Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Ambiguity of Emotion Labels Through Meta-Learning

Emotion labels in emotion recognition corpora are highly noisy and ambiguous, due to the annotators' subjective perception of emotions. Such ambiguity may introduce errors in automatic classification and affect the overall performance. We therefore propose a dynamic label correction and sample contribution weight estimation model. Our model is based on a standard BLSTM model with attention with two extra parameters. The first learns a new corrected label distribution, and is aimed to fix the inaccurate labels from the dataset. The other instead estimates the contribution of each sample to the training process, and is aimed to ignore the ambiguous and noisy samples while giving higher weight to the clear ones. We train our model through an alternating optimization method, where in the first epoch we update the neural network parameters, and in the second we keep them fixed to update the label correction and sample importance parameters. When training and evaluating our model on the IEMOCAP dataset, we obtained a weighted accuracy (WA) and unweighted accuracy (UA) of respectively 65.9% and 61.4%. This yielded an absolute improvement of 2.5%, 2.7% respectively compared to a BLSTM with attention baseline, trained on the corpus gold labels.
Content and linguistic biases in the peer review process of artificial intelligence conferences

We analysed a recently released dataset of scientific manuscripts that were either rejected or accepted from various conferences in artificial intelligence. We used a combination of semantic, lexical and psycholinguistic analyses of the full text of the manuscripts to compare them based on the outcome of the peer review process. We found that accepted manuscripts were written with words that are less frequent, that are acquired at an older age, and that are more abstract than rejected manuscripts. We also found that accepted manuscripts scored lower on two indicators of readability than rejected manuscripts, and that they also used more artificial intelligence jargon. An analysis of the references included in the manuscripts revealed that the subset of accepted submissions were more likely to cite the same publications. This finding was echoed by pairwise comparisons of the word content of the manuscripts (i.e. an indicator or semantic similarity), which was higher in the accepted manuscripts. Finally, we predicted the peer review outcome of manuscripts with their word content, with words related to machine learning and neural networks positively related with acceptance, whereas words related to logic, symbolic processing and knowledge-based systems negatively related with acceptance.
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans

We consider the problem of mapless collisionavoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot’s perspective and social-safety to measure the impact of robot’s actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of the robot’s action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experiments show the learned policy smoothly transferred to different scenarios without any fine tuning. We observe that our method demonstrates timeefficient path planning behavior with high success rate in the mapless navigation task. Furthermore, we test our method in a navigation task among dynamic crowds, considering both low and high volume traffic. Our learned policy demonstrates cooperative behavior that actively drives our robot into traffic flows while showing respect to nearby pedestrians. Evaluation videos are at https://sites.google. com/view/ssw-batman
Density-dependent mating success and colour polymorphism in females of the damselfly Ischnura graellsii (Odonata: Coenagrionidae)

Female-limited colour polymorphism is very common in odonates. One of the forms has male-like colouring (androchromotypics), while the other(s) is cryptic (gynochromotypics). Three hypotheses have been proposed to explain the maintenance of this polymorphism: (1) higher reproductive isolation of androchromotypics, balanced by higher predation on this form; (2) androchromotypics avoid unnecessary long matings, but suffer greater predation; and (3) androchromotypics avoid male harassment at high density, but have lower mating success at low density. To test these hypotheses I measured survivorship and mating success of andro and gynochromotypic females of I.graellsii in two natural populations with different densities in Galicia (NW Spain)
Low-frequency Radio Absorption in Tycho’s Supernova Remnant

Tycho’s supernova remnant (SNR) is the remnant of the SN Ia explosion SN1572. In this work we present new low-frequency radio observations with the LOw Frequency ARray (LOFAR) Low-band and High-band Antennae, centered at 58 MHz and 143 MHz, and with an angular resolution of 41″ and 6″, respectively. We compare these maps to Very Large Array maps at 327 MHz and 1420 MHz, and detect the effect of low-frequency absorption in some regions of the remnant due to the presence of free electrons along the line of sight. We investigate two origins for the low-frequency free–free absorption that we observe: external absorption from the foreground and internal absorption from Tycho’s unshocked ejecta. The external absorption could be due to an ionized thin, diffuse cavity surrounding the SNR (although this cavity would need to be very thin to comply with the neutral fraction required to explain the remnant’s optical lines), or it could be due to an over-ionized molecular shell in the vicinity of the remnant. We note that possible ionizing sources are the X-ray emission from Tycho, its cosmic rays, or radiation from Tycho’s progenitor. For the internal absorption, we are limited by our understanding of the spectral behavior of the region at unabsorbed radio frequencies. However, the observations are suggestive of free–free absorption from unshocked ejecta inside Tycho’s reverse shock.
Joint Superchannel Digital Signal Processing for Ultimate Bandwidth Utilization.

Modern optical communication systems transmit multiple frequency channels, each operating very close to its theoretical limit. The total bandwidth can reach 10THz limited by the optical amplifiers. Maximizing spectral efficiency, the throughput per bandwidth is thus crucial. Replacing independent lasers with an optical frequency comb can enable very dense packing by overcoming relative drifts. However, to date, interference from non-ideal spectral shaping prevents exploiting the full potential of frequency combs. Here, we demonstrate comb-enabled multi-channel digital signal processing, which overcomes these limitations. Each channel is detected using an independent coherent receiver and processed at two samples-per-symbol. By accounting for the unique comb stability and exploiting aliasing in the design of the dynamic equalizer, we show that the optimal spectral shape changes, resulting in a higher signal to noise ratio that pushes the optimal symbol rate towards and even \emph{above} the channel spacing, resulting in the first example of frequency-domain super-Nyquist transmission with multi-channel detection for optical systems. The scheme is verified both in back-to-back configuration and in single span transmission of a 21 channel superchannel originating from a 25GHz-spaced frequency comb. By jointly processing 3 wavelength channels at a time, we achieve spectral efficiency beyond what is possible with independent channels. At the same time, one significantly relaxes the hardware requirements on digital-to-analog resolution and bandwidth, and well as filter tap numbers. Our results show that comb-enabled multi-channel processing can overcome the limitations of classical dense wavelength division multiplexing systems by enabling tighter spacing to reach the ultimate spectral efficiency in optical communications.
Online Spectrogram Inversion for Low-Latency Audio Source Separation

Audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a spectrogram inversion algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has been exploited successfully in several recent works. However, this algorithm suffers from two drawbacks, which we address in this letter. First, it has originally been introduced in a heuristic fashion: we propose here a rigorous optimization framework in which MISI is derived, thus proving the convergence of this algorithm. Besides, while MISI operates offline, we propose here an online version of MISI called oMISI, which is suitable for low-latency source separation, an important requirement for e.g., hearing aids applications. oMISI also allows one to use alternative phase initialization schemes exploiting the temporal structure of audio signals. Experiments conducted on a speech separation task show that oMISI performs as well as its offline counterpart, thus demonstrating its potential for real-time source separation.
New Life for Old Media: Investigations into Speech Synthesis and Deep Learning-based Colorization for Audiovisual Archives

The Netherlands Institute for Sound and Vision (NISV) is the national audiovisual archive and media museum of the Netherlands. The collections comprise of over one million hours of audiovisual material. One of the collections is that of the so-called “Polygoon” newsreels from the 20th century. This paper outlines recent explorations where Artificial Intelligence technologies are used to enrich this archival material to allow for new types of engagement. Firstly, we investigated leveraging an existing, limited corpus of broadcast narration by a single person to build a working text-to-speech (TTS) system. Secondly, we investigated the possibility of colorization of old black-and-white video footage from the Polygoon newsreel collection using Deep Learning approaches.
Machine learning for molecular simulation

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 71 is April 20, 2020. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Confidence intervals for policy evaluation in adaptive experiments

Significance Randomized controlled trials are central to the scientific process, but they can be costly. For example, a clinical trial may assign patients to treatments that are detrimental to them. Adaptive experimental designs, such as multiarmed bandit algorithms, reduce costs by increasing the probability of assigning promising treatments over the course of the experiment. However, because observations collected by these methods are dependent and their distribution is nonstationary, statistical inference can be challenging. We propose a treatment-effect estimator that has an asymptotically unbiased and normal test statistic under straightforward, relatively weak conditions on the adaptive design. This estimator generalizes for a variety of parameters of interest. Adaptive experimental designs can dramatically improve efficiency in randomized trials. But with adaptively collected data, common estimators based on sample means and inverse propensity-weighted means can be biased or heavy-tailed. This poses statistical challenges, in particular when the experimenter would like to test hypotheses about parameters that were not targeted by the data-collection mechanism. In this paper, we present a class of test statistics that can handle these challenges. Our approach is to adaptively reweight the terms of an augmented inverse propensity-weighting estimator to control the contribution of each term to the estimator’s variance. This scheme reduces overall variance and yields an asymptotically normal test statistic. We validate the accuracy of the resulting estimates and their CIs in numerical experiments and show that our methods compare favorably to existing alternatives in terms of mean squared error, coverage, and CI size.
Swarm characteristics of Antarctic krill Euphausia superba relative to the proximity of land during summer in the Scotia Sea

We studied the relationship between the proximity of land and the distribution and swarming characteristics of Antarctic krill across the Scotia Sea in January and February 2003. Krill swarms identified with a Simrad EK60 (38 kHz, 120 kHz) echosounder were grouped into 4 categories according to distance from shoreline: 0 to 50 km, 50 to 100 km, 100 to 200 km and >200 km. Cross-sectional areas of swarms were significantly larger inshore, with a mean value of 120 m(2) in the 0 to 50 km zone compared to < 80 m(2) further offshore. The packing concentration of krill within inshore swarms was also significantly greater, with an average density of 95 ind. m(-3) compared to between 24 and 31 ind. m(-3) elsewhere. A large proportion of the biomass was concentrated into a small number of large, dense swarms throughout the survey area, and this trend increased with decreasing distance from shore. The highest median number of swarms per km and krill acoustic biomass per km was found in the 50 to 100 km zone. However, a significantly greater number of large, biomass-rich swarms occurred in the 0 to 50 km zone compared to all other zones. Swarms in the 0 to 50 km zone were also significantly further apart. The majority of swarms were located in the upper 50 m during the daytime although they were marginally deeper in the night in offshore regions. Krill are likely to move between inshore and offshore environments continuously over their lifetimes. The change in krill behaviour between environments could be a response to local predatory threats over short spatial and temporal scales.
Development And Characterization Of Copper-Coated Basalt Fiber Reinforced Aluminium Alloy Composites

The aim of the work was to investigate the effect of copper coated basalt short fiber reinforced Al alloy composites and was compared with uncoated basalt short fiber Al metal matrix composites (MMCs). Five different wt. % of basalt short fiber reinforced Al MMCs were prepared by squeeze casting technique 2.5, 5, 7.5 and 10% basalt short fiber MMCs. Both type of MMCs (coated and uncoated basalt fiber reinforced Al MMCs) were tested for elastic modulus, ultimate tensile strength and ductility along with microstructural change as per ASTM standard. The result shows the coating of Cu on basalt short fiber increases the Young‟s modulus, due to the homogenous distribution of basalt short fiber and the alignment of these fibers parallel to the axis with minimum segregation in the alloys, the ultimate tensile strength also increased due to their matrix strengthening and reduction in the alloy grain size, but the ductility significantly decreases due to the voids. The microstructure and fracture surfaces of both MMCs were examined using optical and SEM micrographs respectively. The lack of observed fiber pull-out on fracture and improved mechanical properties resulted due to the good wetting of the fibers by the liquid alloy.
Adaptive Kernel Value Caching for SVM Training

Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the least recently used (LRU) replacement strategy for caching kernel values. However, as we analyze in this article, the LRU strategy generally achieves high hit ratio near the final stage of the training but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used), which replaces the EFU kernel values that enhance least frequently used (LFU). Our experimental results show that EFU often has 20% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called hybrid caching for the SVM training (HCST), which has a novel mechanism to automatically adapt the better caching strategy in different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multilabel classification, multiclass classification, and regression problems. Compared with other existing caching strategies, HCST achieves 20% more reduction in training time on average.
Meaningful Framing of IS Education Gamification

Meaningful framing involves engaging in an activity that satisfies an individual’s personal objective while supporting the goals of others (Mekler et al., 2013). People generally want to participate in altruistic endeavors that benefit other people (McGonigal, 2011). There is empirical evidence suggesting that the integration of meaningful framing into a gamified learning environment is a useful mechanism for encouraging a more focused participation among learners. An experiment to investigate the influence of meaningful framing revealed that people focused on completing assigned tasks effectively as opposed to competing to complete tasks in order to earn points and rewards (McGonigal, 2011). This result indicates that meaningful framing has a positive effect on learning. Researchers have shown the effectiveness of using gamification to promote learners’ engagement, participation and performance (Zichermann et al., 2011). These researchers also highlight mixed findings documented in prior studies, indicating that the design and implementation of gamification (Cheong et al., 2014) may be instrumental to the extent that it is successful in effecting the desired change in target learners. Following this line of reasoning, we explore the meaningful framing of game elements to promote learning and engagement in IS education. Meaningful framing is often presented and studied in terms of long-term objectives. For instance, majority of the studies in this area focus on long-term goals such as becoming an outstanding system analyst. However, there is little attention to short-term goals that are mostly within the immediate control of the learner. With the increasing low retention rates in IS programs across the nation, it is imperative to explore how meaningful framing may be useful for helping students connect their learning to short-term goals that are sustainable. This study seeks to explore what short term goals are relevant to the IS field from the learners’ perspective. We look to attendees for feedback, advice and insights on how to develop IS curriculum to consider ways to implement meaningful framing in the gamification of course modules.
A note on totally-omnitonal graphs

Let the edges of the complete graph $K_n$ be coloured red or blue, and let $G$ be a graph with $|V(G)| < n$. Then ot(n,G) is defined to be the minimum integer, if it exists, such that any such colouring of $K_n$ contains a copy of $G$ with $r$ red edges and $b$ blue edges for any $r,b \geq 0$ with $r+b= e(G)$. If ot(n,G) exists for every sufficiently large $n$, we say that $G$ is \emph{omnitonal}. Omnitonal graphs were introduced by Caro, Hansberg and Montejano [arXiv:1810.12375,2019]. Now let $G_1$, $G_2$ be two copies of $G$ with their edges coloured red or blue. If there is a colour-preserving isomorphism from $G_1$ to $G_2$ we say that the 2-colourings of $G$ are equivalent. Now we define tot(n,G) to be the minimum integer, if it exists, such that any such colouring of $K_n$ contains all non-quivalent colourings of $G$ with $r$ red edges and $b$ blue edges for any $r,b \geq 0$ with $r+b= e(G)$. If tot(n, G) exists for every sufficiently large $n$, we say that G is \emph{totally-omnitotal}. In this note we show that the only totally-omnitonal graphs are stars or star forests namely a forest all of whose components are stars.
Metrics Induced by Quantum Jensen-Shannon-Renyí and Related Divergences

We study symmetric divergences on Hermitian positive definite matrices generated by functions closely related to Pick-Nevanlinna functions. Our main result proves that the square root of these divergences is a distance metric. As a corollary we obtain the metric property for Quantum Jensen-Shannon-Tsallis divergences (parameterized by $\alpha\in [0,2]$), which in turn (for $\alpha=1$) yields a proof of the metric property conjectured by Briet and Harremoes a decade ago (\emph{Properties of classical and quantum Jensen-Shannon divergence}, Phy.\ Rev.\ A, \textbf{79}, 052311 (2009)). A somewhat more intricate argument also establishes metric properties of Jensen-Renyi divergences (for $\alpha \in (0,1)$), and outlines a techniques for proving this property for symmetric divergences involving completely monotonic functions.
Alternative job search strategies in remote rural and peri-urban labour markets: the role of social networks

This paper examines the importance of informal methods (especially social networking) to the job search strategies used by unemployed people. It compares three areas: a small rural town; a larger, more sparsely populated, remote rural area; and a centrally-located, peri-urban labour market. The analysis is based first on survey research undertaken with 490 job seekers across the study areas. Emerging issues were then followed up during a series of twelve focus groups. The survey research showed that job seekers in the rural study areas were significantly more likely to use social networks to look for work. However, those who had experienced repeated or long-term periods out of work, the unskilled and young people were significantly less likely to use such networks. Focus groups confirmed the perceived importance of social networking to the job search process in rural areas, in contrast to the more marginal role such methods appear to play in peri-urban settings. For many rural job seekers, formal job search activities conducted through Jobcentres were seen as largely symbolic, lacking the practical value of social networking. These results suggest that service providers seeking to assist unemployed people in rural areas need to address the problems faced by many disadvantaged job seekers who are currently caught between their lack of social network relations and the absence of local public employment service facilities in more remote communities.