Miracle-dz/UniFAR-FTUs huggingface dataset:
De Novo Drug Design with Joint Transformers

De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models. To address this, we propose Joint Transformer that combines a Transformer decoder, a Transformer encoder, and a predictor in a joint generative model with shared weights. We show that training the model with a penalized log-likelihood objective results in state-of-the-art performance in molecule generation, while decreasing the prediction error on newly sampled molecules, as compared to a fine-tuned decoder-only Transformer, by 42%. Finally, we propose a probabilistic black-box optimization algorithm that employs Joint Transformer to generate novel molecules with improved target properties, as compared to the training data, outperforming other SMILES-based optimization methods in de novo drug design.
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?

We present a large empirical investigation on the use of pre-trained visual representations (PVRs) for training downstream policies that execute real-world tasks. Our study spans five different PVRs, two different policy-learning paradigms (imitation and reinforcement learning), and three different robots for 5 distinct manipulation and indoor navigation tasks. From this effort, we can arrive at three insights: 1) the performance trends of PVRs in the simulation are generally indicative of their trends in the real world, 2) the use of PVRs enables a first-of-its-kind result with indoor ImageNav (zero-shot transfer to a held-out scene in the real world), and 3) the benefits from variations in PVRs, primarily data-augmentation and fine-tuning, also transfer to the real-world performance. See project website for additional details and visuals.
First constraints on Non-minimally coupled Natural and Coleman-Weinberg inflation in the light of massive neutrino self-interactions and Planck+BICEP/Keck

In this work, for the first time in literature, we compare the predictions of non-minimally coupled Natural and Coleman-Weinberg potentials in the $n_s-r$ plane against the constraints from the latest cosmological data in an extended $\Lambda$CDM model where we include non-standard self-interactions among massive neutrinos, mediated by a heavy scalar or vector boson. For the inflationary potentials, we consider two different formulations in gravity that are non-minimally coupled to the scalar field of the inflaton: \textit{Metric and Palatini. } We only consider the self-interaction to be present among $\tau$-neutrinos and only at moderate strengths. This is because strong interactions among $\tau$-neutrinos, or any strength self-interaction among electron- and muon-neutrinos, as well as any strength flavor-universal interactions, are strongly disfavoured from particle physics experiments. In terms of cosmological data, we use the latest public CMB datasets from Planck and BICEP/Keck collaborations, along with other data from CMB lensing, BAO, RSD, and SNe Ia luminosity distance measurements. We find that there are some situations where predictions from the inflationary models are ruled out at more than 2$\sigma$ by the minimal $\Lambda$CDM$+r$ model, but they are allowed in the self-interacting neutrino scenario.
MFOS: Model-Free & One-Shot Object Pose Estimation

Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scalability. Notably, recent attempts have been made to solve this issue, but they still require accurate 3D data of the object surface at both train and test time. In this paper, we introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. In contrast to existing state-of-the-art approaches, which rely on task-specific modules, our proposed model is entirely based on a transformer architecture, which can benefit from recently proposed 3D-geometry general pretraining. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark. Finally, extensive ablations allow us to determine good practices with this relatively new type of architecture in the field.
Baseline Nutritional Status and In-Hospital Step Count are Associated with Muscle Quantity, Quality, and Function: Results of an Exploratory Study

Abstract This exploratory study aimed to assess associations of baseline nutritional status and in-hospital step count with muscle quantity, quality, and function. Seventy-nine participants aged ≥70 years (mean age 79.1 years, 44.3% female) were recruited (elective colorectal surgery, emergency abdominal surgery, and general medical patients with infections). Baseline nutrition (Mini Nutritional Assessment) and in-hospital step count (Fitbit Inspire devices) were assessed. Ultrasound quadriceps, bioelectrical impedance analysis, and physical function were assessed at baseline and 7 (±2) days and 13 (±1) weeks post-admission/post-operatively. Baseline nutritional status was associated with baseline rectus femoris ultrasound echogenicity (normal: 58.5, at risk: 68.5, malnourished: 81.2; p = 0.025), bilateral anterior thigh thickness (normal: 5.07 cm, at risk: 4.03 cm, malnourished: 3.05 cm; p = 0.021), and skeletal muscle mass (Sergi equation) (normal: 21.6 kg, at risk: 18.2 kg, malnourished: 12.0 kg; p = 0.007). Step count was associated with baseline patient-reported physical function (<900 37.1, ≥900 44.5; p = 0.010). There was a significant interaction between nutrition, step count, and time for skeletal muscle mass (Janssen equation) (p = 0.022).
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency

Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards. Even though HER improves the sample efficiency of RL-based agents by learning from mistakes made in past experiences, it does not provide any guidance while exploring the environment. This leads to very large training times due to the volume of experience required to train an agent using this replay strategy. In this paper, we propose a method that uses primitive behaviours that have been previously learned to solve simple tasks in order to guide the agent toward more rewarding actions during exploration while learning other more complex tasks. This guidance, however, is not executed by a manually designed curriculum, but rather using a critic network to decide at each timestep whether or not to use the actions proposed by the previously-learned primitive policies. We evaluate our method by comparing its performance against HER and other more efficient variations of this algorithm in several block manipulation tasks. We demonstrate the agents can learn a successful policy faster when using our proposed method, both in terms of sample efficiency and computation time. Code is available at https://github.com/franroldans/qmp-her.
Electronic structure in a rare-earth based nodal-line semimetal candidate PrSbTe

Nodal line semimetals feature topologically protected band crossings between the bulk valence and conduction bands that extend along a finite dimension in the form of a line or a loop. While ZrSiS and similar materials have attracted extensive research as hosts for the nodal line semimetallic phase, an alternative avenue has emerged in the form of isostructural rare-earth (RE) based RESbTe materials. Such systems possess intriguing potentialities for harboring elements of magnetic ordering and electronic correlations owing to the presence of 4f electrons intrinsic to the RE elements. In this study, we have carried out angle resolved photoemission spectroscopy (ARPES) and thermodynamic measurements in conjunction with first principles computations on PrSbTe to elucidate its electronic structure and topological characteristics. Magnetic and thermal characterizations indicate the presence of well-localized 4f states with the absence of any discernible phase transition down to 2 K. The ARPES results reveal the presence of gapless Dirac crossings that correspond to a nodal-line along the XR direction in the three-dimensional Brillouin zone. Furthermore, Dirac crossing that makes up nodal line, which forms a diamond-shaped nodal plane centered at the center of the Brillouin zone is also identified within the experimental resolution. This study on the electronic structure of PrSbTe contributes to the understanding of the pivotal role played by spin-orbit coupling in the context of the RESbTe family of materials
Quantum Entanglement Phase Transitions and Computational Complexity: Insights from Ising Models

In this paper, we construct 2-dimensional bipartite cluster states and perform single-qubit measurements on the bulk qubits. We explore the entanglement scaling of the unmeasured 1-dimensional boundary state and show that under certain conditions, the boundary state can undergo a volume-law to an area-law entanglement transition driven by variations in the measurement angle. We bridge this boundary state entanglement transition and the measurement-induced phase transition in the non-unitary 1+1-dimensional circuit via the transfer matrix method. We also explore the application of this entanglement transition on the computational complexity problems. Specifically, we establish a relation between the boundary state entanglement transition and the sampling complexity of the bipartite $2$d cluster state, which is directly related to the computational complexity of the corresponding Ising partition function with complex parameters. By examining the boundary state entanglement scaling, we numerically identify the parameter regime for which the $2$d quantum state can be efficiently sampled, which indicates that the Ising partition function can be evaluated efficiently in such a region.
Beyond Labeling Oracles: What does it mean to steal ML models?

Model extraction attacks are designed to steal trained models with only query access, as is often provided through APIs that ML-as-a-Service providers offer. ML models are expensive to train, in part because data is hard to obtain, and a primary incentive for model extraction is to acquire a model while incurring less cost than training from scratch. Literature on model extraction commonly claims or presumes that the attacker is able to save on both data acquisition and labeling costs. We show that the attacker often does not. This is because current attacks implicitly rely on the adversary being able to sample from the victim model's data distribution. We thoroughly evaluate factors influencing the success of model extraction. We discover that prior knowledge of the attacker, i.e. access to in-distribution data, dominates other factors like the attack policy the adversary follows to choose which queries to make to the victim model API. Thus, an adversary looking to develop an equally capable model with a fixed budget has little practical incentive to perform model extraction, since for the attack to work they need to collect in-distribution data, saving only on the cost of labeling. With low labeling costs in the current market, the usefulness of such attacks is questionable. Ultimately, we demonstrate that the effect of prior knowledge needs to be explicitly decoupled from the attack policy. To this end, we propose a benchmark to evaluate attack policy directly.
Characterization of Acute Low Back Pain in a Community-Based Cohort

Acute low back pain (LBP) is a common experience, however, the associated pain severity, pain frequency, and characteristics of individuals with acute LBP in community settings have yet to be well understood. The purpose of this manuscript is to categorize and compare acute LBP groups for differences in the following characteristics: 1) sociodemographic, 2) general and physical health, and 3) psychological. This cross-sectional study used baseline data from 131 community-based participants with acute LBP (<4 weeks duration before screening and >30 pain-free days before acute LBP onset). Two LBP categorization definitions were used based on LBP frequency combined with either: 1) pain interference frequency (impact-based) or 2) pain intensity (intensity-based). Descriptive associations were calculated as prevalence ratios for categorical variables and Hedges g for continuous variables. Our analyses identified several large associations for impact-based and intensity-based categories for pain interference with activity and with enjoyment in life, global mental health, STarT Back Screening Tool risk category, general health, and Fear Avoidance Beliefs Questionnaire-Physical Activity subscale. Larger associations were found with social constructs (racially and ethnically minoritized, performance of social roles, and isolation) when using the intensity-based versus impact-based categorization. This study adds to the literature by providing standard ways to characterize community-based individuals experiencing acute LBP. The robust differences observed between these categorization approaches suggest they may be used to improve the early identification of factors potentially contributing to the development of chronic LBP.
3D multi-scale feature extraction and recalibration network for spinal structure and lesion segmentation

Background Automatic segmentation has emerged as a promising technique for the diagnosis of spinal conditions. Purpose To design and evaluate a deep convolution network for segmenting the intervertebral disc, spinal canal, facet joint, and herniated disk on magnetic resonance imaging (MRI) scans. Material and Methods MRI scans of 70 patients with disc herniation were gathered and manually annotated by radiologists. A novel deep neural network was developed, comprising 3D squeeze-and-excitation blocks and multi-scale feature extraction blocks for automated segmentation of spinal structure and lesion. To address the issue of class imbalance, a weighted cross-entropy loss was introduced for training. In addition, semi-supervision segmentation was accomplished to reduce annotation labor cost. Results The proposed model achieved 77.67% mean intersection over union, with 9.56% and 11.11% gains over typical V-Net and U-Net respectively, outperforming the other models in ablation experiments. In addition, the semi-supervision segmentation method was proven to work. Conclusion The 3D multi-scale feature extraction and recalibration network achieved an excellent segmentation performance of intervertebral disc, spinal canal, facet joint, and herniated disk, outperforming typical encoder-decoder networks.
Split and Merge: Aligning Position Biases in Large Language Model based Evaluators

Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems. However, these LLM-based evaluators exhibit position bias, or inconsistency, when used to evaluate candidate answers in pairwise comparisons, favoring either the first or second answer regardless of content. To address this limitation, we propose PORTIA, an alignment-based system designed to mimic human comparison strategies to calibrate position bias in a lightweight yet effective manner. Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs. We conducted extensive experiments with six diverse LLMs to evaluate 11,520 answer pairs. Our results show that PORTIA markedly enhances the consistency rates for all the models and comparison forms tested, achieving an average relative improvement of 47.46%. Remarkably, PORTIA enables less advanced GPT models to achieve 88% agreement with the state-of-the-art GPT-4 model at just 10% of the cost. Furthermore, it rectifies around 80% of the position bias instances within the GPT-4 model, elevating its consistency rate up to 98%. Subsequent human evaluations indicate that the PORTIA-enhanced GPT-3.5 model can even surpass the standalone GPT-4 in terms of alignment with human evaluators. These findings highlight PORTIA's ability to correct position bias, improve LLM consistency, and boost performance while keeping cost-efficiency. This represents a valuable step toward a more reliable and scalable use of LLMs for automated evaluations across diverse applications.
Grid-Forming Control Methods for Weakly Connected Offshore WPPs

Grid-forming control (GFC) has seen numerous technological advances in their control types, applications, and the multitude of services they provide. Some examples of the services they provide include black start, inertial frequency response, and islanded operation capabilities with the possibility of re-synchronization without the need of additional support from other devices such as storage. State of the art literature proposes a variety of GFCs which can provide single or multiple of these services. However, study of these different GFCs for weakly-connected offshore wind power plants (WPPs) based on time-domain simulation and focusing on the large signal disturbance is not well covered. This paper reviews some of the most researched grid-forming control methods applicable to offshore WPPs and provides a comparative investigation and discussion of their stability properties and applicability, especially when connected to a weak-grid. The paper also provides a discussion on the prerequisites and challenges surrounding the comparative study of different GFCs.
MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection

Deepfakes are synthetic media generated using deep generative algorithms and have posed a severe societal and political threat. Apart from facial manipulation and synthetic voice, recently, a novel kind of deepfakes has emerged with either audio or visual modalities manipulated. In this regard, a new generation of multimodal audio-visual deepfake detectors is being investigated to collectively focus on audio and visual data for multimodal manipulation detection. Existing multimodal (audio-visual) deepfake detectors are often based on the fusion of the audio and visual streams from the video. Existing studies suggest that these multimodal detectors often obtain equivalent performances with unimodal audio and visual deepfake detectors. We conjecture that the heterogeneous nature of the audio and visual signals creates distributional modality gaps and poses a sig-nificant challenge to effective fusion and efficient performance. In this paper, we tackle the problem at the representation level to aid the fusion of audio and visual streams for multimodal deepfake detection. Specifically, we propose the joint use of modality (audio and visual) invariant and specific representations. This ensures that the common patterns and patterns specific to each modality representing pristine or fake content are preserved and fused for multimodal deepfake manipulation detection. Our experimental results on FakeAVCeleb and KoDF audio-visual deepfake datasets suggest the enhanced accuracy of our proposed method over SOTA unimodal and multimodal audio-visual deepfake detectors by 17.8% and 18.4%, respectively. Thus, obtaining state-of-the-art performance.
Stress pattern in Portugal mainland and the adjacent Atlantic region, West Iberia

The Portuguese mainland territory is located close to the Azores-Gibraltar plate boundary, in a tectonic setting responsible for significant neotectonic and seismic activities. However, few data concerning the present regional lithospheric stress field were available, as testified by recently published maps of stress indicators for the Europe and Mediterranean regions. One of the authors already presented a synthesis on this subject [Cabral, 1993], where geological and geophysical stress indicators were considered. In this paper we introduce new information, mainly a considerable amount of borehole breakout data. The updated data set comprises 32 reliable stress indicators showing a mean azimuth of 145° (standard deviation 21°) for the maximum horizontal stress direction (SHmax). On the average, the geological data are rotated clockwise and the focal mechanism data deviated anticlockwise to that azimuth, while the borehole elongation results are consistent with the mean SHmax trend. These differences in stress trend suggest a regional progressive rotation of the SHmax direction from NNW-SSE to WNW-ESE since the upper Pliocene. To estimate stress trajectories, new and published stress indicators in the adjacent Atlantic area and northern Africa were also investigated, showing a very uniform NW-SE SHmax trend in west Iberia. A high level of horizontal compressive stress acting oblique to the western Portuguese continental margin is inferred and interpreted in view of a proposed regional geodynamical model, of activation of this passive margin, with the nucleation of a subduction zone in the Atlantic SW of Iberia, at the Gorringe submarine bank, which is propagating northward along the base of the continental slope, at the transition between thinned and normal continental crust.
Jury: A Comprehensive Evaluation Toolkit

Evaluation plays a critical role in deep learning as a fundamental block of any prediction-based system. However, the vast number of Natural Language Processing (NLP) tasks and the development of various metrics have led to challenges in evaluating different systems with different metrics. To address these challenges, we introduce jury, a toolkit that provides a unified evaluation framework with standardized structures for performing evaluation across different tasks and metrics. The objective of jury is to standardize and improve metric evaluation for all systems and aid the community in overcoming the challenges in evaluation. Since its open-source release, jury has reached a wide audience and is available at https://github.com/obss/jury.
Threat Modelling in Internet of Things (IoT) Environment Using Dynamic Attack Graphs

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, i.e., when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs. However, traditional attack graph approaches have been applied in static environments that do not continuously change such as the Enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. In this paper, a threat modelling approach is developed that copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths whilst allowing for system dynamics. Dynamic topology and attack graphs were developed that are able to cope with the changes in the IoT environment rapidly by maintaining their associated graphs. To motivate the work and illustrate the proposed approach, an example scenario based on healthcare systems is introduced. The proposed approach is implemented using a Graph Database Management Tool (GDBM)- Neo4j- which is a popular tool for mapping, visualising and querying the graphs of highly connected data, and is efficient in providing a rapid threat modelling mechanism, which makes it suitable for capturing security changes in the dynamic IoT environment.