Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Moments of Change from Longitudinal User Text

Identifying changes in individuals’ behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disadvantage of such work is the lack of a strong temporal component and the inability to make longitudinal assessments following an individual’s trajectory and allowing timely interventions. Here we define a new task, that of identifying moments of change in individuals on the basis of their shared content online. The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations). We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18.7K posts). We have developed a variety of baseline models drawing inspiration from related tasks and show that the best performance is obtained through context aware sequential modelling. We also introduce new metrics for capturing rare events in temporal windows.
EXPERIMENTAL STUDY for WIND TURBINE BLADES LIGHTNING PROTECTION

Various lightning protection measures are used for the wind turbine blade. Although in a lot of wind turbines, lightning protection measures which consist of lightning attachment points and down-conductors for allowing lightning current to flow to the earth safely are generally adopted, the influences of shape and the size etc. of the receptor on the effect of the lightning protection are slightly reported. In this paper, the results of the high voltage impulse tests to understand the difference of manner of lightning attachment are reported. Actual 29.5m length wind turbine blades are used for some tests.
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond

Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.
Reducing Activation Recomputation in Large Transformer Models

Training large transformer models is one of the most important computational challenges of modern AI. In this paper, we show how to significantly accelerate training of large transformer models by reducing activation recomputation. Activation recomputation is commonly used to work around memory capacity constraints. Rather than storing activations for backpropagation, they are traditionally recomputed, which saves memory but adds redundant compute. In this work, we show most of this redundant compute is unnecessary because we can reduce memory consumption sufficiently without it. We present two novel yet very simple techniques: sequence parallelism and selective activation recomputation. In conjunction with tensor parallelism, these techniques almost eliminate the need to recompute activations. We evaluate our approach on language models up to one trillion parameters in scale and show that our method reduces activation memory by 5x, while reducing execution time overhead from activation recomputation by over 90%. For example, when training a 530B parameter GPT-3 style model on 2240 NVIDIA A100 GPUs, we achieve a Model Flops Utilization of 54.2%, which is 29% faster than the 42.1% we achieve using recomputation. Our implementation will be available in both Megatron-LM and NeMo-Megatron.
Hierarchical Collaborative Hyper-parameter Tuning

Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.
NDGGNET-A Node Independent Gate based Graph Neural Networks

Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on. Generally, for a certain node in a given graph, a traditional GNN layer can be regarded as an aggregation from one-hop neighbors, thus a set of stacked layers are able to fetch and update node status within multi-hops. For nodes with sparse connectivity, it is difficult to obtain enough information through a single GNN layer as not only there are only few nodes directly connected to them but also can not propagate the high-order neighbor information. However, as the number of layer increases, the GNN model is prone to over-smooth for nodes with the dense connectivity, which resulting in the decrease of accuracy. To tackle this issue, in this thesis, we define a novel framework that allows the normal GNN model to accommodate more layers. Specifically, a node-degree based gate is employed to adjust weight of layers dynamically, that try to enhance the information aggregation ability and reduce the probability of over-smoothing. Experimental results show that our proposed model can effectively increase the model depth and perform well on several datasets.
The Limits of Commitment

We study partial commitment in leader-follower games. A collection of subsets cover-ing the leader’s action space determines her commitment opportunities. We characterize the outcomes resulting from all possible commitment structures of this kind. If the commitment structure is an interval partition, then the leader’s payoﬀ is bounded by the payoﬀs she obtains under the full and no-commitment benchmarks. We apply our results to study new design problems.
Towards Run-Time Search for Real-World Multi-Agent Systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.
Experimental Analysis of a Shake Table Test of Timber-Framed Structures with Stone and Earth Infill

The seismic performance of timber-framed structures filled with stones and earth mortar has been analyzed by introducing the structural subscales (cell, wall, house) at which monotonic and cyclic loadings were considered. This article aims to present the dynamic behavior of a house as determined through shaking table tests. Based on this experimental multiscale analysis, this paper confirms that timbered masonry structures offer effective seismic resistance; moreover, such a comprehensive analysis helps enhance understanding of the seismic-resistant behavior of timber-framed structures with infill. This paper also aids ongoing development of a numerical tool intended to predict the seismic-resistant behavior of this type of structure.
RotorTM: A Flexible Simulator for Aerial Transportation and Manipulation

Low-cost autonomous micro aerial vehicles have great potential to help humans by simplifying and speeding up complex tasks, such as construction, package delivery, and search and rescue. These systems, which may consist of single or multiple vehicles, can be equipped with passive connection mechanisms, such as rigid links or cables for transportation and manipulation tasks. However, these systems are inherently complex. They are often underactuated and evolve in nonlinear manifold configuration spaces. In addition, the complexity escalates for systems with cable-suspended load due to the hybrid dynamics that vary with the cables' tension conditions. This article presents the first aerial transportation and manipulation simulator incorporating different payloads and passive connection mechanisms with full system dynamics, planning, and control algorithms. Furthermore, it includes a novel general model accounting for the transient hybrid dynamics for aerial systems with cable-suspended load to closely mimic real-world systems. The availability of a flexible and intuitive interface further contributes to its usability and versatility. Comparisons between simulations and real-world experiments with different vehicles' configurations show the fidelity of the simulator results with respect to real-world settings. The experiments also show the simulator's benefit for the rapid prototyping and transitioning of aerial transportation and manipulation systems to real-world deployment.
Efficient Risk-Averse Reinforcement Learning

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns. A risk measure often focuses on the worst returns out of the agent's experience. As a result, standard methods for risk-averse RL often ignore high-return strategies. We prove that under certain conditions this inevitably leads to a local-optimum barrier, and propose a soft risk mechanism to bypass it. We also devise a novel Cross Entropy module for risk sampling, which (1) preserves risk aversion despite the soft risk; (2) independently improves sample efficiency. By separating the risk aversion of the sampler and the optimizer, we can sample episodes with poor conditions, yet optimize with respect to successful strategies. We combine these two concepts in CeSoR - Cross-entropy Soft-Risk optimization algorithm - which can be applied on top of any risk-averse policy gradient (PG) method. We demonstrate improved risk aversion in maze navigation, autonomous driving, and resource allocation benchmarks, including in scenarios where standard risk-averse PG completely fails.
Improved maximin fair allocation of indivisible items to three agents

We consider the problem of approximate maximin share (MMS) allocation of indivisible items among three agents with additive valuation functions. For goods, we show that an $\frac{11}{12}$ - MMS allocation always exists, improving over the previously known bound of $\frac{8}{9}$ . Moreover, in our allocation, we can prespecify an agent that is to receive her full proportional share (PS); we also present examples showing that for such allocations the ratio of $\frac{11}{12}$ is best possible. For chores, we show that a $\frac{19}{18}$-MMS allocation always exists. Also in this case, we can prespecify an agent that is to receive no more than her PS, and we present examples showing that for such allocations the ratio of $\frac{19}{18}$ is best possible.
Linking pressure gradients with the stability of molecular clouds in galactic outflows

The jets launched by actively accreting black holes are capable of launching several of the massive (million or billion solar mass) molecular outflows observed in galaxies. These outflows could suppress or enhance star formation in galaxies. To investigate the stability of clouds capable to form stars in outflows, we modeled CO and HCO+ ALMA data of the galaxy IC5063, in which black-hole jets impact molecular clouds. Using a radiative transfer code that self-consistently performs astrochemical and thermal balance calculations based on the available gas heating sources, we found that mechanical heating and cosmic ray (CR) heating are fully capable of individually reproducing the data. In our best-fit model, CRs provide about 1/3rd of the dense gas heating at the radio lobes, emphasizing the role of this often neglected mechanism in heating the gas and potentially generating outflows. The gas temperature and density indicate that the jet passage leads to an increase of about 1 order of magnitude in the internal pressure Pi of molecular clouds (with Pi/k from 8*10^5 up to 7*10^6 K cm^-3), irrespective of the excitation mechanism. From the fluxes of [S II] and [N II] lines in VLT MUSE data, the external pressure Pe of molecular clouds increases in several regions enough to exceed Pi. This result leads us to conclude that we are observing the expansion of an ionized overpressurized cocoon that compresses molecular clouds and that could lead to their collapse. Some jet-impacted clouds, nonetheless, near pathways that the jet cleared have increased Pi and decreased Pe. They are likely to undergo evaporation of their outer layers. Part of the evaporated layers could mass load the outflow thanks to ram pressure from co-spatial ionized gas flows. The observed pressure changes thus suggest that both star formation enhancement and suppression could simultaneously occur.
Understanding and suppressing backscatter in optical resonators

Optical cavities have found widespread use in interfacing to quantum emitters. Concerns about backreﬂection and resulting loss, however, have largely prevented the placement of optics such as lenses or modulators within high-ﬁnesse cavities. In this work, we demonstrate a million-fold suppression of backreﬂections from lenses within a twisted optical cavity. We achieve this by quantitatively exploring backscatter in Fabry-Pérot resonators, separating the eﬀect into three physical sectors: polarization, mode envelope and spatial mode proﬁle. We describe the impact of each of these sectors, and demonstrate how to minimize backreﬂections within each. This culminates in measured eﬀective reﬂectivities below the part-per-billion level for the fundamental mode. Addition-ally, we show that beams carrying orbital angular momentum experience up to 10 4 times additional suppression, limited only by the density of states of other cavity modes. Applying these ideas to laser gyroscopes could strongly suppress lock-in, thereby improving sensitivity at low rotation rates.
Groups and Protocluster Candidates in the CLAUDS and HSC-SSP Joint Deep Surveys

Using the extended halo-based group finder developed by Yang et al., which is able to deal with galaxies via spectroscopic and photometric redshifts simultaneously, we construct galaxy group and candidate protocluster catalogs in a wide redshift range (0 < z < 6) from the joint CFHT Large Area U-band Deep Survey and Hyper Suprime-Cam Subaru Strategic Program deep data set. Based on a selection of 5,607,052 galaxies with i-band magnitude m i < 26 and a sky coverage of 34.41 deg2, we identify a total of 2,232,134 groups, of which 402,947 groups have at least three member galaxies. We have visually checked and discussed the general properties of these richest groups at redshift z > 2.0. By checking the galaxy number distributions within a 5–7 h −1Mpc projected separation and a redshift difference Δz ≤ 0.1 around those richest groups at redshift z > 2, we identify lists of 761, 343, and 43 protocluster candidates in the redshift bins 2 ≤ z < 3, 3 ≤ z < 4, and z ≥ 4, respectively. In general, these catalogs of galaxy groups and protocluster candidates will provide useful environmental information in probing galaxy evolution along cosmic time.
Effective submodularity of influence maximization on temporal networks

We study influence maximization on temporal networks. This is a special setting where the influence function is not submodular, and there is no optimality guarantee for solutions achieved via greedy optimization. We perform an exhaustive analysis on both real and synthetic networks. We show that the influence function of randomly sampled sets of seeds often violates the necessary conditions for submodularity. However, when sets of seeds are selected according to the greedy optimization strategy, the influence function behaves effectively as a submodular function. Specifically, violations of the necessary conditions for submodularity are never observed in real networks, and only rarely in synthetic ones. The direct comparison with exact solutions obtained via brute-force search indicates that the greedy strategy provides approximate solutions that are well within the optimality gap guaranteed for strictly submodular functions. Greedy optimization appears, therefore, to be an effective strategy for the maximization of influence on temporal networks.
Deep fusion of gray level co-occurrence matrices for lung nodule classification

Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.
The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization

In this paper, we revisit the smooth and strongly-convex-strongly-concave minimax optimization problem. Zhang et al. (2021) and Ibrahim et al. (2020) established the lower bound $\Omega\left(\sqrt{\kappa_x\kappa_y} \log \frac{1}{\epsilon}\right)$ on the number of gradient evaluations required to find an $\epsilon$-accurate solution, where $\kappa_x$ and $\kappa_y$ are condition numbers for the strong convexity and strong concavity assumptions. However, the existing state-of-the-art methods do not match this lower bound: algorithms of Lin et al. (2020) and Wang and Li (2020) have gradient evaluation complexity $\mathcal{O}\left( \sqrt{\kappa_x\kappa_y}\log^3\frac{1}{\epsilon}\right)$ and $\mathcal{O}\left( \sqrt{\kappa_x\kappa_y}\log^3 (\kappa_x\kappa_y)\log\frac{1}{\epsilon}\right)$, respectively. We fix this fundamental issue by providing the first algorithm with $\mathcal{O}\left(\sqrt{\kappa_x\kappa_y}\log\frac{1}{\epsilon}\right)$ gradient evaluation complexity. We design our algorithm in three steps: (i) we reformulate the original problem as a minimization problem via the pointwise conjugate function; (ii) we apply a specific variant of the proximal point algorithm to the reformulated problem; (iii) we compute the proximal operator inexactly using the optimal algorithm for operator norm reduction in monotone inclusions.
The Conflict Between Explainable and Accountable Decision-Making Algorithms

Decision-making algorithms are being used in important decisions, such as who should be enrolled in health care programs and be hired. Even though these systems are currently deployed in high-stakes scenarios, many of them cannot explain their decisions. This limitation has prompted the Explainable Artificial Intelligence (XAI) initiative, which aims to make algorithms explainable to comply with legal requirements, promote trust, and maintain accountability. This paper questions whether and to what extent explainability can help solve the responsibility issues posed by autonomous AI systems. We suggest that XAI systems that provide post-hoc explanations could be seen as blameworthy agents, obscuring the responsibility of developers in the decision-making process. Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i.e., patients), due to a misguided perception that they have control over explainable algorithms. This conflict between explainability and accountability can be exacerbated if designers choose to use algorithms and patients as moral and legal scapegoats. We conclude with a set of recommendations for how to approach this tension in the socio-technical process of algorithmic decision-making and a defense of hard regulation to prevent designers from escaping responsibility.
Differential Flatness Method Based on Pre-set Guidance and Control Subsystem Design for a Surface to Surface Flying Vehicle (TECHNICAL NOTE)

The purpose of this paper is to design a guidance and control system and evaluate the performance of a sample surface-to-surface flying object based on preset guidance with a new prospective. In this study, the main presented idea is usage of unique property of governor differential equations in order to design and develop a controlled system. Thereupon a set of system output variables have been examined by specific tests as candidate of flattened variables. It is proved that the dynamism of the studying system has a property of differential flatness. This property as a basement for observing all of the system dynamic variables could be a perfect option to remove lack of observability of nonlinear systems. According to the information gained in the procedure of flatness demonstrating, there was a similarity between the control command generating in feedback linearization and flat systems tests. This similarity led to the application of the flat systems technique for the mentioned control method. The guidance and control system suggested in this paper is able to follow a set of specific reference trajectories in order to target different ranges. This ability without recalculating controller gains could be done only by having the rate of rotate of flying object in middle phase of maneuver. To validate the proposed FBC for the studied problem, another usual control method has been investigated. For this purpose, the linear quadratic regulator as straight forward control method in optimal control field has been applied. This feature reveals full compatibility between controller block and reference trajectory generator block. doi: 10.5829/ije.2017. 30.06c. 12