Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuromodulators in food ingredients: insights from network pharmacological evaluation of Ayurvedic herbs

The global burden of neurological diseases, the second leading cause of death after heart diseases constitutes one of the major challenges of modern medicine. Ayurveda, the traditional Indian medicinal systemenrooted in the Vedic literature and considered as a schema for the holistic management of health, characterizes various neurological diseases disorders (NDDs) and prescribes several herbs, formulations and bio-cleansing regimes for their care and cure. In this work, we examined neuro-phytoregulatory potential of 34,472 phytochemicals among 3,038 herbs (including their varieties) mentioned in Ayurveda using network pharmacology approach and found that 45% of these Ayurvedic phytochemicals (APCs) have regulatory associations with 1,643 approved protein targets. Metabolite interconversion enzymes and protein modifying enzymes were found to be the major target classes of APCs against NDDs. The study further suggests that the actions of Ayurvedic herbs in managing NDDs were majorly via regulating signalling processes, like, G-protein signaling, acetylcholine signaling, chemokine signaling pathway and GnRH signaling. A high confidence network specific to 219 pharmaceutically relevant neuro-phytoregulators (NPRs) from 1,197 Ayurvedic herbs against 102 approved protein-targets involved in NDDs was developed and analyzed for gaining mechanistic insights. The key protein targets of NPRs to elicit their neuro-regulatory effect were highlighted as CYP and TRPA, while estradiol and melatonin were identified as the NPRs with high multi-targeting ability. 32 herbs enriched in NPRs were identified that include some of the well-known Ayurvedic neurological recommendations, like, Papaver somniferum, Glycyrrhiza glabra, Citrus aurantium, Cannabis sativa etc. Herbs enriched in NPRs may be used as a chemical source library for drug-discovery agaisnt NDDs from systems medicine perspectives.
The Near-stellar Environment of Class 0 Protostars: A First Look with Near-infrared Spectroscopy

We present near-infrared K-band spectra for a sample of seven Class 0 protostars in the Perseus and Orion star-forming regions. We detect Brγ, CO overtone, and H2 emission, features that probe the near-circumstellar environment of the protostar and reveal evidence of magnetospheric accretion, a hot inner disk atmosphere, and outflows, respectively. Comparing the properties of these features with those of Class I sources from the literature, we find that their Brγ emission and CO emission are generally consistent in strength and velocity width. The Brγ line profiles are broad and centrally peaked, with FWHMs of ∼200 km s−1 and wings extending to ∼300 km s−1. The line ratios of our H2 emission features, which are spatially extended for some sources, are consistent with shock excitation and indicate the presence of strong jets or a disk wind. Within our small sample, the frequency of CO band emission (∼67%) is high relative to that of Class I samples (∼15%), indicating that Class 0 sources have high inner disk accretion rates, similar to those of the most actively accreting Class I sources. Collectively, our results suggest that Class 0 sources have similar accretion mechanisms to the more evolved classes, with strong organized stellar magnetic fields established at the earliest observable stage of evolution.
A New Constructive Heuristic driven by Machine Learning for the Traveling Salesman Problem

Recent systems applying Machine Learning (ML) to solve the Traveling Salesman Problem (TSP) exhibit issues when they try to scale up to real case scenarios with several hundred vertices. The use of Candidate Lists (CLs) has been brought up to cope with the issues. A CL is defined as a subset of all the edges linked to a given vertex such that it contains mainly edges that are believed to be found in the optimal tour. The initialization procedure that identifies a CL for each vertex in the TSP aids the solver by restricting the search space during solution creation. It results in a reduction of the computational burden as well, which is highly recommended when solving large TSPs. So far, ML was engaged to create CLs and values on the elements of these CLs by expressing ML preferences at solution insertion. Although promising, these systems do not restrict what the ML learns and does to create solutions, bringing with them some generalization issues. Therefore, motivated by exploratory and statistical studies of the CL behavior in multiple TSP solutions, in this work, we rethink the usage of ML by purposely employing this system just on a task that avoids well-known ML weaknesses, such as training in presence of frequent outliers and the detection of under-represented events. The task is to confirm inclusion in a solution just for edges that are most likely optimal. The CLs of the edge considered for inclusion are employed as an input of the neural network, and the ML is in charge of distinguishing when such edge is in the optimal solution from when it is not. The proposed approach enables a reasonable generalization and unveils an efficient balance between ML and optimization techniques. Our ML-Constructive heuristic is trained on small instances. Then, it is able to produce solutions—without losing quality—for large problems as well. We compare our method against classic constructive heuristics, showing that the new approach performs well for TSPLIB instances up to 1748 cities. Although ML-Constructive exhibits an expensive constant computation time due to training, we proved that the computational complexity in the worst-case scenario—for the solution construction after training—is O(n2logn2), n being the number of vertices in the TSP instance.
Deep Relational Metric Learning

This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval. Most existing deep metric learning methods learn an embedding space with a general objective of increasing interclass distances and decreasing intraclass distances. However, the conventional losses of metric learning usually suppress intraclass variations which might be helpful to identify samples of unseen classes. To address this problem, we propose to adaptively learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. We further employ a relational module to capture the correlations among each feature in the ensemble and construct a graph to represent an image. We then perform relational inference on the graph to integrate the ensemble and obtain a relation-aware embedding to measure the similarities. Extensive experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.1
AMPPERE: A Universal Abstract Machine for Privacy-Preserving Entity Resolution Evaluation

Entity resolution is the task of identifying records in different datasets that refer to the same entity in the real world. In sensitive domains (e.g. financial accounts, hospital health records), entity resolution must meet privacy requirements to avoid revealing sensitive information such as personal identifiable information to untrusted parties. Existing solutions are either too algorithmically-specific or come with an implicit trade-off between accuracy of the computation, privacy, and run-time efficiency. We propose AMMPERE, an abstract computation model for performing universal privacy-preserving entity resolution. AMMPERE offers abstractions that encapsulate multiple algorithmic and platform-agnostic approaches using variants of Jaccard similarity to perform private data matching and entity resolution. Specifically, we show that two parties can perform entity resolution over their data, without leaking sensitive information. We rigorously compare and analyze the feasibility, performance overhead and privacy-preserving properties of these approaches on the Sharemind multi-party computation (MPC) platform as well as on PALISADE, a lattice-based homomorphic encryption library. The AMMPERE system demonstrates the efficacy of privacy-preserving entity resolution for real-world data while providing a precise characterization of the induced cost of preventing information leakage.
Effective Streaming Low-Tubal-Rank Tensor Approximation via Frequent Directions

Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multidimensional data. However, finding such an accurate approximation is challenging in the streaming setting, due to the limited computational resources. To alleviate this issue, this article extends a popular matrix sketching technique, namely, frequent directions (FDs), for constructing an efficient and accurate low-tubal-rank tensor approximation from streaming data based on the tensor singular value decomposition (t-SVD). Specifically, the new algorithm allows the tensor data to be observed slice by slice but only needs to maintain and incrementally update a much smaller sketch, which could capture the principal information of the original tensor. The rigorous theoretical analysis shows that the approximation error of the new algorithm can be arbitrarily small when the sketch size grows linearly. Extensive experimental results on both synthetic and real multidimensional data further reveal the superiority of the proposed algorithm compared with other sketching algorithms for getting low-tubal-rank approximation, in terms of both efficiency and accuracy.
On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing

Accelerating deep neural network (DNN) inference on resource-limited devices is one of the most important barriers to ensuring a wider and more inclusive adoption. To alleviate this, DNN binary quantization for faster convolution and memory savings is one of the most promising strategies despite its serious drop in accuracy. The present paper therefore proposes a novel binary quantization function based on quantized compressed sensing (QCS). Theoretical arguments conjecture that our proposal preserves the practical benefits of standard methods, while reducing the quantization error and the resulting drop in accuracy.
Non-Minimal Systems with Switching Topology: Dynamics and Controls

This paper presents a non-minimal order dynamics model for many analysis, simulation, and control problems of constrained mechanical systems with switching topology by making use of linear projection operator. The distinct features of this model describing dynamics of the dependent coordinates are: i) The mass matrix M̄ (q) is always positive definite even at singular configurations; ii) matrix ̇̄ M − 2C̄ is skew symmetric, where all nonlinear terms are lumped into vector C̄(q, q̇)q̇ after elimination of constraint forces. Eigenvalue analysis shows that the condition number of the constraint mass matrix can be minimized upon adequate selection of a scalar parameter called “virtual mass” thereby reducing the sensitivity to round-off errors in numerical computation. It follows by derivation of two oblique projection matrices for computation of constraint forces and actuation forces. It is shown that projection-based model allows feedback control of dependent coordinates which, unlike reduced-order dependent coordinates, uniquely define spatial configuration of constrained systems.
Analyzing the Granularity and Cost of Annotation in Clinical Sequence Labeling

Well-annotated datasets, as shown in recent top studies, are becoming more important for researchers than ever before in supervised machine learning (ML). However, the dataset annotation process and its related human labor costs remain overlooked. In this work, we analyze the relationship between the annotation granularity and ML performance in sequence labeling, using clinical records from nursing shift-change handover. We first study a model derived from textual language features alone, without additional information based on nursing knowledge. We find that this sequence tagger performs well in most categories under this granularity. Then, we further include the additional manual annotations by a nurse, and find the sequence tagging performance remaining nearly the same. Finally, we give a guideline and reference to the community arguing it is not necessary and even not recommended to annotate in detailed granularity because of a low Return on Investment. Therefore we recommend emphasizing other features, like textual knowledge, for researchers and practitioners as a cost-effective source for increasing the sequence labeling performance.
Gender Differences in the Cost of Corrections in Group Work

Corrections among colleagues are an integral part of group work, but people may take corrections as personal criticism, especially corrections by women. I study whether people dislike collaborating with someone who corrects them and more so when that person is a woman. People, including those with high productivity, are less willing to collaborate with a person who has corrected them even if the correction improves group performance. Yet, people respond to corrections by women as negatively as by men. These findings suggest that although women do not face a higher hurdle, correcting colleagues is costly and reduces group efficiency. JEL codes: J16, M54, D91, C92
Learning Causal Models of Autonomous Agents using Interventions

One of the several obstacles in the widespread use of AI systems is the lack of requirements of interpretability that can enable a layperson to ensure the safe and reliable behavior of such systems. We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system in stationary, fully observable, and deterministic settings. We also introduce dynamic causal decision networks (DCDNs) that capture the causal structure of STRIPS-like domains. A comparative analysis of different classes of queries is also presented in terms of the computational requirements needed to answer them and the efforts required to evaluate their responses to learn the correct model.
Analysis of Chronic Pain Experiences Based on Online Reports: the RRCP Dataset

Chronic pain is recognized as a major health problem, with impacts at the economic, social, and individual levels. Being a private and subjective experience, dependent on a complex cognitive process involving the subject’s past experiences, sociocultural embeddedness, as well as emotional and psychological loads, it is impossible to externally and impartially experience, describe, and interpret chronic pain as a purely noxious stimulus that would directly point to a causal agent and facilitate its mitigation. Verbal communication is, thus, key to convey relevant information to health professionals that would otherwise not be accessible to external entities. Specifically, what a patient suffering of chronic pain describes from the experience and how this information is disclosed reveals intrinsic qualities about the patient and the experience of pain itself. We present the Reddit Reports of Chronic Pain (RRCP) dataset, which comprises social media textual descriptions and discussion of various forms of chronic pain experiences, as reported from the perspective of different base pathologies. For each pathology, we identify the main concerns emergent of its consequent experience of chronic pain, as represented by the subset of documents explicitly related to it. This is obtained via document clustering in the latent space. By means of cosine similarity, we determine which concerns of different pathologies are core to all experiences of pain, and which are exclusive to certain forms. Finally, we argue that our unsupervised semantic analysis of descriptions of chronic pain echoes clinical research on how different pathologies manifest in terms of the chronic pain experience.
Reliable Spread Spectrum Communication Systems by Using Low-Density Parity-Check Codes

A joint scheme for the channel coding and spectrum spreading communication system is proposed in this paper. The Bit Error Rate (BER) performance of the joint proposed scheme is evaluated and compared with the case of channel coding scheme without spectrum spreading. We employ a single Low-Density Parity Check (LDPC) code for the both channel coding and direct sequence spectrum spreading. We show that the BER performance is significantly improved while the complexity does not increase too. The philosophy behind this achievement is that the spectrum spreading techniques provide the ability of channel noise protection for the communication systems by themselves. Keywords—LDPC Codes; Direct Sequence; Spread Spectrum; SPA; Channel Coding; BER.
Effective and Privacy preserving Tabular Data Synthesizing

While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN). In this thesis, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types with complex distributions. CTAB-GAN is extensively evaluated with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%. Additionally, to ensure greater security for training tabular GANs against malicious privacy attacks, differential privacy (DP) is studied and used to train CTAB-GAN with strict privacy guarantees. DP-CTAB-GAN is rigorously evaluated using state-of-the-art DP-tabular GANs in terms of data utility and privacy robustness against membership and attribute inference attacks. Our results on three datasets indicate that strict theoretical differential privacy guarantees come only after severely affecting data utility. However, it is shown empirically that these guarantees help provide a stronger defence against privacy attacks. Overall, it is found that DP-CTABGAN is capable of being robust to privacy attacks while maintaining the highest data utility as compared to prior work, by up to 18% in terms of the average precision score. Aditya Kunar Delft, August 2021
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

In-hand manipulation of objects is an important capability to enable robots to carry-out tasks which demand high levels of dexterity. This work presents a robot systems approach to learning dexterous manipulation tasks involving moving objects to arbitrary 6-DoF poses. We show empirical benefits, both in simulation and sim - to- real transfer, of using keypoint-based representations for object pose in policy observations and reward calculation to train a model-free reinforcement learning agent. By utilizing domain randomization strategies and large-scale training, we achieve a high success rate of 83 % on a real TriFinger system, with a single policy able to perform grasping, ungrasping, and finger gaiting in order to achieve arbitrary poses within the workspace. We demonstrate that our policy can generalise to unseen objects, and success rates can be further improved through finetuning. With the aim of assisting further research in learning in-hand manipulation, we provide a detailed exposition of our system and make the codebase of our system available, along with checkpoints trained on billions of steps of experience, at https://s2r2-ig.github. io
Testable Designs of Toffoli Fredkin Reversible Circuits

Loss of every bit in traditional logic circuits involves dissipation of power in the form of heat that evolve to the environment. Reversible logic is one of the alternatives that have capabilities to mitigate this dissipation by preventing the loss of bits. It also have the potential to broaden the horizon of futuristic reckon with its applications to quantum computation. Application of testing strategies to the logic circuits is a necessity that guarantees their true functioning where the researchers are at par with solutions for the upcoming challenges and agreements for reversible logic circuits. Novel methods of designing Toffoli, Fredkin and mixed Toffoli-Fredkin gates based reversible circuits for testability are put fourth in this article. The proposed designs are independent of the implementation techniques and can be brought into real hardware devices after obtaining a stable fabrication environment. The experimentation for the proposed models are performed on RCViewer and RevKit tools to verify the functionality and computation of cost metrics. Fault simulations are carried out using C++ and Java to calculate fault coverage in respective methodologies. The results confirmed that all the presented work outperforms existing state-of-art approaches.
Reliable firmware updates for the information-centric internet of things

Security in the Internet of Things (IoT) requires ways to regularly update firmware in the field. These demands ever increase with new, agile concepts such as security as code and should be considered a regular operation. Hosting massive firmware roll-outs present a crucial challenge for the constrained wireless environment. In this paper, we explore how information-centric networking can ease reliable firmware updates. We start from the recent standards developed by the IETF SUIT working group and contribute a system that allows for a timely discovery of new firmware versions by using cryptographically protected manifest files. Our design enables a cascading firmware roll-out from a gateway towards leaf nodes in a low-power multi-hop network. While a chunking mechanism prepares firmware images for typically low-sized maximum transmission units (MTUs), an early Denial-of-Service (DoS) detection prevents the distribution of tampered or malformed chunks. In experimental evaluations on a real-world IoT testbed, we demonstrate feasible strategies with adaptive bandwidth consumption and a high resilience to connectivity loss when replicating firmware images into the IoT edge.
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning

Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc. In order to aid the training process, existing methods either use a hard mining strategy to extract the most informative samples or seek to generate hard synthetics using an additional network. Such approaches face different challenges and can lead to biased embeddings in the former case, and (i) harder optimization (ii) slower training speed (iii) higher model complexity in the latter case. In order to overcome these challenges, we propose a novel approach that looks for optimal hard negatives (LoOp) in the embedding space, taking full advantage of each tuple by calculating the minimum distance between a pair of positives and a pair of negatives. Unlike mining-based methods, our approach considers the entire space between pairs of embeddings to calculate the optimal hard negatives. Extensive experiments combining our approach and representative metric learning losses reveal a significant boost in performance on three benchmark datasets1.