Miracle-dz/UniFAR-FTUs huggingface dataset:
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning

Offline safe RL is of great practical relevance for deploying agents in real-world applications. However, acquiring constraint-satisfying policies from the fixed dataset is non-trivial for conventional approaches. Even worse, the learned constraints are stationary and may become invalid when the online safety requirement changes. In this paper, we present a novel offline safe RL approach referred to as SaFormer, which tackles the above issues via conditional sequence modeling. In contrast to existing sequence models, we propose cost-related tokens to restrict the action space and a posterior safety verification to enforce the constraint explicitly. Specifically, SaFormer performs a two-stage auto-regression conditioned by the maximum remaining cost to generate feasible candidates. It then filters out unsafe attempts and executes the optimal action with the highest expected return. Extensive experiments demonstrate the efficacy of SaFormer featuring (1) competitive returns with tightened constraint satisfaction; (2) adaptability to the in-range cost values of the offline data without retraining; (3) generalizability for constraints beyond the current dataset.
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.
Development of Fractional Trigonometry and an Application of Fractional Calculus to Pharmacokinetic Model

This Thesis is brought to you for free and open access by TopSCHOLAR®. It has been accepted for inclusion in Masters Theses & Specialist Projects by an authorized administrator of TopSCHOLAR®. For more information, please contact connie.foster@wku.edu. Recommended Citation Almusharrf, Amera, "Development of Fractional Trigonometry and an Application of Fractional Calculus to Pharmacokinetic Model" (2011). Masters Theses & Specialist Projects. Paper 1048. http://digitalcommons.wku.edu/theses/1048 brought to you by CORE View metadata, citation and similar papers at core.ac. uk
Exploring Attention Map Reuse for Efficient Transformer Neural Networks

Transformer-based deep neural networks have achieved great success in various sequence applications due to their powerful ability to model long-range dependency. The key module of Transformer is self-attention (SA) which extracts features from the entire sequence regardless of the distance between positions. Although SA helps Transformer performs particularly well on long-range tasks, SA requires quadratic computation and memory complexity with the input sequence length. Recently, attention map reuse, which groups multiple SA layers to share one attention map, has been proposed and achieved significant speedup for speech recognition models. In this paper, we provide a comprehensive study on attention map reuse focusing on its ability to accelerate inference. We compare the method with other SA compression techniques and conduct a breakdown analysis of its advantages for a long sequence. We demonstrate the effectiveness of attention map reuse by measuring the latency on both CPU and GPU platforms.
On the summability of Random Fourier--Jacobi Series

This article is a study on the summability of random Fourier–Jacobi series of some functions in diﬀerent spaces. We consider the random series P ∞ n =0 a n A n ( ω ) p ( γ,δ ) n ( y ) , where p ( γ,δ ) n ( y ) , γ,δ > − 1 are orthonormal Jacobi polynomials, the scalars a n are Fourier–Jacobi coeﬃcients of a function f and the random variables A n ( ω ) are Fourier–Jacobi coeﬃcients of the symmetric stable process X ( t,ω ) of index α ∈ [1 , 2] . It is established that the random Fourier–Jacobi series is Θ–summable in probability, if a n are the Fourier–Jacobi coeﬃcients of function f in the space C ( η,τ ) [ − 1 , 1] . The Ces´aro ( C,φ ) ,φ ≥ 1 summability of random Fourier–Jacobi series is shown, for the symmetric stable process X ( t,ω ) of index α ∈ [1 , 2] under diﬀerent conditions on the parameters γ,δ,η and τ. The other cases of summability, such as Riesz, Rogosinski, etc., are also discussed. Further, the N¨orlund summability, generalized N¨orlund summability, and lower triangular summability of random Fourier–Jacobi series are proved if a n are the Fourier–Jacobi coeﬃcients of a function f ∈ L 1 , ( γ,δ ) [ − 1 , 1] , and A n ( ω ) are associated with the symmetric stable process X ( t,ω ) of index one. It is observed that the conditions on the parameters γ,δ diﬀer from that of the conditions on γ,δ for the Fourier–Jacobi series of functions f in L 1 , ( γ,δ ) [ − 1 , 1] . 2020 MSC Classiﬁcation -: 60G99, 40G15.
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts

Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the ProtST framework to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM's original representation power. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation.
On the equivariant main conjecture for imaginary quadratic fields

Abstract In this paper we first prove the main conjecture for imaginary quadratic fields for all prime numbers p, improving slightly earlier results by Rubin. From this we deduce the equivariant main conjecture in the case that a certain μ-invariant vanishes. For prime numbers p ∤ 6 which split in K, we can prove the equivariant main conjecture using a theorem by Gillard.
Spectrum Maximizing Products Are Not Generically Unique

It is widely believed that typical finite families of $d \times d$ matrices admit finite products that attain the joint spectral radius. This conjecture is supported by computational experiments and it naturally leads to the following question: are these spectrum maximizing products typically unique, up to cyclic permutations and powers? We answer this question negatively. As discovered by Horowitz around fifty years ago, there are products of matrices that always have the same spectral radius despite not being cyclic permutations of one another. We show that the simplest Horowitz products can be spectrum maximizing in a robust way; more precisely, we exhibit a small but nonempty open subset of pairs of $2 \times 2$ matrices $(A,B)$ for which the products $A^2 B A B^2$ and $B^2 A B A^2$ are both spectrum maximizing.
A Comparative Study of Solar Active Region 12371 with Data-constrained and Data-driven Magnetohydrodynamic Simulations

We performed two data-based magnetohydrodynamic (MHD) simulations for solar active region 12371, which produced an M6.5 flare. The first simulation is a full data-driven simulation where the initial condition is given by a nonlinear force-free field (NLFFF). This NLFFF was extrapolated from photospheric magnetograms approximately 1 hr prior to the flare, and then a time-varying photospheric magnetic field is imposed at the bottom surface. The second simulation is also a data-driven simulation, but it stops driving at the bottom before the time of flare onset and then switches to the data-constrained simulation, where the horizontal component of the magnetic field varies according to an induction equation, while the normal component is fixed with time. Both simulations lead to an eruption, with both simulations producing highly twisted field lines before the eruption, which were not found in the NLFFF alone. After the eruption, the first simulation based on the time-varying photospheric magnetic field continues to produce sheared field lines after the flare without reproducing phenomena such as postflare loops. The second simulation reproduces the phenomena associated with flares well. However, in this case, the evolution of the bottom magnetic field is inconsistent with the evolution of the observed magnetic field. In this Letter, we report potential advantages and disadvantages in data-constrained and data-driven MHD simulations that need to be taken into consideration in future studies.
Specializing Smaller Language Models towards Multi-Step Reasoning

The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters). We show that such abilities can, in fact, be distilled down from GPT-3.5 ($\ge$ 175B) to T5 variants ($\le$ 11B). We propose model specialization, to specialize the model's ability towards a target task. The hypothesis is that large models (commonly viewed as larger than 100B) have strong modeling power, but are spread on a large spectrum of tasks. Small models (commonly viewed as smaller than 10B) have limited model capacity, but if we concentrate their capacity on a specific target task, the model can achieve a decent improved performance. We use multi-step math reasoning as our testbed because it is a very typical emergent ability. We show two important aspects of model abilities: (1). there exists a very complex balance/ tradeoff between language models' multi-dimensional abilities; (2). by paying the price of decreased generic ability, we can clearly lift up the scaling curve of models smaller than 10B towards a specialized multi-step math reasoning ability. We further give comprehensive discussions about important design choices for better generalization, including the tuning data format, the start model checkpoint, and a new model selection method. We hope our practice and discoveries can serve as an important attempt towards specialized smaller models in the new research paradigm set by LLMs.
Threat Modelling in Virtual Assistant Hub Devices Compared With User Risk Perceptions (2021)

Despite increasing uptake, there are still many concerns as to the security of virtual assistant hubs (such as Google Nest and Amazon Alexa) in the home. Consumer fears have been somewhat exacerbated by widely-publicised privacy breaches, and the continued prevalence of high-profile attacks targeting IoT networks. Literature suggests a considerable knowledge gap between consumer understanding and the actual threat environment; furthermore, little work has been done to compare which threat modelling approach(es) would be most appropriate for these devices, in order to elucidate the threats which can then be communicated to consumers. There is therefore an opportunity to explore different threat modelling methodologies as applied to this context, and then use the findings to prototype a software aimed at educating consumers in an accessible manner. Five approaches (STRIDE, CVSS, Attack Trees (a.k.a. Threat Trees), LINDUNN GO, and Quantitative TMM) were compared as these were determined to be either the most prominent or potentially applicable to an IoT context. The key findings suggest that a combination of STRIDE and LINDUNN GO is optimal for elucidating threats under the pressures of a tight industry deadline cycle (with potential for elements of CVSS depending on time constraints), and that the trialled software prototype was effective at engaging consumers and educating about device security. Such findings are useful for IoT device manufacturers seeking to optimally model threats, or other stakeholders seeking ways to increase information security knowledge among consumers.
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents

The optimized certainty equivalent (OCE) is a family of risk measures that cover important examples such as entropic risk, conditional value-at-risk and mean-variance models. In this paper, we propose a new episodic risk-sensitive reinforcement learning formulation based on tabular Markov decision processes with recursive OCEs. We design an efficient learning algorithm for this problem based on value iteration and upper confidence bound. We derive an upper bound on the regret of the proposed algorithm, and also establish a minimax lower bound. Our bounds show that the regret rate achieved by our proposed algorithm has optimal dependence on the number of episodes and the number of actions.
Basis-Finding Algorithm for Decoding Fountain Codes for DNA-Based Data Storage

In this paper, we consider the decoding of fountain codes where the received symbols may have errors. It is motivated by the application of fountain codes in DNA-based data storage systems where the inner code decoding, which generally has undetectable errors, is performed before the outer fountain code decoding. We propose a novel and efficient decoding algorithm, namely basis-finding algorithm (BFA), followed by three implementations. The key idea of the BFA is to find a basis of the received symbols, and then use the most reliable basis elements to recover the source symbols with the inactivation decoding. Gaussian elimination is used to find the basis and to identify the most reliable basis elements. As a result, the BFA has polynomial time complexity. For random fountain codes, we are able to derive some theoretical bounds for the frame error rate (FER) of the BFA. Extensive simulations with Luby transform (LT) codes show that, the BFA has significantly lower FER than the belief propagation (BP) algorithm except for an extremely large amount of received symbols, and the FER of the BFA generally decreases as the average weight of basis elements increases.
Eulerian Simulations Of Oscillating Airfoils InPower Extraction Regime

A wing that is both heaving and pitching simultaneously may extract energy from an oncoming flow, thus acting as a turbine. The theoretical performance of such a concept is investigated here through unsteady, two-dimensional laminar flow simulations using the finite volume, commercial CFD code FLUENT. Computations are performed in the heaving reference frame of the airfoil, thus leaving only the pitching motion of the airfoil to be dealt with through a rigid-body mesh rotation and a circular, non-conformal sliding interface. This approach offers the benefit of second order time accurate simulations. For a NACA 0015 airfoil at a Reynolds number of Re = 1 100, a heaving amplitude of one chord (H0 = c), and a pitching axis at the third chord (xp = c/3), we present a mapping of power extraction efficiency in the frequency and pitching amplitude domain: 0 55◦ of better than 20% results. Impact of varying some of the fixed parameters is also addressed.
On the propagation of acoustic–gravity waves due to a slender rupture in an elastic seabed

Abstract The propagation of waves from a vertical uplift of a slender rectangular fault in a sea of constant depth is discussed, accounting for water compressibility, gravity and seabed elasticity. The compressed water column results in the generation of acoustic–gravity waves that travel at the speed of sound in water. Acoustic–gravity waves are found to terminate after a finite time, with the decay time most influenced by seabed rigidity, which is in contrast to the rigid stationary-phase model where signals persist indefinitely. At certain frequencies acoustic–gravity waves couple with the elastic seabed and travel at the shear velocity (speed of sound in an elastic solid). Improved estimates of the critical frequencies are derived. Moreover, besides the usual tsunami, a second – very small amplitude – surface wave mode travelling at the speed of sound arises under certain frequencies. We derive the cut-off frequency for this mode. The acoustic modes possess a frequency spectrum which depends on the time evolution and spatial properties of the rupture. We find that appropriate filtering of the acoustic–gravity wave signal can reveal characteristic peaks that encode information on the fault's geometry and dynamics.
Linear Accuracy of Intraoral Scanners for Full-Arch Impressions of Implant-Supported Prostheses: A Systematic Review and Meta-Analysis

Abstract This article compares the accuracy of intraoral scanners (IOSs) used in the digital impression of full arches to fabricate implant-supported complete prostheses. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered in the Open Science Framework (DOI 10.17605/OSF.IO/CPM9K). Six electronic databases, gray literature databases, and a manual search were performed in April 2022. Studies that evaluated the accuracy of intraoral scan impressions compared with conventional impressions in full-arch impressions were included for complete implant-supported prostheses. In addition, an adapted checklist for reporting in vitro studies was used to assess the risk of bias. Meta-analysis was conducted using a random-effects Hunter– Schmidt model. Nine studies were included in the analysis. IOS impressions present higher accuracy (137.86 μm) than conventional impressions (182.51 μm) ( p <0.001). The heterogeneity of the study's methodology was I2¼18. 34. However, impression accuracy varies significantly with scan body type, IOS type, scanning strategy, and modification technique. For most IOS systems, the acceptable clinical threshold of linear accuracy of 200 μm can be achieved, except for the True Definition Scanner in one of the studies. Based on the results of the included studies, digital impressions using IOS present similar or better linear accuracy than conventional impression techniques.
Sex and ABO Blood Differences in SARS-CoV-2 Infection Susceptibility

Abstract Data consisting of millions of cases cannot still explain the immunopathogenesis mechanism between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and host cell for ongoing coronavirus disease 2019 (COVID-19) pandemics. Epidemiological studies among different populations suggested different impacts of ABO and Rh antibodies on the COVID-19 susceptibility. Thus, the ABO blood group and the SARS-CoV-2 infection paradox remain unclear. Therefore, the present retrospective case–control study aimed to investigate the possible association between ABO blood groups and Rh blood types on SARS-CoV-2 infection in the Turkish Cypriot population. A total of 18,639 Turkish Cypriot subjects (297 SARS-CoV-2 COVID-19 patients and 18,342 healthy) were included in this study. Personal and clinical characteristics including age, gender, SARS-CoV-2 infection status, the ABO blood group and Rh blood types were evaluated and compared between two groups. As a result, ABO blood group was shown to be associated with a higher risk of SARS-CoV-2 infection as well as with male sex ( p = 0.018). There was no association between Rh blood type and COVID-19. Overall, this study is the first largest sample group study to show the distribution of ABO blood group and Rh blood types in the healthy Turkish Cypriot population. Based on the current evidence, there are insufficient data to guide public health policies regarding COVID-19 pathogenesis.
Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI

Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, which has been recognised in HCI and related fields. Recent publications have begun to address the sociotechnical challenges of designing, developing, and successfully deploying clinician-facing ML-based systems. We conducted a qualitative systematic review and provided answers to the question: “How can HCI researchers and practitioners contribute to the successful realisation of ML in medical practice? ” We reviewed 25 eligible papers that investigated the real-world clinical implications of concrete clinician-facing ML-based systems. The main contributions of this systematic review are: (1) an overview of the technical aspects of ML innovation and their consequences for HCI researchers and practitioners; (2) a description of the different roles that ML-based systems can take in clinical settings; (3) a conceptualisation of the main activities of medical ML innovation processes; (4) identification of five sociotechnical interdependencies that emerge from medical ML innovation; and (5) implications for HCI researchers and practitioners on how to mitigate the sociotechnical challenges of medical ML innovation.
Compiling Distributed System Models with PGo

Distributed systems are difficult to design and implement correctly. In response, both research and industry are exploring applications of formal methods to distributed systems. A key challenge in this domain is the missing link between the formal design of a system and its implementation. Today, practitioners bridge this link through manual effort. We present a language called Modular PlusCal that extends PlusCal by cleanly separating the model of a system from a model of its environment. We then present a compiler tool-chain called PGo that automatically translates MPCal models to TLA+ for model checking, and that also compiles MPCal models to runnable Go code. PGo provides system designers with a new ability to model and check their designs, and then re-use their modeling efforts to mechanically extract runnable implementations of their designs. Our evaluation shows that the PGo approach works for complex models: we model check, compile, and evaluate the performance of MPCal systems based on Raft, CRDTs, and primary-backup. Compared to previous work, PGo requires less time to develop a checked model and derive a fully working implementation. With PGo we created a formally checked Raft model and its corresponding implementation in under 1 person-month, which is 3× less time than Ivy. Our evaluation shows that a PGo-based Raft KV store with three nodes has 41% higher throughput than a Raft KV store based on Ivy, the highest performing verified Raft-based KV store from related work. A PGo-based CRDT set has a latency within 2 × of a CRDT set implementation from SoundCloud called Roshi.