Miracle-dz/UniFAR-FTUs huggingface dataset:
Strong coupling and single-photon nonlinearity in free-electron quantum optics

The observation that free electrons can interact coherently with quantized electromagnetic fields and matter systems has led to a plethora of proposals leveraging the unique quantum properties of free electrons. At the heart of these proposals lies the assumption of a strong quantum interaction between a flying free electron and a photonic mode. However, existing schemes are intrinsically limited by electron diffraction, which puts an upper bound on the interaction length and therefore the quantum coupling strength. Here, we propose the use of"free-electron fibers'': effectively one-dimensional photonic systems where free electrons co-propagate with two guided modes. The first mode applies a ponderomotive trap to the free electron, effectively lifting the limitations due to electron diffraction. The second mode strongly couples to the guided free electron, with an enhanced coupling that is orders of magnitude larger than previous designs. Moreover, the extended interaction lengths enabled by our scheme allows for strong single-photon nonlinearities mediated by free electrons. We predict a few interesting observable quantum effects in our system, such as deterministic single-photon emission and complex, nonlinear multimode dynamics. Our proposal paves the way towards the realization of many anticipated effects in free-electron quantum optics, such as non-Gaussian light generation, deterministic single photon emission, and quantum gates controlled by free-electron--photon interactions.
Reconstruction of Extracellular Respiratory Pathways for Iron(III) Reduction in Shewanella Oneidensis Strain MR-1

Shewanella oneidensis strain MR-1 is a facultative anaerobic bacterium capable of respiring a multitude of electron acceptors, many of which require the Mtr respiratory pathway. The core Mtr respiratory pathway includes a periplasmic c-type cytochrome (MtrA), an integral outer-membrane β-barrel protein (MtrB), and an outer-membrane-anchored c-type cytochrome (MtrC). Together, these components facilitate transfer of electrons from the c-type cytochrome CymA in the cytoplasmic membrane to electron acceptors at and beyond the outer-membrane. The genes encoding these core proteins have paralogs in the S. oneidensis genome (mtrB and mtrA each have four while mtrC has three) and some of the paralogs of mtrC and mtrA are able to form functional Mtr complexes. We demonstrate that of the additional three mtrB paralogs found in the S. oneidensis genome, only MtrE can replace MtrB to form a functional respiratory pathway to soluble iron(III) citrate. We also evaluate which mtrC/mtrA paralog pairs (a total of 12 combinations) are able to form functional complexes with endogenous levels of mtrB paralog expression. Finally, we reconstruct all possible functional Mtr complexes and test them in a S. oneidensis mutant strain where all paralogs have been eliminated from the genome. We find that each combination tested with the exception of MtrA/MtrE/OmcA is able to reduce iron(III) citrate at a level significantly above background. The results presented here have implications toward the evolution of anaerobic extracellular respiration in Shewanella and for future studies looking to increase the rates of substrate reduction for water treatment, bioremediation, or electricity production.
Agent-based MST Construction

{\em Minimum-weight spanning tree} (MST) is one of the fundamental and well-studied problems in distributed computing. In this paper, we initiate the study of constructing MST using mobile agents (aka robots). Suppose $n$ agents are positioned initially arbitrarily on the nodes of a connected, undirected, arbitrary, anonymous, port-labeled, weighted $n$-node, $m$-edge graph $G$ of diameter $D$ and maximum degree $\Delta$. The agents relocate themselves autonomously and compute an MST of $G$ such that exactly one agent positions on a node and tracks in its memory which of its adjacent edges belong to the MST. The objective is to minimize time and memory requirements. Following the literature, we consider the synchronous setting in which each agent performs its operations synchronously with others and hence time can be measured in rounds. We first establish a generic result: if $n$ and $\Delta$ are known a priori and memory per agent is as much as node memory in the message-passing model (of distributed computing), agents can simulate any $O(T)$-round deterministic algorithm for any problem in the message-passing model to the agent model in $O(\Delta T \log n+n\log^2n)$ rounds. As a corollary, MST can be constructed in the agent model in $O(\max\{\Delta \sqrt{n} \log n \log^*n, \Delta D \log n,n\log^2n\})$ rounds simulating the celebrated $O(\sqrt{n} \log^*n +D)$-round GKP algorithm for MST in the message-passing model. We then establish that, without knowing any graph parameter a priori, there exists a deterministic algorithm to construct MST in the agent model in $O(m+n\log n)$ rounds with $O(n \log n)$ bits memory at each agent. The presented algorithm needs to overcome highly non-trivial challenges on how to synchronize agents in computing MST as they may initially be positioned arbitrarily on the graph nodes.
Hardware and algorithms for ultrasonic depth imaging

Depth imaging is commonly based on light. For example, LIDAR and Kinect use infrared light, while stereo cameras use visible light. These systems require hardware operating at high sampling frequencies, precise calibration, and they dissipate significant power. In this paper, we investigate the potential of ultrasound for image and depth acquisition, with applications to human-computer interaction and skeletal tracking in mind. We use a loudspeaker array and a microphone array to sense the scene. We discuss a technique for offline loudspeaker beamforming (commonly used for microphone beamforming) which enables us to significantly increase the frame rate. Further, we propose a sound-source-localization-based method for computing the depth image, giving a substantial improvement over the näıve time-of-flight approach. We designed inexpensive hardware with eight elements per array to obtain both the depth and the intensity images. Even with this limited number of transducers we obtain promising experimental results.
A cooperative SIP infrastructure for highly reliable telecommunication services

Voice over IP (VoIP) has been a promising technology for several years. Although it has become very popular, the vision to substitute the "good old" Public Switched Telephone Network (PSTN) is still far from being true. Several issues, such as security, reliability and emergency calls are slowing down the integration of VoIP. Currently, there is a mixture of technologies where VoIP is often used for reducing costs, while still keeping the PSTN if a reliable communication is needed. Peer-to-Peer (P2P) and overlay networks received a large attention in the research community in the last few years. P2P networks provide self-organization and scalability. Recent work involved P2P-based signaling for lookup services for Voice over IP (VoIP) communication based on the Session Initiation Protocol (SIP). The main motivation behind P2P-basd SIP is to support ad hoc communication, to simplify the configuration of SIP networks, to make SIP networks more scalable and to provide services independently of other network components such as DNS. However, pure P2P networks do not have only their advantages. They create also several security threats which are hard to solve. Some of them, for example, the Sybil attack, have been proven to be unsolvable without centralized authorities or an additional out-of-band mechanism. P2P-based SIP may also create a high potential for Spam over IP Telephony (SPIT). In this paper, we present a hybrid solution for telecommunication networks that fills the gap between centralized (and therefore vulnerable to Denial-of-Service (DoS) attacks) telecommunication infrastructures and pure P2P-based telecommunication networks (which are vulnerable to SPIT, Sybil attacks, eclipse attacks, partition attacks, etc.). Our approach works with a SIP server under normal operation, while SIP User Agents (UAs) organize themselves into a P2P network. In the normal case, SIP UAs can profit from the lookup performance provided by the SIP server, which is usually better than the performance provided by the P2P network. In case of a service interruption of the infrastructure, the SIP network will function further due to the interconnection in the P2P network. Since the server and the P2P network cooperate in order to improve reliability, survivability, performance and security, we call this approach Cooperative SIP (CoSIP). We present our prototype implementation of CoSIP and discuss the potential for improvements compared to server-based SIP and P2P-based SIP.
HCiM: ADC-Less Hybrid Analog-Digital Compute in Memory Accelerator for Deep Learning Workloads

Analog Compute-in-Memory (CiM) accelerators are increasingly recognized for their efficiency in accelerating Deep Neural Networks (DNN). However, their dependence on Analog-to-Digital Converters (ADCs) for accumulating partial sums from crossbars leads to substantial power and area overhead. Moreover, the high area overhead of ADCs constrains the throughput due to the limited number of ADCs that can be integrated per crossbar. An approach to mitigate this issue involves the adoption of extreme low-precision quantization (binary or ternary) for partial sums. Training based on such an approach eliminates the need for ADCs. While this strategy effectively reduces ADC costs, it introduces the challenge of managing numerous floating-point scale factors, which are trainable parameters like DNN weights. These scale factors must be multiplied with the binary or ternary outputs at the columns of the crossbar to ensure system accuracy. To that effect, we propose an algorithm-hardware co-design approach, where DNNs are first trained with quantization-aware training. Subsequently, we introduce HCiM, an ADC-Less Hybrid Analog-Digital CiM accelerator. HCiM uses analog CiM crossbars for performing Matrix-Vector Multiplication operations coupled with a digital CiM array dedicated to processing scale factors. This digital CiM array can execute both addition and subtraction operations within the memory array, thus enhancing processing speed. Additionally, it exploits the inherent sparsity in ternary quantization to achieve further energy savings. Compared to an analog CiM baseline architecture using 7 and 4-bit ADC, HCiM achieves energy reductions up to 28% and 12%, respectively
Evaluative presentations

We study presentations of $C^*(X)$ that are evaluative over a presentation of $X$ in that $(f,p) \mapsto f(p)$ is computable. We prove existence-uniqueness theorems for such presentations. We use our methods to prove an effective Banach-Stone Theorem for unital commutative $C^*$ algebras. We also apply our results to the computable categoricity of $C^*$ algebras and compact Polish spaces.
When Do We Not Need Larger Vision Models?

Scaling up the size of vision models has been the de facto standard to obtain more powerful visual representations. In this work, we discuss the point beyond which larger vision models are not necessary. First, we demonstrate the power of Scaling on Scales (S$^2$), whereby a pre-trained and frozen smaller vision model (e.g., ViT-B or ViT-L), run over multiple image scales, can outperform larger models (e.g., ViT-H or ViT-G) on classification, segmentation, depth estimation, Multimodal LLM (MLLM) benchmarks, and robotic manipulation. Notably, S$^2$ achieves state-of-the-art performance in detailed understanding of MLLM on the V* benchmark, surpassing models such as GPT-4V. We examine the conditions under which S$^2$ is a preferred scaling approach compared to scaling on model size. While larger models have the advantage of better generalization on hard examples, we show that features of larger vision models can be well approximated by those of multi-scale smaller models. This suggests most, if not all, of the representations learned by current large pre-trained models can also be obtained from multi-scale smaller models. Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models. We release a Python package that can apply S$^2$ on any vision model with one line of code: https://github.com/bfshi/scaling_on_scales.
Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors

Abstract Autolysin E (AtlE) is a bacteriolytic enzyme which plays an important role in division and growth of bacterial cells and therefore represents a promising potential drug target. Its 3D structure has been recently elucidated. We used in silico prediction tools to study substrate or ligand (inhibitor) binding regions of AtlE. We applied several freely available tools and a commercial tool for binding site identification and compared results of the prediction. Calculation time, number of predictions and output data provided by specific software vary according to the different approaches utilized by specific method categories. Despite different approaches, binding sites in similar locations on the protein were predicted. Specific amino acid residues that form these binding sites were predicted as binding residues. The predicted residues, especially those with predicted highest conservation score, could theoretically have catalytic and binding properties. According to our results, we assume that E138, which has the highest conservation score, is the catalytic residue and F161, G162 and Y224, which are also highly conserved, are responsible for substrate binding. Ligands developed with binding specificity towards these residues could inhibit the catalysis and binding of the substrate of AtlE. The molecules with inhibitory potency could therefore represent potential new antibacterial agents.
Improving the Adaptive Moment Estimation (ADAM) stochastic optimizer through an Implicit-Explicit (IMEX) time-stepping approach

The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical Adam algorithm is a first order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the Adam scheme obtained by using higher order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical Adam on several regression and classification problems.
A class of bootstrap based residuals for compositional data

Regression models for compositional data are common in several areas of knowledge. As in other classes of regression models, it is desirable to perform diagnostic analysis in these models using residuals that are approximately standard normally distributed. However, for regression models for compositional data, there has not been any multivariate residual that meets this requirement. In this work, we introduce a class of asymptotically standard normally distributed residuals for compositional data based on bootstrap. Monte Carlo simulation studies indicate that the distributions of the residuals of this class are well approximated by the standard normal distribution in small samples. An application to simulated data also suggests that one of the residuals of the proposed class is better to identify model misspecification than its competitors. Finally, the usefulness of the best residual of the proposed class is illustrated through an application on sleep stages. The class of residuals proposed here can also be used in other classes of multivariate regression models.
Serum substance P concentrations and silent aspiration in elderly patients with stroke

Substance P (SP) is released from sensory nerve terminals and degraded by angiotensin-converting enzyme (ACE),1 and its action is potentiated by ACE inhibitors.2 SP levels in the sputum are reportedly low in patients with aspiration pneumonia,3 and silent aspiration disappears by treatment with ACE inhibitors in association with an increase in serum concentrations of SP in hypertensive patients with stroke.4 However, the relation between serum SP concentrations and improvement in silent aspiration has not been investigated yet in elderly patients with stroke. Therefore, we administered various doses of an ACE inhibitor to elderly patients with stroke to determine the effective concentration of serum SP that correlates best with improvement of the silent aspiration in such patients. The eligible patients had a history of stroke but were not bedridden. We enrolled 60 patients (29 men and 31 women) with normotension and silent aspiration detected by the method described below. Randomization was done by a random-numbers table, and the list …
Listenable Maps for Audio Classifiers

Despite the impressive performance of deep learning models across diverse tasks, their complexity poses challenges for interpretation. This challenge is particularly evident for audio signals, where conveying interpretations becomes inherently difficult. To address this issue, we introduce Listenable Maps for Audio Classifiers (L-MAC), a posthoc interpretation method that generates faithful and listenable interpretations. L-MAC utilizes a decoder on top of a pretrained classifier to generate binary masks that highlight relevant portions of the input audio. We train the decoder with a special loss that maximizes the confidence of the classifier decision on the masked-in portion of the audio while minimizing the probability of model output for the masked-out portion. Quantitative evaluations on both in-domain and out-of-domain data demonstrate that L-MAC consistently produces more faithful interpretations than several gradient and masking-based methodologies. Furthermore, a user study confirms that, on average, users prefer the interpretations generated by the proposed technique.
Self-generated Replay Memories for Continual Neural Machine Translation

Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving. Their ability to learn continuously is, however, still severely limited by the catastrophic forgetting issue. In this work, we leverage a key property of encoder-decoder Transformers, i.e. their generative ability, to propose a novel approach to continually learning Neural Machine Translation systems. We show how this can effectively learn on a stream of experiences comprising different languages, by leveraging a replay memory populated by using the model itself as a generator of parallel sentences. We empirically demonstrate that our approach can counteract catastrophic forgetting without requiring explicit memorization of training data. Code will be publicly available upon publication. Code: https://github.com/m-resta/sg-rep
Projection-free computation of robust controllable sets with constrained zonotopes

We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and outer-approximate robust controllable sets using constrained zonotopes, when the additive uncertainty set is a symmetric, convex, and compact set. Our least-squares-based approach uses novel closed-form approximations of the Pontryagin difference between a constrained zonotopic minuend and a symmetric, convex, and compact subtrahend. Unlike existing approaches, our approach does not rely on convex optimization solvers, and is projection-free for ellipsoidal and zonotopic uncertainty sets. We also propose a least-squares-based approach to compute a convex, polyhedral outer-approximation to constrained zonotopes, and characterize sufficient conditions under which all these approximations are exact. We demonstrate the computational efficiency and scalability of our approach in several case studies, including the design of abort-safe rendezvous trajectories for a spacecraft in near-rectilinear halo orbit under uncertainty. Our approach can inner-approximate a 20-step robust controllable set for a 100-dimensional linear system in under 15 seconds on a standard computer.
Optimal VPPI strategy under Omega ratio with stochastic benchmark

This paper studies a variable proportion portfolio insurance (VPPI) strategy. The objective is to determine the risk multiplier by maximizing the extended Omega ratio of the investor's cushion, using a binary stochastic benchmark. When the stock index declines, investors aim to maintain the minimum guarantee. Conversely, when the stock index rises, investors seek to track some excess returns. The optimization problem involves the combination of a non-concave objective function with a stochastic benchmark, which is effectively solved based on the stochastic version of concavification technique. We derive semi-analytical solutions for the optimal risk multiplier, and the value functions are categorized into three distinct cases. Intriguingly, the classification criteria are determined by the relationship between the optimal risky multiplier in Zieling et al. (2014 and the value of 1. Simulation results confirm the effectiveness of the VPPI strategy when applied to real market data calibrations.
Non-fluid like Boltzmann code architecture for early times f(T) cosmologies

There have been several works that have studied scalar cosmological perturbations in $f(T)$ teleparallel gravity theories to understand early cosmic times dynamics. In this direction, the perturbations presented have been performed by considering $f(T)$ extensions as an effective fluid-like scheme, where the equation-of-state contains extra terms due to the torsion. In this work, we discuss introducing a non-fluid-like approach as a direct consequence of $f(T)$ extensions, particularly for $f(T)$ power law model scenarios. This approach will be compared using CMB constraints data from Planck 2018 and SDSS catalogs, showing a change in about 17% in $C_{l}$ at $l<10^{1}$ from the ones reported in the literature as a fluid-like approach, which will bring significant changes in the analysis on cosmological tensions at early cosmic times.
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks to the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.