Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations

During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., ‘displays warmth and confidence’, or ‘attempts to set up collaboration’) to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.
The trace metals accumulation in marine organisms of the southeastern Adriatic coast, Montenegro

The concentration and accumulation of trace metals (Co, Ni, As, Cd, Pb and Hg) were measured in sea water, sediments and marine organisms in the coastline of the Montenegro. The obtained results of trace metals in seagrass and mussels were compared with those found in the water column and sediment. Sampling was performed in the fall of 2005 at five locations in the Montenegrin coastline, Sveta Stasija, Herceg Novi, Zanjice, Budva and Bar, which present different levels and sources of human impact. The heavy metals analyses in seawater, sediment, P. oceanica and M. galloprovincialis identified the harbor of Bar as the most Hg-contaminated site, Zanjice as the most As contaminated and Sveta Stasija as the most Pb-contaminated areas of the Montenegrin coastal area. This study showed that P. oceanica may have a greater bioaccumulation capacity than M. galloprovincialis for the considered metals, except for As and Hg, and both organisms may reflect contamination in the water column and in the sediment. For the first time, seagrass P. oceanica and M. galloprovincialis were employed as metal bioindicators for the southeastern Adriatic. The results of this study could serve as a baseline in the future for the assessment of anthropogenic effects in this marine ecosystem.
Signatures of elastoviscous buckling in the dilute rheology of stiff polymers

Abstract As a stiff polymer tumbles in shear flow, it experiences compressive viscous forces that can cause it to buckle and undergo a sequence of morphological transitions with increasing flow strength. We use numerical simulations to uncover the effects of these transitions on the steady shear rheology of a dilute suspension of stiff polymers. Our results agree with classic scalings for Brownian rods in relatively weak flows but depart from them above the buckling threshold. Signatures of elastoviscous buckling include enhanced shear thinning and an increase in the magnitude of normal stress differences. We discuss our findings in the light of past work on rigid Brownian rods and non-Brownian elastic fibres and highlight the subtle role of thermal fluctuations in triggering instabilities.
STEP: Segmenting and Tracking Every Pixel

The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-world setting requiring dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. To overcome this, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP. The datasets contain long video sequences, providing challenging examples and a test-bed for studying long-term pixel-precise segmentation and tracking under real-world conditions. We further propose a novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly balances semantic and tracking aspects of this task and is more appropriate for evaluating sequences of arbitrary length. Finally, we provide several baselines to evaluate the status of existing methods on this new challenging dataset. We have made our datasets, metric, benchmark servers, and baselines publicly available, and hope this will inspire future research.
Direct-Search for a Class of Stochastic Min-Max Problems

Recent applications in machine learning have renewed the interest of the community in min-max optimization problems. While gradient-based optimization methods are widely used to solve such problems, there are however many scenarios where these techniques are not well-suited, or even not applicable when the gradient is not accessible. We investigate the use of direct-search methods that belong to a class of derivative-free techniques that only access the objective function through an oracle. In this work, we design a novel algorithm in the context of min-max saddle point games where one sequentially updates the min and the max player. We prove convergence of this algorithm under mild assumptions, where the objective of the max-player satisfies the Polyak-\L{}ojasiewicz (PL) condition, while the min-player is characterized by a nonconvex objective. Our method only assumes dynamically adjusted accurate estimates of the oracle with a fixed probability. To the best of our knowledge, our analysis is the first one to address the convergence of a direct-search method for min-max objectives in a stochastic setting.
Statistical analysis of fireballs: Seismic signature survey

Abstract Fireballs are infrequently recorded by seismic sensors on the ground. If recorded, they are usually reported as one-off events. This study is the first seismic bulk analysis of the largest single fireball data set, observed by the Desert Fireball Network (DFN) in Australia in the period 2014–2019. The DFN typically observes fireballs from cm-m scale impactors. We identified 25 fireballs in seismic time series data recorded by the Australian National Seismograph Network (ANSN). This corresponds to 1.8% of surveyed fireballs, at the kinetic energy range of $10^6$ – $10^{10}$ J. The peaks observed in the seismic time series data were consistent with calculated arrival times of the direct airwave or ground-coupled Rayleigh wave caused by shock waves by the fireball in the atmosphere (either due to fragmentation or the passage of the Mach cone). Our work suggests that identification of fireball events in the seismic time series data depends on both physical properties of a fireball (such as fireball energy and entry angle in the atmosphere) and the sensitivity of a seismic instrument. This work suggests that fireballs are likely detectable within 200 km direct air distance between a fireball and seismic station, for sensors used in the ANSN. If each DFN observatory had been accompanied by a seismic sensor of similar sensitivity, 50% of surveyed fireballs could have been detected. These statistics justify the future consideration of expanding the DFN camera network into the seismic domain.
Comparing Soil and Tissue Testing of Copper for Early Growth of Wheat

Abstract About 40% of soils used for agriculture in southwestern Australia (SWA) were acutely copper (Cu) deficient when first cleared, and Cu fertilizer needed to be applied to grow the first wheat (Triticum aestivum L.) crop, the major crop in the region. Tissue testing rather than soil testing is used in SWA to indicate when fertilizer Cu needs to be reapplied to wheat crops. In a glasshouse study using six Cu‐deficient soils from SWA, we compared four Cu soil test procedures as possible alternatives to tissue testing to predict Cu deficiency for wheat. Different critical values were obtained for different soils for all four soil test procedures, indicating separate soil test calibrations would be required for the four procedures for different soils in SWA. By contrast, critical tissue test values in plant parts (young leaves and rest of shoots) were unaffected by soil type. Developing soil Cu test calibrations would be time consuming and costly, and tissue testing has resulted in most deficiency being corrected by fertilizer applications, so there are few Cu‐deficient soils in SWA available to develop the soil test calibrations. Therefore, tissue testing using young leaves is likely to remain the most reliable method for diagnosing Cu deficiency for wheat in SWA and determining when reapplication of fertilizer Cu to soil is required.
Decohesion of glue bonds in wood connections

There is little information on the mechanism involved in destruction of furniture profile-adhesive joints. Information is especially needed on the destruction mechanisms for glue bonds in wood tenon and dowel joints upon external forces. Hence we investigated destructive processes for profile-adhesive joints and analysed the acoustic signals generated during loading of selected furniture joints. Acoustic emission (AE) is a method that allows observation and registration of destructive changes occurring in a material at the moment of stress. It provides possibilities to follow destructive processes that take place in a joint, beginning with microscopic cracks and ending up in total failure. There are links between AE signals and the causes of their excitation (Moliński 1998). AE phenomena were investigated in the context of glue overlap joints, such as in construction nodes (Porter and El-Osta 1972; Kirylov and Kovalczuk 1987; Baltruszajtis and Kovalczuk 1988; Beyon et al. 1990, 1992; Smardzewski and Gozdecki 2002; Gozdecki and Smardzewski 2005). Furniture profile joints have not yet been investigated satisfactorily. The strength of these joints depends not only on the strength of the glue line, but also on the strength of all the elements in a joint, such as dowels and holes or tenon and mortise. The main goal of this study was to determine the destruction mechanism for glue bonds in tenon and dowel joints during the action of external forces. These construction elements are commonly applied as nodes in skeleton furniture (Figure 1). The main objective was to predict critical load values that might lead to the destruction of construction nodes in furniture.
Effects of waterlogging at different stages of development on the growth and yield of peas (Pisum sativum L.)

The effects of waterlogging, for up to 8 days at different stages of development, on the growth and yield of peas have been examined. The peas were grown outdoors subject to field conditions in lysimeters containing monolith sandy loam or clay soils, in facilities where drainage and rainfall could be controlled. The experiments, made during 2 years, involved waterlogging treatments likely to be the most extreme occurring in the pea-growing areas of the UK. During each waterlogging treatment the oxygen concentration at 20 cm depth in the soil declined to less than 2% within 2 days. On average, when the soil was waterlogged to the surface for 5 days at the 1–2, 3–4 and 6–7 leaf stages (the latter being just before flowering), the losses of yield of seeds (peas) at the freezing stage were 6, 15 and 42%, respectively. The severity of the effect depended on the duration of the treatment and the height of the water-table; at the 6–7 leaf stage either 2 days waterlogging to the surface or 5 days with the water-table at 50 cm below the soil surface had little effect on seed yield. Waterlogging restricted leaf expansion and internode extension. After waterlogging at the pre-flowering stage, leaves usually senesced prematurely and the shoot apex became prematurely quiescent. In all experiments the yield of haulm was more affected by waterlogging than were the yields of pods and seeds. The losses in yield were mainly a consequence of fewer pods per plant. Waterlogging before flowering for 5 days depressed the concentrations of nitrogen, phosphorus and potassium in the haulm grown on both soils and of nitrogen in the seeds (peas) of plants in 1 year on the clay. Other treatments had little effect on nutrient concentration. After waterlogging to the surface for 5 days before flowering the uptake of water by roots was restricted, but other treatments had only a small effect, being least in the early treatments.
The Tipping Effect of Delayed Interventions on the Evolution of COVID-19 Incidence

We combine infectious disease transmission and the non-pharmaceutical (NPI) intervention response to disease incidence into one closed model consisting of two coupled delay differential equations for the incidence rate and the time-dependent reproduction number. The model contains three parameters, the initial reproduction number, the intervention strength, and the response delay. The response is modeled by assuming that the rate of change of the reproduction number is proportional to the negative deviation of the incidence rate from an intervention threshold. This delay dynamical system exhibits damped oscillations in one part of the parameter space, and growing oscillations in another, and these are separated by a surface where the solution is a strictly periodic nonlinear oscillation. For the COVID-19 pandemic, the tipping transition from damped to growing oscillations occurs for response delays of about one week, and suggests that, without vaccination, effective control and mitigation of successive epidemic waves cannot be achieved unless NPIs are implemented in a precautionary manner, rather as a response to the present incidence rate. Vaccination increases the quiet intervals between waves, but with delayed response, future flare-ups can only be prevented by establishing a post-pandemic normal with lower basic reproduction number.
Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory

This work investigates the problem of estimating the weight matrices of a stable time-invariant linear dynamical system from a single sequence of noisy measurements. We show that if the unknown weight matrices describing the system are in Brunovsky canonical form, we can efficiently estimate the ground truth unknown matrices of the system from a linear system of equations formulated based on the transfer function of the system, using both online and offline stochastic gradient descent (SGD) methods. Specifically, by deriving concrete complexity bounds, we show that SGD converges linearly in expectation to any arbitrary small Frobenius norm distance from the ground truth weights. To the best of our knowledge, ours is the first work to establish linear convergence characteristics for online and offline gradient-based iterative methods for weight matrix estimation in linear dynamical systems from a single trajectory. Extensive numerical tests verify that the performance of the proposed methods is consistent with our theory, and show their superior performance relative to existing state of the art methods.
Ricci curvature, Bruhat graphs and Coxeter groups

We consider the notion of discrete Ricci curvature for graphs defined by Schmuckenschläger [14] and compute its value for Bruhat graphs associated to finite Coxeter groups. To do so we work with the geometric realization of a finite Coxeter group and a classical result obtained by Dyer in [7]. As an applications we obtain a bound for the spectral gap of the Bruhat graph of any finite Coxeter group and an isoperimetric inequality for them. Our proofs are case-free.
Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge with continuously generated new semantics in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) takes different semantic features from multi-knowledge as input, which enables more relevant semantic features to be trained for semantic-to-visual embedding, and finally generates more generalized visual features by adaptively fusing visual features from different knowledge domain. The proposed New Feature Generator (NFG) with adaptive genetic strategy is used to enrich semantic information on the one hand, and on the other hand it greatly improves the intersection of visual feature generated by MKFNet and unseen visual faetures. Empirically, we show that our approach can achieve significantly better performance compared to existing state-of-the-art methods on a large number of benchmarks for several ZSL tasks, including traditional ZSL, generalized ZSL and zero-shot retrieval.
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning

In many contemporary applications such as healthcare, finance, robotics, and recommendation systems, continuous deployment of new policies for data collection and online learning is either cost ineffective or impractical. We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited. To solve this challenging task, we propose a new algorithmic learning framework called Model-based Uncertainty regularized and Sample Efficient Batch Optimization (MUSBO). Our framework discovers novel and high quality samples for each deployment to enable efficient data collection. During each offline training session, we bootstrap the policy update by quantifying the amount of uncertainty within our collected data. In the high support region (low uncertainty), we encourage our policy by taking an aggressive update. In the low support region (high uncertainty) when the policy bootstraps into the out-of-distribution region, we downweight it by our estimated uncertainty quantification. Experimental results show that MUSBO achieves state-of-the-art performance in the deployment constrained RL setting.
Causal Mediation Analysis with Hidden Confounders

An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway. For instance, in causal fairness, the total effect of being a male employee (i.e., treatment) constitutes its direct effect on annual income (i.e., outcome) and the indirect effect via the employee's occupation (i.e., mediator). Causal mediation analysis (CMA) is a formal statistical framework commonly used to reveal such underlying causal mechanisms. One major challenge of CMA in observational studies is handling confounders, variables that cause spurious causal relationships among treatment, mediator, and outcome. Conventional methods assume sequential ignorability that implies all confounders can be measured, which is often unverifiable in practice. This work aims to circumvent the stringent sequential ignorability assumptions and consider hidden confounders. Drawing upon proxy strategies and recent advances in deep learning, we propose to simultaneously uncover the latent variables that characterize hidden confounders and estimate the causal effects. Empirical evaluations using both synthetic and semi-synthetic datasets validate the effectiveness of the proposed method. We further show the potentials of our approach for causal fairness analysis.
Palmitic acid sophorolipid biosurfactant: from self-assembled fibrillar network (SAFiN) to hydrogels with fast recovery

Nanofibres are an interesting phase into which amphiphilic molecules can self-assemble. Described for a large number of synthetic lipids, they were seldom reported for natural lipids like microbial amphiphiles, known as biosurfactants. In this work, we show that the palmitic acid congener of sophorolipids (SLC16:0), one of the most studied families of biosurfactants, spontaneously forms a self-assembled fibre network (SAFiN) at pH below 6 through a pH jump process. pH-resolved in situ small-angle X-ray scattering (SAXS) shows a continuous micelle-to-fibre transition, characterized by an enhanced core–shell contrast between pH 9 and pH 7 and micellar fusion into a flat membrane between pH 7 and pH 6, approximately. Below pH 6, homogeneous, infinitely long nanofibres form by peeling off the membranes. Eventually, the nanofibre network spontaneously forms a thixotropic hydrogel with fast recovery rates after applying an oscillatory strain amplitude out of the linear viscoelastic regime: after being submitted to strain amplitudes during 5 min, the hydrogel recovers about 80% and 100% of its initial elastic modulus after, respectively, 20 s and 10 min. Finally, the strength of the hydrogel depends on the medium's final pH, with an elastic modulus fivefold higher at pH 3 than at pH 6. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)’.
Spacetime Harmonic Functions and Applications to Mass

In the pioneering work of Stern, level sets of harmonic functions have been shown to be an effective tool in the study of scalar curvature in dimension 3. Generalizations of this idea, utilizing level sets of so called spacetime harmonic functions as well as other elliptic equations, are similarly effective in treating geometric inequalities involving the ADM mass. In this paper, we survey recent results in this context, focusing on applications of spacetime harmonic functions to the asymptotically flat and asymptotically hyperbolic versions of the spacetime positive mass theorem, and additionally introduce a new concept of total mass valid in both settings which is encoded in interpolation regions between generic initial data and model geometries. Furthermore, a novel and elementary proof of the positive mass theorem with charge is presented, and the level set approach to the Penrose inequality given by Huisken and Ilmanen is related to the current developments. Lastly, we discuss several open problems.
Outage Probability Analysis of IRS-Assisted Systems Under Spatially Correlated Channels

This letter investigates the impact of spatial channel correlation on the outage probability of intelligent reflecting surface (IRS)-assisted single-input single-output (SISO) communication systems. In particular, we derive a novel closed-form expression of the outage probability for arbitrary phase shifts and correlation matrices of the indirect channels. To shed light on the impact of the spatial correlation, we further attain the closed-form expressions for two common scenarios met in the literature when the large-scale fading coefficients are expressed by the loss over a propagation distance. Numerical results validate the tightness and effectiveness of the closed-form expressions. Furthermore, the spatial correlation offers significant decreases in the outage probability as the direct channel is blocked.