) infrastructure that is co-located with the edge server, an entity responsible for computing the offloaded delay-sensitive workloads. To alleviate this problem, this paper offers a resource management solution that seeks to minimize the energy consumption per communication site through (i) BS infrastructure and resource sharing, and (ii) assisted (peer-to-peer) computation offloading for the energy-constrained communication sites, within a protected area. Using this resource management strategy guarantees a Quality of Service (QoS). The performance evaluation conducted through simulations validates our analysis as the prediction variations observed shows greater accuracy between the harvested energy and the traffic load. Towards energy savings, the proposed algorithm achieves a 52 % energy savings when compared with the 46 % obtained by our benchmark algorithm. The energy savings that can be achieved decreases as the QoS is prioritized, within each communication site, and when the number of active computing resources increases.
Simultaneous Constraints on the Star Formation History and Nucleosynthesis of Sculptor dSph

We demonstrate that using up to seven stellar abundance ratios can place observational constraints on the star formation histories (SFHs) of Local Group dSphs, using Sculptor dSph as a test case. We use a one-zone chemical evolution model to fit the overall abundance patterns of α elements (which probe the core-collapse supernovae that occur shortly after star formation), s-process elements (which probe AGB nucleosynthesis at intermediate delay times), and iron-peak elements (which probe delayed Type Ia supernovae). Our best-fit model indicates that Sculptor dSph has an ancient SFH, consistent with previous estimates from deep photometry. However, we derive a total star formation duration of ∼0.9 Gyr, which is shorter than photometrically derived SFHs. We explore the effect of various model assumptions on our measurement and find that modifications to these assumptions still produce relatively short SFHs of duration ≲1.4 Gyr. Our model is also able to compare sets of predicted nucleosynthetic yields for supernovae and AGB stars, and can provide insight into the nucleosynthesis of individual elements in Sculptor dSph. We find that observed [Mn/Fe] and [Ni/Fe] trends are most consistent with sub-M Ch Type Ia supernova models, and that a combination of “prompt” (delay times similar to core-collapse supernovae) and “delayed” (minimum delay times ≳50 Myr) r-process events may be required to reproduce observed [Ba/Mg] and [Eu/Mg] trends.
Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is important as it yields the optimal policy that achieves the maximum reward on a give task. In the case of the Hamilton-Jacobi-Isaacs equation, which includes an adversary controlling the environment and minimizing the reward, the obtained policy is also robust to perturbations of the dynamics. In this paper we propose continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI). These algorithms leverage the non-linear control-affine dynamics and separable state and action reward of many continuous control problems to derive the optimal policy and optimal adversary in closed form. This analytic expression simplifies the differential equations and enables us to solve for the optimal value function using value iteration for continuous actions and states as well as the adversarial case. Notably, the resulting algorithms do not require discretization of states or actions. We apply the resulting algorithms to the Furuta pendulum and cartpole. We show that both algorithms obtain the optimal policy. The robustness Sim2Real experiments on the physical systems show that the policies successfully achieve the task in the real-world. When changing the masses of the pendulum, we observe that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at https://sites.google. com/view/rfvi.
Hypernetworks for Continual Semi-Supervised Learning

Learning from data sequentially arriving, possibly in a non i.i. d. way, with changing task distribution over time is called continual learning. Much of the work thus far in continual learning focuses on supervised learning and some recent works on unsupervised learning. In many domains, each task contains a mix of labelled (typically very few) and unlabelled (typically plenty) training examples, which necessitates a semi-supervised learning approach. To address this in a continual learning setting, we propose a framework for semi-supervised continual learning called Meta-Consolidation for Continual Semi-Supervised Learning (MCSSL). Our framework has a hypernetwork that learns the meta-distribution that generates the weights of a semi-supervised auxiliary classifier generative adversarial network $(\textit{Semi-ACGAN})$ as the base network. We consolidate the knowledge of sequential tasks in the hypernetwork, and the base network learns the semi-supervised learning task. Further, we present $\textit{Semi-Split CIFAR-10}$, a new benchmark for continual semi-supervised learning, obtained by modifying the $\textit{Split CIFAR-10}$ dataset, in which the tasks with labelled and unlabelled data arrive sequentially. Our proposed model yields significant improvements in the continual semi-supervised learning setting. We compare the performance of several existing continual learning approaches on the proposed continual semi-supervised learning benchmark of the Semi-Split CIFAR-10 dataset.