Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Efficient Balanced Networks with Mixed-Signal Spike-Based Learning Circuits

Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and distributed information representation. It is for these benefits that it would be desirable to implement such networks in low-power neuromorphic processors. However, the degree of device mismatch in analog mixed-signal neuromorphic circuits renders the use of pre-trained EBNs challenging, if not impossible. To overcome this issue, we developed a novel local learning rule suitable for on-chip implementation that drives a randomly connected network of spiking neurons into a tightly balanced regime. Here we present the integrated circuits that implement this rule and demonstrate their expected behaviour in low-level circuit simulations. Our proposed method paves the way towards a system-level implementation of tightly balanced networks on analog mixed-signal neuromorphic hardware. Thanks to their coding properties and sparse activity, neuromorphic electronic EBNs will be ideally suited for extreme-edge computing applications that require low-latency, ultra-low power consumption and which cannot rely on cloud computing for data processing.
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability

To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around potential trade-offs is often informal and misconceptions abound. In this work, we aim to initiate a formal study of these trade-offs. A seemingly insurmountable roadblock is the lack of any agreed upon definition of interpretability. Instead, we propose a shift in perspective. Rather than attempt to define interpretability, we propose to model the \emph{act} of \emph{enforcing} interpretability. As a starting point, we focus on the setting of empirical risk minimization for binary classification, and view interpretability as a constraint placed on learning. That is, we assume we are given a subset of hypothesis that are deemed to be interpretable, possibly depending on the data distribution and other aspects of the context. We then model the act of enforcing interpretability as that of performing empirical risk minimization over the set of interpretable hypotheses. This model allows us to reason about the statistical implications of enforcing interpretability, using known results in statistical learning theory. Focusing on accuracy, we perform a case analysis, explaining why one may or may not observe a trade-off between accuracy and interpretability when the restriction to interpretable classifiers does or does not come at the cost of some excess statistical risk. We close with some worked examples and some open problems, which we hope will spur further theoretical development around the tradeoffs involved in interpretability.
On the long-term evolution of rotating radio transients

Investigation of the long-term evolution of rotating radio transients (RRATs) is important to understand the evolutionary connections between the isolated neutron star populations in a single picture. The X-ray luminosities of RRATs (except one source) are not known. In the fallback disc model, we have developed a method to estimate the dipole field strengths of RRATs without X-ray information. We have found that RRATs could have dipole field strengths, B0, at the poles ranging from ∼7 × 109 to ∼6 × 1011 G which fill the gap between the B0 ranges of central compact objects (CCOs) and dim isolated neutron stars (XDINs) estimated in the same model. In our model, most of RRATs are evolving at ages (∼2–6) × 105 yr, much smaller than their characteristic ages, such that, cooling luminosities of a large fraction of relatively nearby RRATs could be detected by the eROSITA all-sky survey. Many RRATs are located above the upper border of the pulsar death valley with the fields inferred from the dipole-torque formula, while they do not show strong, continuous radio pulses. The B0 values estimated in our model, place all RRATs either into the death valley or below the death line. We have tentatively proposed that RRATs could be the sources below their individual death points, and their short radio bursts could be ignited by the disc-field interaction occasionally enhancing the flux of open field lines through the magnetic poles. We have also discussed the evolutionary links between CCOs, RRATs, and XDINs.
Symmetry-protected sign problem and magic in quantum phases of matter

We introduce the concepts of a symmetry-protected sign problem and symmetry-protected magic to study the complexity of symmetry-protected topological (SPT) phases of matter. In particular, we say a state has a symmetry-protected sign problem or symmetry-protected magic, if finite-depth quantum circuits composed of symmetric gates are unable to transform the state into a non-negative real wave function or stabilizer state, respectively. We prove that states belonging to certain SPT phases have these properties, as a result of their anomalous symmetry action at a boundary. For example, we find that one-dimensional Z2×Z2 SPT states (e.g. cluster state) have a symmetry-protected sign problem, and two-dimensional Z2 SPT states (e.g. Levin-Gu state) have symmetry-protected magic. Furthermore, we comment on the relation between a symmetry-protected sign problem and the computational wire property of one-dimensional SPT states. In an appendix, we also introduce explicit decorated domain wall models of SPT phases, which may be of independent interest.
Production and Activity of Cristazarin in the Lichen-Forming Fungus Cladonia metacorallifera

Lichens are a natural source of bioactive compounds. Cladonia metacorallifera var. reagens KoLRI002260 is a rare lichen known to produce phenolic compounds, such as rhodocladonic, thamnolic, and didymic acids. However, these metabolites have not been detected in isolated mycobionts. We investigated the effects of six carbon sources on metabolite biosynthesis in the C. metacorallifera mycobiont. Red pigments appeared only in Lilly and Barnett’s media with fructose at 15 °C after 3 weeks of culture and decreased after 6 weeks. We purified these red pigments using preparative-scale high performance liquid chromatography and analyzed them via nuclear magnetic resonance. Results indicated that 1% fructose-induced cristazarin and 6-methylcristazarin production under light conditions. In total, 27 out of 30 putative polyketide synthase genes were differentially expressed after 3 weeks of culture, implying that these genes may be required for cristazarin production in C. metacorallifera. Moreover, the white collar genes Cmwc-1 and Cmwc-2 were highly upregulated at all times under light conditions, indicating a possible correlation between cristazarin production and gene expression. The cancer cell lines AGS, CT26, and B16F1 were sensitive to cristazarin, with IC50 values of 18.2, 26.1, and 30.9 μg/mL, respectively, which highlights the value of cristazarin. Overall, our results suggest that 1% fructose under light conditions is required for cristazarin production by C. metacorallifera mycobionts, and cristazarin could be a good bioactive compound.
γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

We introduce the $\gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. Replacing standard single-step models with $\gamma$-models leads to generalizations of the procedures central to model-based control, including the model rollout and model-based value estimation. The $\gamma$-model, trained with a generative reinterpretation of temporal difference learning, is a natural continuous analogue of the successor representation and a hybrid between model-free and model-based mechanisms. Like a value function, it contains information about the long-term future; like a standard predictive model, it is independent of task reward. We instantiate the $\gamma$-model as both a generative adversarial network and normalizing flow, discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors, and empirically investigate its utility for prediction and control.
Solitary Spectrally-Discrete Bound States in the Continuum in an Open System.

Bound states in the continuum (BICs) exist in a variety of physical systems where they appear as lossless propagating states surrounded by radiating modes. However, in the case of open systems, they coexist with continuous families of guided states, which may be modes or other BICs, located in different regions of the frequency-momentum parameter space. Here we report that waveguiding structures comprising anisotropic materials with two radiation channels where continuous families of modes are not possible whatsoever can support discrete BICs that exist for a single frequency and a single propagation direction. The results are isolated spectrally-discrete BIC states existing as lossless needles emerging from a sea of radiating leaky modes.
The item selection problem for user cold-start recommendation

When a new user just signs up on a website, we usually have no information about him/her, i.e. no interaction with items, no user profile and no social links with other users. Under such circumstances, we still expect our recommender systems could attract the users at the first time so that the users decide to stay on the website and become active users. This problem falls into new user cold-start category and it is crucial to the development and even survival of a company. Existing works on user cold-start recommendation either require additional user efforts, e.g. setting up an interview process, or make use of side information [10] such as user demographics, locations, social relations, etc. However, users may not be willing to take the interview and side information on cold-start users is usually not available. Therefore, we consider a pure cold-start scenario where neither interaction nor side information is available and no user effort is required. Studying this setting is also important for the initialization of other cold-start solutions, such as initializing the first few questions of an interview.
Improving Reinforcement Learning for Neural Relation Extraction with Hierarchical Memory Extractor

Distant supervision relation extraction (DSRE) is an efficient method to extract semantic relations on a large-scale heuristic labeling corpus. However, it usually brings in a massive noisy data. In order to alleviate this problem, many recent approaches adopt reinforcement learning (RL), which aims to select correct data autonomously before relation classification. Although these RL methods outperform conventional multi-instance learning-based methods, there are still two neglected problems: 1) the existing RL methods ignore the feedback of noisy data, 2) the reduction of training corpus exacerbates long-tail problem. In this paper, we propose a novel framework to solve the two problems mentioned above. Firstly, we design a novel reward function to obtain feedback from both correct and noisy data. In addition, we use implicit relations information to improve RL. Secondly, we propose the hierarchical memory extractor (HME), which utilizes the gating mechanism to share the semantics from correlative instances between data-rich and data-poor classes. Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing. Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baseline methods.
Anti-perturbation of Online Social Networks by Graph Label Transition

Numerous popular online social networks (OSN) would classify users into different categories and recommend users to each other with similar interests. A small number of users, so-called perturbators, may perform some types of behaviors, which significantly disturb such an OSN classifier. Manual annotation by OSN administrators is one kind of potential solutions. However, the manual annotation unavoidably brings into noise. Besides, such perturbators are not Sybil users, and therefore their accounts cannot be frozen. To improve the robustness of such an OSN classifier, we generalize this issue as the defense of Graph Convolutional Networks (GCNs) on the node classification task. Most existing defenses on this task can be divided into the adversarial-based method and the detection-based method. The adversarial-based method improves the robustness of GCNs by training with adversarial samples. However, in our case, the perturbators are hard to be distinguished by OSN administrators and thus we cannot use adversarial samples in the training phase. By contrast, the detection-based method aims at detecting the attacker nodes or edges and alleviates the negative impact by removing them. In our scenario, nevertheless, the perturbators are not the attacker and thus cannot be eliminated. Both methods could not solve the aforementioned problems. To address these issues, we propose a novel graph label transition model, named GraphLT, to improve the robustness of the OSN classifier by transiting the node latent representation based on dynamic conditional label transition. Extensive experiments demonstrate that GraphLT can not only considerably enhance the performance of the node classifier in a clean environment but also successfully remedy the classifier with superior performance over competing methods on seven benchmark datasets after graph perturbation.
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models

We consider the sequential optimization of an unknown function from noisy feedback using Gaussian process modeling. A prevailing approach to this problem involves choosing query points based on finding the maximum of an upper confidence bound (UCB) score over the entire domain of the function. Due to the multi-modal nature of the UCB, this maximization can only be approximated, usually using an increasingly fine sequence of discretizations of the entire domain, making such methods computationally prohibitive. We propose a general approach that reduces the computational complexity of this class of algorithms by a factor of $O(T^{2d-1})$ (where $T$ is the time horizon and $d$ the dimension of the function domain), while preserving the same regret order. The significant reduction in computational complexity results from two key features of the proposed approach: (i) a tree-based localized search strategy rooted in the methodology of domain shrinking to achieve increasing accuracy with a constant-size discretization; (ii) a localized optimization with the objective relaxed from a global maximizer to any point with value exceeding a given threshold, where the threshold is updated iteratively to approach the maximum as the search deepens. More succinctly, the proposed optimization strategy is a sequence of localized searches in the domain of the function guided by an iterative search in the range of the function to approach the maximum.
An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes Mellitus

Diabetes is a massive global problem, with growth especially rapid in developing regions, which can lead to several damaging complications. Among the most impactful of these are diabetic retinopathy, the leading cause of blindness among working class adults, and cardiovascular disease, the leading cause of death worldwide. However, diagnosis is often too late to prevent irreversible damage caused by these linked conditions. This study describes the development of an integrated test, automated and not requiring laboratory blood analysis, for screening of these conditions. First, a random forest model was developed by retrospectively analyzing the influence of various risk factors (obtained quickly and non- invasively) on cardiovascular risk. Next, a deep-learning model was developed for prediction of diabetic retinopathy from retinal fundus images by a modified and re-trained InceptionV3 image classification model. The input was simplified by automatically segmenting the blood vessels in the retinal image. The technique of transfer learning enables the model to capitalize on existing infrastructure on the target device, meaning more versatile deployment, especially helpful in low-resource settings. The models were integrated into a smartphone-based device, combined with an inexpensive 3D-printed retinal imaging attachment. Accuracy scores, as well as the receiver operating characteristic curve, the learning curve, and other gauges, were promising. This test is much cheaper and faster, enabling continuous monitoring for two damaging complications of diabetes. It has the potential to replace the manual methods of diagnosing both diabetic retinopathy and cardiovascular risk, which are time consuming and costly processes only done by medical professionals away from the point of care, and to prevent irreversible blindness and heart-related complications through faster, cheaper, and safer monitoring of diabetic complications. As well, tracking of cardiovascular and ocular complications of diabetes can enable improved detection of other diabetic complications, leading to earlier and more efficient treatment on a global scale.
The DigitalTwin from an Artificial Intelligence Perspective

Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed, i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.
A controlled clinical effectiveness trial of multimodal cognitive rehabilitation on episodic memory functioning in older adults with traumatic brain injury (TBI)

Objectives. This study aimed to evaluate the impact of a multimodal cognitive intervention, the Cognitive Enrichment Program (CEP), on episodic memory in traumatic brain injured (TBI) older adults, as compared to an active control group that received usual care in the form of holistic rehabilitation. Methods. The CEP's Memory module consisted in memory strategies to promote encoding. Effectiveness was evaluated by psychometric tests (Face-name association, Word list recall, Text memory), while generalization was measured through self-reported questionnaires about daily memory functioning (Self-Evaluation Memory Questionnaire) and psychological well-being (Psychological General Well-Being Index). Measures were obtained before and after intervention, and six months later. Results. Both groups showed improvement on most measures, but the experimental group showed greater statistically significant improvement. ANCOVA mixed model repeated measures analysis showed a strong group-by-time interaction for the Face-name association test, with a large effect size. A significant group-by-time interaction was obtained on three generalization self-report measures, including increased memorization of the content of Conversations, reduced Slips of attention, and increased memory of Political & social Events, with moderate to large effect sizes. Clinically significant improvements were found for Psychological well-being in the experimental group, where 50% of participants improved to the well-being category and remained stable six months later (9/17; 53%). Also, the number of experimental participants showing severe distress before CEP training (9) was reduced after intervention (5) and remained relatively stable at 6 months. Conclusions. The CEP is a promising cognitive rehabilitation program that showed high satisfaction in participants and that can not only improve episodic memory in terms of psychometric scores, but also in daily life situations, as well as enhance psychological well-being in older individuals having sustained a TBI.
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills

Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvement of such primitives in the reinforcement learning (RL) setting, especially for learning with physical robots. In this paper we propose a novel view on handling the demonstrated trajectories for acquiring low-dimensional, non-linear latent dynamics, using mixtures of probabilistic principal component analyzers (MPPCA) on the movements’ parameter space. Moreover, we introduce a new contextual off-policy RL algorithm, named LAtent-Movements Policy Optimization (LAMPO). LAMPO can provide gradient estimates from previous experience using self-normalized importance sampling, hence, making full use of samples collected in previous learning iterations. These advantages combined provide a complete framework for sample-efficient off-policy optimization of movement primitives for robot learning of high-dimensional manipulation skills. Our experimental results conducted both in simulation and on a real robot show that LAMPO provides sample-efficient policies against common approaches in literature. Code available at https://github.com/SamuelePolimi/lampo.
Predict and Use Latent Patterns for Short-Text Conversation

Many neural network models nowadays have achieved promising performances in Chit-chat settings. The majority of them rely on an encoder for understanding the post and a decoder for generating the response. Without given assigned semantics, the models lack the fine-grained control over responses as the semantic mapping between posts and responses is hidden on the fly within the end-to-end manners. Some previous works utilize sampled latent words as a controllable semantic form to drive the generated response around the work, but few works attempt to use more complex semantic forms to guide the generation. In this paper, we propose to use more detailed semantic forms, including latent responses and part-of-speech sequences sampled from the corresponding distributions, as the controllable semantics to guide the generation. Our experimental results show that the richer semantics are not only able to provide informative and diverse responses, but also increase the overall performance of response quality, including fluency and coherence.
A GPU-accelerated adaptive FSAI preconditioner for massively parallel simulations

The solution of linear systems of equations is a central task in a number of scientific and engineering applications. In many cases the solution of linear systems may take most of the simulation time thus representing a major bottleneck in the further development of scientific and technical software. For large scale simulations, nowadays accounting for several millions or even billions of unknowns, it is quite common to resort to preconditioned iterative solvers for exploiting their low memory requirements and, at least potential, parallelism. Approximate inverses have been shown to be robust and effective preconditioners in various contexts. In this work, we show how adaptive Factored Sparse Approximate Inverse (aFSAI), characterized by a very high degree of parallelism, can be successfully implemented on a distributed memory computer equipped with GPU accelerators. Taking advantage of GPUs in adaptive FSAI set-up is not a trivial task, nevertheless we show through an extensive numerical experimentation how the proposed approach outperforms more traditional preconditioners and results in a close-to-ideal behavior in challenging linear algebra problems.