Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling Neural Networks via Energy Dissipation

The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques. Lately, many works have demonstrated that learning-based approaches with suitable network architectures even exhibit superior performance for the solution of (ill-posed) image reconstruction problems such as deblurring, super-resolution, or medical image reconstruction. The drawback of purely learning-based methods, however, is that they cannot provide provable guarantees for the trained network to follow a given data formation process during inference. In this work we propose energy dissipating networks that iteratively compute a descent direction with respect to a given cost function or energy at the currently estimated reconstruction. Therefore, an adaptive step size rule such as a line-search, along with a suitable number of iterations can guarantee the reconstruction to follow a given data formation model encoded in the energy to arbitrary precision, and hence control the model's behavior even during test time. We prove that under standard assumptions, descent using the direction predicted by the network converges (linearly) to the global minimum of the energy. We illustrate the effectiveness of the proposed approach in experiments on single image super resolution and computed tomography (CT) reconstruction, and further illustrate extensions to convex feasibility problems.
Topic Spotting using Hierarchical Networks with Self Attention

Success of deep learning techniques have renewed the interest in development of dialogue systems. However, current systems struggle to have consistent long term conversations with the users and fail to build rapport. Topic spotting, the task of automatically inferring the topic of a conversation, has been shown to be helpful in making dialog system more engaging and efficient. We propose a hierarchical model with self attention for topic spotting. Experiments on the Switchboard corpus show the superior performance of our model over previously proposed techniques for topic spotting and deep models for text classification. Additionally, in contrast to offline processing of dialog, we also analyze the performance of our model in a more realistic setting i.e. in an online setting where the topic is identified in real time as the dialog progresses. Results show that our model is able to generalize even with limited information in the online setting.
LC-ESI-QTOF-MS/MS Characterisation of Phenolics in Herbal Tea Infusion and Their Antioxidant Potential

Ginger (Zingiber officinale R.), lemon (Citrus limon L.) and mint (Mentha sp.) are commonly consumed medicinal plants that have been of interest due to their health benefits and purported antioxidant capacities. This study was conducted on the premise that no previous study has been performed to elucidate the antioxidant and phenolic profile of the ginger, lemon and mint herbal tea infusion (GLMT). The aim of the study was to investigate and characterise the phenolic contents of ginger, lemon, mint and GLMT, as well as determine their antioxidant potential. Mint recorded the highest total phenolic content, TPC (14.35 ± 0.19 mg gallic acid equivalent/g) and 2,2′-azino-bis(3-e-thylbenzothiazoline-6-sulfonic acid), ABTS (24.25 ± 2.18 mg ascorbic acid equivalent/g) antioxidant activity. GLMT recorded the highest antioxidant activity in the reducing power assay, RPA (1.01 ± 0.04 mg ascorbic acid equivalent/g) and hydroxyl radical scavenging assay, •OH-RSA (0.77 ± 0.08 mg ascorbic acid equivalent/g). Correlation analysis showed that phenolic content positively correlated with the antioxidant activity. Venn diagram analysis revealed that mint contained a high proportion of exclusive phenolic compounds. Liquid chromatography coupled with electrospray ionisation and quadrupole time of flight tandem mass spectrometry (LC-ESI-QTOF-MS/MS) characterised a total of 73 phenolic compounds, out of which 11, 31 and 49 were found in ginger, lemon and mint respectively. These characterised phenolic compounds include phenolic acids (24), flavonoids (35), other phenolic compounds (9), lignans (4) and stilbene (1). High-performance liquid chromatography photometric diode array (HPLC-PDA) quantification showed that GLMT does contain a relatively high concentration of phenolic compounds. This study presented the phenolic profile and antioxidant potential of GLMT and its ingredients, which may increase the confidence in developing GLMT into functional food products or nutraceuticals.
Learning Group Convolution for Efficient Inference

Efficient inference of Convolutional Neural Networks is a thriving topic recently. It is desirable to achieve the maximal test accuracy under given inference budget constraints when deploying a pre-trained model. Network pruning is a commonly used technique while it may produce irregular sparse models that can hardly gain actual speed-up. Group convolution is a promising pruning target due to its regular structure; however, incorporating such structure into the pruning procedure is challenging. It is because structural constraints are hard to describe and can make pruning intractable to solve. The need for configuring sparsity that maximises test accuracy also increases difficulty. This paper presents an efficient method to address this challenge. We formulate group convolution pruning as finding the optimal channel permutation to impose structural constraints and solve it efficiently by heuristics. We also apply local search to exploring the sparsity configuration that maximises test accuracy. Compared to prior work, results show that our method produces competitive group convolution models for various tasks within a shorter pruning period and enables rapid model sparsity exploration subject to inference budget constraints
The International Linear Collider. A Global Project

A large, world-wide community of physicists is working to realise an exceptional physics program of energy-frontier, electron-positron collisions with the International Linear Collider (ILC). This program will begin with a central focus on high-precision and model-independent measurements of the Higgs boson couplings. This method of searching for new physics beyond the Standard Model is orthogonal to and complements the LHC physics program. The ILC at 250 GeV will also search for direct new physics in exotic Higgs decays and in pair-production of weakly interacting particles. Polarised electron and positron beams add unique opportunities to the physics reach. The ILC can be upgraded to higher energy, enabling precision studies of the top quark and measurement of the top Yukawa coupling and the Higgs self-coupling. The key accelerator technology, superconducting radio-frequency cavities, has matured. Optimised collider and detector designs, and associated physics analyses, were presented in the ILC Technical Design Report, signed by 2400 scientists. There is a strong interest in Japan to host this international effort. A detailed review of the many aspects of the project is nearing a conclusion in Japan. Now the Japanese government is preparing for a decision on the next phase of international negotiations, that could lead to a project start within a few years. The potential timeline of the ILC project includes an initial phase of about 4 years to obtain international agreements, complete engineering design and prepare construction, and form the requisite international collaboration, followed by a construction phase of 9 years.
A Goal-based Framework Integrating Disparate Media Choice Theories

Media choice and selection theories are numerous and highly fragmented. While much of this theorizing has helped IS researchers better understand what influences people’s media choices and selections, the proliferation of theories also leads to redundancies, and decreased clarity and impact. Here, we develop and apply an approach to "better know what we know" about a set of related theories. We present a unifying framework of media choice that (1) builds on prior work, (2) streamlines disparate lines of research, and (3) links media choices to goals. In addition to advancing media choice theorizing, the framework is a useful template for relating future research contributions to previous theories, an effective teaching aid, and a tool for practitioners applying media choice theories.
Measurement-only quantum computation with Floquet Majorana corner modes

Majorana modes, typically arising at the edges of one-dimensional topological superconductors, are considered to be a promising candidate for encoding nonlocal qubits in fault-tolerant quantum computing. Here we exploit the two-dimensional geometry of Majorana corner modes in second-order topological superconductors to establish measurement-only quantum computation. It is shown that eight Majorana corner modes emerge when such systems are periodically driven, through which two nonlocal logical qubits and one nonlocal ancilla qubit can be constructed. Quantum gate operations can then be implemented by a designed series of parity measurements of topologically protected Majorana corner modes, accomplished via Mach-Zehnder type interference in the conductance between different corners of a second-order topological superconductor. Our theoretical proposal represents a scenario in which topologically protected single- and two-qubit gate operations can be carried out in a minimal setup, thus potentially establishing an efficient and low-cost building block for Majorana-based qubit architectures.
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability

The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding
Evaluation of Physiological and Quality Parameters of Green Asparagus Spears Subjected to Three Treatments against the Decline Syndrome

Green asparagus (Asparagus officinalis L.) is a widely grown and consumed crop which provides high-level nutritional interest. In recent years, the decline syndrome in asparagus plantations has been rapidly augmenting. This syndrome causes the early death of whole plants, also negatively affecting the new replanting. Decline causes notable economic losses in the sector. The objective of this work was to verify the effect of different treatments against asparagus decline syndrome on the physiological parameters and nutritional quality of the spears. To meet the objective, four different treatments were applied to asparagus plots strongly affected by decline syndrome: (T1) untreated control soil, (T2) biofumigation with Brassica pellets, (T3) biofumigation with chicken manure pellets, and (T4) disinfestation of the soil with Dazomet. The cumulative yield and physiological and quality parameters of green asparagus spears were studied. Thus, malondialdehyde (MDA), photosynthetic pigments, glutathione (GSH), ascorbate (AsA), total phenols, flavonoids, anthocyanin, antioxidant test, mineral nutrients, and the amino acid profile were measured on asparagus spears. The results showed that the Brassica pellets and Dazomet treatments were the most effective against the damage caused by the decline syndrome. However, it would be necessary to monitor the evolution in the following years.
Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure

The redundancy is widely recognized in Convolutional Neural Networks (CNNs), which enables to remove some unimportant filters from convolutional layers so as to slim the network with acceptable performance drop. Inspired by the linearity of convolution, we seek to make some filters increasingly close and eventually identical for network slimming. To this end, we propose Centripetal SGD (C-SGD), a novel optimization method, which can train several filters to collapse into a single point in the parameter hyperspace. When the training is completed, the removal of the identical filters can trim the network with NO performance loss, thus no finetuning is needed. By doing so, we have partly solved an open problem of constrained filter pruning on CNNs with complicated structure, where some layers must be pruned following the others. Our experimental results on CIFAR-10 and ImageNet have justified the effectiveness of C-SGD-based filter pruning. Moreover, we have provided empirical evidences for the assumption that the redundancy in deep neural networks helps the convergence of training by showing that a redundant CNN trained using C-SGD outperforms a normally trained counterpart with the equivalent width.
Progressive Pose Attention Transfer for Person Image Generation

This paper proposes a new generative adversarial network to the problem of pose transfer, i.e., transferring the pose of a given person to a target one. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, our generated person images possess better appearance consistency and shape consistency with the input images, thus significantly more realistic-looking. The efficacy and efficiency of the proposed network are validated both qualitatively and quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed architecture can generate training images for person re-identification, alleviating data insufficiency.
Halo Spin from Primordial Inner Motions

The standard explanation for galaxy spin starts with the tidal-torque theory (TTT), in which an ellipsoidal dark-matter protohalo, which comes to host the galaxy, is torqued up by the tidal gravitational field around it. We discuss a complementary picture, using the relatively familiar velocity field, instead of the tidal field, whose intuitive connection to the surrounding, possibly faraway matter arrangement is more obscure. In this 'spin from primordial inner motions' (SPIM) concept, implicit in TTT derivations but not previously emphasized, the angular momentum from the gravity-sourced velocity field inside a protohalo largely cancels out, but has some excess from the aspherical outskirts. At first, the net spin scales according to linear theory, a sort of comoving conservation of familiar angular momentum. Then, at collapse, it is conserved in physical coordinates. Small haloes are then typically subject to secondary exchanges of angular momentum. The TTT is useful for analytic estimates. But a literal interpretation of the TTT is inaccurate in detail, without some implicit concepts about smoothing of the velocity and tidal fields. This could lead to misconceptions, for those first learning about how galaxies come to spin. Protohaloes are not perfectly ellipsoidal and do not uniformly torque up, as in a naive interpretation of the TTT; their inner velocity fields retain substantial dispersion. Furthermore, quantitatively, given initial conditions and protohalo boundaries, SPIM is more direct and accurate than the TTT to predict halo spins. We also discuss how SPIM applies to rotating filaments, and the relation between halo mass and spin, in which the total spin of a halo can be thought of as a sum of random contributions.
Relations between HEXACO personality and ideology variables are mostly genetic in nature

Existing work indicates that socio-political attitudes (or: ideology) are associated with personality, with Social Dominance Orientation and Right-Wing Authoritarianism relating most strongly to honesty-humility and openness to experience, the two value-related domains of the HEXACO framework. Using a sample of 7067 twins and siblings of twins (including 1376 complete twin pairs), we examined the degree to which these relations arise from common genetic and environmental sources. Heritability estimates for the HEXACO personality and ideology variables ranged from . 34 to . 58. Environmental factors shared by twins reared together showed negligible effects on individual differences in personality and ideology. At the phenotypic level, Social Dominance Orientation and Right-Wing Authoritarianism dimensions related most strongly to honesty-humility and openness to experience. These associations were mostly explained by genetic factors (48%–93%). Genetic correlations between openness to experience and the ideology scales ranged from –. 29 to –. 53; those between honesty-humility and the ideology scales ranged from –. 31 to –. 43. None of the environmental correlations exceeded |r| = . 18. These results suggest that the relations between the two value-related domains of the HEXACO personality model and ideology are mostly genetic in nature, and that there is substantial overlap in the heritable components of personality and ideology.
Is Waze joking? Perceived Irrationality dynamics in user-robot interactions

Artificial intelligence, as well as the use of smarter and smarter systems are gradually pervading our everyday life. While AI-embedded systems are increasingly becoming ‘human-like’, individuals tend to fictionize interpersonal relationships with them. We conceptualize the notion of Perceived Irrationality as the discrepancy between an individual’s expectations and his/her perceptions towards a smart system’s recommendation. We then develop a conceptual model that aims at better understanding the inner mechanisms that govern perceived irrationality. This research opens up a vast uncharted research territory that proposes to adopt a long-term relational lens towards the study of humans / smart systems interactions.
Adoption of a Social Learning Platform in Higher Education: An Extended UTAUT Model Implementation

The aim of this research is to investigate the factors influencing the adoption of a social learning platform called PairForm using an extended unified theory of acceptance and use of technology (UTAUT) model. The UTAUT extension consists of adding three personal characteristics of students, namely autonomy, anxiety, and attitude. Data obtained from 85 French-speaking students and 14 English-speaking students at the Skema Business School, a higher education institution, showed good reliability coefficients and satisfactory convergent and discriminant validities. Regression analysis suggests the facilitating conditions construct is the main predictor of behavioral intention to use and behavioral use of PairForm. Attitude is the only personal characteristic that explains behavioral intention to use. In the light of these results, we propose recommendations that, if implemented, could create more favorable conditions for the use of social learning technologies.
The power of moral words: Loaded language generates framing effects in the extreme dictator game

Understanding whether preferences are sensitive to the frame has been a major topic of debate in the last decades. For example, several works have explored whether the dictator game in the give frame gives rise to a different rate of pro-sociality than the same game in the take frame, leading to mixed results. Here we contribute to this debate with two experiments. In Study 1 (N=567) we implement an extreme dictator game in which the dictator either gets $0.50 and the recipient gets nothing, or the opposite (i.e., the recipient gets $0.50 and the dictator gets nothing). We experimentally manipulate the words describing the available actions using six terms, from very negative (e.g., stealing) to very positive (e.g., donating) connotations. We find that the rate of pro-sociality is affected by the words used to describe the available actions. In Study 2 (N=221) we ask brand new participants to rate each of the words used in Study 1 from “extremely wrong” to “extremely right”. We find that these moral judgments can explain the framing effect in Study 1. In sum, our studies provide evidence that framing effects in an extreme Dictator game can be generated using morally loaded language.
A 4d N=1 Cardy Formula

We study the asymptotic behavior of the (modified) superconformal index for 4d $\mathcal{N} = 1$ gauge theory. By considering complexified chemical potential, we find that the `high-temperature limit' of the index can be written in terms of the conformal anomalies $3c-2a$. We also find macroscopic entropy from our asymptotic free energy when the Hofman-Maldacena bound $1/2 < a/c < 3/2$ for the interacting SCFT is satisfied. We study $\mathcal{N} = 1$ theories that are dual to AdS$_5 \times Y^{p, p}$ and find that the Cardy limit of our index accounts for the Bekenstein-Hawking entropy of large black holes.
On Simulating the Proton-irradiation of O2 and H2O Ices Using Astrochemical-type Models, with Implications for Bulk Reactivity

Many current astrochemical models explicitly consider the species that comprise the bulk of interstellar dust grain ice mantles separately from those in the top few monolayers. Bombardment of these ices by ionizing radiation—whether in the form of cosmic rays, stellar winds, or radionuclide emission—represents an astrochemically viable means of driving a rich chemistry even in the bulk of the ice mantle, now supported by a large body of work in laboratory astrophysics. In this study, using an existing rate-equation-based astrochemical code modified to include a method of considering radiation chemistry recently developed by us, we attempted to simulate two such studies in which (a) pure O2 ice at 5 K and (b) pure H2O ice at 16 K and 77 K, were bombarded by keV H+ ions. Our aims were twofold: (1) to test the capability of our newly developed method to replicate the results of ice-irradiation experiments, and (2) to determine how bulk chemistry in such a well-constrained system is best handled using the same gas-grain codes that are used to model the interstellar medium. We found that our modified astrochemical model was able to reproduce both the abundance of O3 in the 5 K pure O2 ice, as well as both the abundance of H2O2 in the 16 K water ice and the previously noted decrease of hydrogen peroxide at higher temperatures. However, these results require the assumption that radicals and other reactive species produced via radiolysis react quickly and non-diffusively with neighbors in the ice.
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs

This paper describes the UM-IU@LING’s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.