Miracle-dz/UniFAR-FTUs huggingface dataset:
Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation

Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory. One main challenge is the problem of spatiotemporal variations (e.g. different people may perform the same activity in various ways). Therefore, we exploit unlabeled videos to address this problem by reformulating the action segmentation task as a cross-domain problem with domain discrepancy caused by spatio-temporal variations. To reduce the discrepancy, we propose SelfSupervised Temporal Domain Adaptation (SSTDA), which contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics, achieving better performance than other Domain Adaptation (DA) approaches. On three challenging benchmark datasets (GTEA, 50Salads, and Breakfast), SSTDA outperforms the current state-of-the-art method by large margins (e.g. for the F1@25 score, from 59.6% to 69.1% on Breakfast, from 73.4% to 81.5% on 50Salads, and from 83.6% to 89.1% on GTEA), and requires only 65% of the labeled training data for comparable performance, demonstrating the usefulness of adapting to unlabeled target videos across variations. The source code is available at https://github.com/cmhungsteve/SSTDA.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out ‘hard’ and ‘easy’ instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBerta, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.
Claim Check-Worthiness Detection as Positive Unlabelled Learning

As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wikipedia. To date, there has been no structured comparison of these various tasks to understand their relatedness, and no investigation into whether or not a unified approach to all of them is achievable. In this work, we illuminate a central challenge in claim check-worthiness detection underlying all of these tasks, being that they hinge upon detecting both how factual a sentence is, as well as how likely a sentence is to be believed without verification. As such, annotators only mark those instances they judge to be clear-cut check-worthy. Our best performing method is a unified approach which automatically corrects for this using a variant of positive unlabelled learning that finds instances which were incorrectly labelled as not check-worthy. In applying this, we out-perform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.
Minimum bounded chains and minimum homologous chains in embedded simplicial complexes

We study two optimization problems on simplicial complexes with homology over $\mathbb{Z}_2$, the minimum bounded chain problem: given a $d$-dimensional complex $\mathcal{K}$ embedded in $\mathbb{R}^{d+1}$ and a null-homologous $(d-1)$-cycle $C$ in $\mathcal{K}$, find the minimum $d$-chain with boundary $C$, and the minimum homologous chain problem: given a $(d+1)$-manifold $\mathcal{M}$ and a $d$-chain $D$ in $\mathcal{M}$, find the minimum $d$-chain homologous to $D$. We show strong hardness results for both problems even for small values of $d$; $d = 2$ for the former problem, and $d=1$ for the latter problem. We show that both problems are APX-hard, and hard to approximate within any constant factor assuming the unique games conjecture. On the positive side, we show that both problems are fixed parameter tractable with respect to the size of the optimal solution. Moreover, we provide an $O(\sqrt{\log \beta_d})$-approximation algorithm for the minimum bounded chain problem where $\beta_d$ is the $d$th Betti number of $\mathcal{K}$. Finally, we provide an $O(\sqrt{\log n_{d+1}})$-approximation algorithm for the minimum homologous chain problem where $n_{d+1}$ is the number of $d$-simplices in $\mathcal{M}$.
Hybrid modeling: Applications in real-time diagnosis

Reduced-order models that accurately abstract high fidelity models and enable faster simulation is vital for real-time, model-based diagnosis applications. In this paper, we outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models to generate reduced-order models from high fidelity models. We are using such models for real-time diagnosis applications. Specifically, we have developed machine learning inspired representations to generate reduced order component models that preserve, in part, the physical interpretation of the original high fidelity component models. To ensure the accuracy, scalability and numerical stability of the learning algorithms when training the reduced-order models we use optimization platforms featuring automatic differentiation. Training data is generated by simulating the high-fidelity model. We showcase our approach in the context of fault diagnosis of a rail switch system. Three new model abstractions whose complexities are two orders of magnitude smaller than the complexity of the high fidelity model, both in the number of equations and simulation time are shown. The numerical experiments and results demonstrate the efficacy of the proposed hybrid modeling approach.
Path Planning Using Probability Tensor Flows

Probability models are emerging as a promising framework to account for “intelligent” behavior. In this article, probability propagation is discussed to model agent's motion in potentially complex grids that include goals and obstacles. Tensor messages in the state-action space (due to grid structure, states are 2-D and the concomitant probability distributions are represented by 3-D arrays), propagated bi-directionally on a Markov chain, provide crucial information to guide the agent's decisions. The discussion is carried out with reference to a set of simulated grids and includes scenarios with multiple goals and multiple agents. The visualization of the tensor flow reveals interesting clues about how decisions are made by the agents. The emerging behaviors are very realistic and demonstrate great potential for the application of this framework to real environments.
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms

The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed on the robot. In this work we explicitly address the correspondence problem by introducing a distance measure between dissimilar embodiments. This measure is then used as a loss function for static pose imitation and as a feedback signal within a model-free deep reinforcement learning framework for dynamic movement imitation between two anthropomorphic robotic arms in simulation. We find that the measure is well suited for describing the similarity between embodiments and for learning imitation policies by distance minimization.
Updated constraints on the Georgi-Machacek model from LHC Run 2

We study the experimental constraints on the Georgi-Machacek model from direct searches for the new Higgs bosons and measurements of the properties of the discovered 125 GeV Higgs boson. We apply these by interfacing the public codes HiggsBounds 5.2. 0 and HiggsSignals 2.2. 1, which implement a large collection of direct-search constraints on extra Higgs bosons and measurements of the properties of the 125 GeV state to the model calculator GMCALC. We also implement constraints involving searches for doubly-charged Higgs bosons and Drell-Yan production of a neutral Higgs with decays to photon pairs directly into GMCALC; these channels are not included in HiggsBounds but provide important constraints on the model, especially at low mass. We identify new constraints on the model from $H_3^0 \to Z h$ and $H \to hh$ and point out that these channels remain promising. We also determine the allowed range of couplings of the SM-like Higgs boson after all experimental constraints are applied and show that the direct searches for additional Higgs bosons are generally more constraining than the measurements of the 125 GeV Higgs boson couplings. The updated GMCALC code will be released as version 1.5. 0.
Finding binaries from phase modulation of pulsating stars with Kepler – VI. Orbits for 10 new binaries with mischaracterized primaries

Measuring phase modulation in pulsating stars has proved to be a highly successful way of finding binary systems. The class of pulsating main-sequence A and F variables known as delta Scuti stars are particularly good targets for this, and the \textit{Kepler} sample of these has been almost fully exploited. However, some \textit{Kepler} $\delta$ Scuti stars have incorrect temperatures in stellar properties catalogues, and were missed in previous analyses. We used an automated pulsation classification algorithm to find 93 new $\delta$ Scuti pulsators among tens of thousands of F-type stars, which we then searched for phase modulation attributable to binarity. We discovered 10 new binary systems and calculated their orbital parameters, which we compared with those of binaries previously discovered in the same way. The results suggest that some of the new companions may be white dwarfs.
Phase transitions in a decentralized graph-based approach to human language

Zipf's law establishes a scaling behavior for word-frequencies in large text corpora. The appearance of Zipfian properties in human language has been previously explained as an optimization problem for the interests of speakers and hearers. On the other hand, human-like vocabularies can be viewed as bipartite graphs. The aim here is double: within a bipartite-graph approach to human vocabularies, to propose a decentralized language game model for the formation of Zipfian properties. To do this, we define a language game, in which a population of artificial agents is involved in idealized linguistic interactions. Numerical simulations show the appearance of a phase transition from an initially disordered state to three possible phases for language formation. Our results suggest that Zipfian properties in language seem to arise partly from decentralized linguistic interactions between agents endowed with bipartite word-meaning mappings.
Permute to Train: A New Dimension to Training Deep Neural Networks

We show that Deep Neural Networks (DNNs) can be efficiently trained by permuting neuron connections. We introduce a new family of methods to train DNNs called Permute to Train (P2T). Two implementations of P2T are presented: Stochastic Gradient Permutation and Lookahead Permutation. The former computes permutation based on gradient, and the latter depends on another optimizer to derive the permutation. We empirically show that our proposed method, despite only swapping randomly weighted connections, achieves comparable accuracy to that of Adam on MNIST, Fashion-MNIST, and CIFAR-10 datasets. It opens up possibilities for new ways to train and regularize DNNs.
Beam Designs for Millimeter-Wave Backhaul With Dual-Polarized Uniform Planar Arrays

This paper proposes hybrid beamforming designs for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) backhaul systems equipped with uniform planar arrays (UPAs) of dual-polarization antennas at both the transmit and receive base stations. The proposed beamforming designs are to near-optimally solve optimization problems taking the dual-polarization UPA structure into account. Based on the solutions of optimization problems, this paper shows it is possible to generate the optimal dual-polarization beamformer from the optimal single-polarization beamformer sharing the same optimality. As specific examples, squared error and magnitude of inner product are considered respectively for optimization criteria. To optimize proposed beamformers, partial channel information is needed, and the use of low overhead pilot sequences is also proposed to figure out the required information. Simulation results verify that the resulting beamformers have the most uniform gain (with the squared error criterion) or the highest average gain (with the magnitude of inner product criterion) in the covering region with the UPA of dual-polarization antennas.
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization

Deep Learning with noisy labels is a practically challenging problem in weakly-supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels.
Cystic Fibrosis in Gaza Strip: Mutation Analysis and Major Disease Manifestations

Cystic fibrosis (CF) is a devastating autosomal recessive disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator ( CFTR ) gene the product of which is responsible for the transport of chloride ions across the membranes of epithelial cells. The disease is characterized by abnormally high sweat electrolytes, exocrine pancreatic insufficiency and chronic pulmonary obstruction, causing progressive disability and ultimately death. The aim of the present work was to determine for the first time in Gaza strip the common CF mutations and the major disease manifestations in Palestinian CF patients. The study population consisted of one hundred CF cases, 71% of them were males. The average age of the subjects was 70.6±44 months. Ninety three percent of the cases had white colored skin. Most of the CF cases (85%) were initially diagnosed by clinical manifestations and sweat test. The most frequent respiratory manifestations among CF cases were recurrent chest infections and chronic cough with viscid sputum whereas, the most frequent gastrointestinal manifestations were recurrent gastroenteritis, abdominal colic and flatulence, and malabsorption. Regarding the anthropometric measurements most CF cases had short stature (86%), 94% were underweight, and 65% had wasting. Also, 82% of CF cases were anemic. Allele-specific PCR was employed for screening DNA samples available from 64 patients for nine mutations: Δ F508, 3120+1G>A, N1303K, G85E, 1717+1G>A, G542X, W1282X, and 1209G>A, whereas, mutation 3120+1kbdel8.6kb was assayed by multiplex PCR. The results of mutation testing revealed that 61% of mutation-identified CF cases have at least a single ∆F508 allele. Moreover, 12.2% were homozygous for the 3120+1G>A CFTR mutation. Homozygous N1303K, G85E and 3120+1kbdel8.6kb mutations occurred in equal proportions and collectively represented 14.7%. The recorded CFTR homozygous mutations could be classified as: 51.2% class II, 19.5% class V, 4.9% class I and 2.4% class IV. The study shows that CF is not uncommon in Gaza strip and the findings will be useful for planning future screening and proper genetic counseling programs for Palestinian CF patients.
Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference

A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations are generally defined based on collision prediction in the existing works. However, collision is only a source of potential risks, and a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., objects influencing drivers’ behavior are risky. A new task called risk object identification is introduced. We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model. We demonstrate favorable performance on risk object identification compared with strong baselines on the Honda Research Institute Driving Dataset (HDD). Our framework achieves a substantial average performance boost over a strong baseline by 7.5%.
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

The discovery of novel materials and functional molecules can help to solve some of society’s most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering–generally denoted as inverse design–was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model’s internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models.
Privacy-Aware Time-Series Data Sharing With Deep Reinforcement Learning

Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we study the privacy-utility trade-off (PUT) in time-series data sharing. Existing approaches to PUT mainly focus on a single data point; however, temporal correlations in time-series data introduce new challenges. Methods that preserve the privacy for the current time may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We consider sharing the distorted version of a user’s true data sequence with an untrusted third party. We measure the privacy leakage by the mutual information between the user’s true data sequence and shared version. We consider both the instantaneous and average distortion between the two sequences, under a given distortion measure, as the utility loss metric. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL). We evaluate the performance of the proposed solution in location trace privacy on both synthetic and GeoLife GPS trajectory datasets. For the latter, we show the validity of our solution by testing the privacy of the released location trajectory against an adversary network.
Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning

Learning the distance metric between pairs of samples has been studied for image retrieval and clustering. With the remarkable success of pair-based metric learning losses, recent works have proposed the use of generated synthetic points on metric learning losses for augmentation and generalization. However, these methods require additional generative networks along with the main network, which can lead to a larger model size, slower training speed, and harder optimization. Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information. In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion. The proposed method generates synthetic points containing augmented information by a combination of feature points and performs hard negative pair mining to learn with the most informative feature representations. Because of its simplicity and flexibility, it can be used for existing metric learning losses without affecting model size, training speed, or optimization difficulty. Finally, the combination of embedding expansion and representative metric learning losses outperforms the state-of-the-art losses and previous sample generation methods in both image retrieval and clustering tasks. The implementation is publicly available.