Miracle-dz/UniFAR-FTUs huggingface dataset:
Holographic reconstruction of optical anisotropy of blood films and diagnostics of prostate cancer

Theoretical bases of 3D Muller-matrix mapping methods for optically anisotropic polycrystalline films of blood are presented. Algorithms for layerwise polarization reconstruction of the parameters of linear and circular birefringence and dichroism are presented. Phase dependencies of the first-fourth-order statistical moments that characterize the parameters of phase and amplitude anisotropy of polycrystalline blood films of healthy and patients with prostate cancer patients are determined. Differential diagnostics of the oncological state with excellent balanced accuracy was realized.
A low Diffusion HLL-CPS Scheme for all Mach number flows

A low diffusion version of the HLL-CPS scheme for resolving the shear layers and the flow features at low Mach numbers is presented here. The low diffusion HLL-CPS scheme is obtained by reconstructing the velocities at the cell interface with the face normal Mach number and a pressure function. Asymptotic analysis of the modified scheme shows a correct scaling of the pressure at low Mach numbers and a significant reduction in numerical dissipation. The robustness of the HLL-CPS scheme for strong shock is improved by reducing the contribution of the contact wave in the vicinity of the shock. The improvement in robustness for strong shock is demonstrated analytically through linear perturbation and matrix stability analyses. A set of numerical test cases are solved to demonstrate the efficacy of the proposed scheme over a wide range of Mach numbers.
Anonymous Refugee Housing with Upper-Bounds

Knop and Schierreich [AAMAS '23] recently introduced a novel model of refugee housing and specifically asked for the computational complexity picture of the following variant. Given a topology modelled as an undirected graph, a set of inhabitants, a number of refugees $R$, an assignment of inhabitants to houses of the topology, and an upper-bound for every inhabitant, find a set $\pi$ of unoccupied houses of size $R$ intended such that the number of refugees in the neighbourhood of every inhabitant is at most its upper-bound. If such a set $\pi$ exists, we say that the instance admits an inhabitant-respecting housing. In this paper, we show that the existence of inhabitant-respecting housing is not guaranteed even under several further restrictions of the upper-bounds. Then, we focus on the computational complexity of deciding whether inhabitant-respecting housing exists. To this end, we provide tractable algorithms for several restrictions of the topology. We complement these results with appropriate hardness results and running-time lower-bounds. Furthermore, we introduce a relaxed (or approximate) version of the inhabitant-respecting housing, where we allow at most $t$ upper-bounds to be exceeded.
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification

Endoscopy plays a major role in identifying any underlying abnormalities within the gastrointestinal (GI) tract. There are multiple GI tract diseases that are life-threatening, such as precancerous lesions and other intestinal cancers. In the usual process, a diagnosis is made by a medical expert which can be prone to human errors and the accuracy of the test is also entirely dependent on the expert's level of experience. Deep learning, specifically Convolution Neural Networks (CNNs) which are designed to perform automatic feature learning without any prior feature engineering, has recently reported great benefits for GI endoscopy image analysis. Previous research has developed models that focus only on improving performance, as such, the majority of introduced models contain complex deep network architectures with a large number of parameters that require longer training times. However, there is a lack of focus on developing lightweight models which can run in low-resource environments, which are typically encountered in medical clinics. We investigate three KD-based learning frameworks, response-based, feature-based, and relation-based mechanisms, and introduce a novel multi-head attention-based feature fusion mechanism to support relation-based learning. Compared to the existing relation-based methods that follow simplistic aggregation techniques of multi-teacher response/feature-based knowledge, we adopt the multi-head attention technique to provide flexibility towards localising and transferring important details from each teacher to better guide the student. We perform extensive evaluations on two widely used public datasets, KVASIR-V2 and Hyper-KVASIR, and our experimental results signify the merits of our proposed relation-based framework in achieving an improved lightweight model (only 51.8k trainable parameters) that can run in a resource-limited environment.
Reinforced Self-Training (ReST) for Language Modeling

Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.
Searching for axions with kaon decay at rest

We describe a novel search strategy for axions (or hadronically coupled axion-like particles) in the mass range of $m_a \lesssim 350\,{\rm MeV}$. The search relies on kaon decay at rest, which produces a mono-energetic signal in a large volume detector (e.g.\ a tank of liquid scintillator) from axion decays $a\rightarrow \gamma\gamma$ or $a\rightarrow e^+e^-$. The decay modes $K^+\to \pi^+ a$ and $a \to \gamma \gamma$ are induced by the axion's coupling to gluons, which is generic to any model which addresses the strong CP problem. We recast a recent search from MicroBooNE for $e^+e^-$ pairs, and study prospects at JSNS$^2$ and other near-term facilities. We find that JSNS$^2$ will have world-leading sensitivity to hadronically coupled axions in the mass range of $40\,\mathrm{MeV} \lesssim m_a \lesssim 350\,\mathrm{MeV}$.
Modular Superconducting Qubit Architecture with a Multi-chip Tunable Coupler

We use a floating tunable coupler to mediate interactions between qubits on separate chips to build a modular architecture. We demonstrate three different designs of multi-chip tunable couplers using vacuum gap capacitors or superconducting indium bump bonds to connect the coupler to a microwave line on a common substrate and then connect to the qubit on the next chip. We show that the zero-coupling condition between qubits on separate chips can be achieved in each design and that the relaxation rates for the coupler and qubits are not noticeably affected by the extra circuit elements. Finally, we demonstrate two-qubit gate operations with fidelity at the same level as qubits with a tunable coupler on a single chip. Using one or more indium bonds does not degrade qubit coherence or impact the performance of two-qubit gates.
Safe Collision and Clamping Reaction for Parallel Robots During Human-Robot Collaboration

Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety is investigated in this work through various contact reactions on a real planar PR. External forces are estimated based on proprioceptive information and a dynamics model, which allows contact detection. Retraction along the direction of the estimated line of action provides an instantaneous response to limit the occurring contact forces within the experiment to 70 N at a maximum velocity of 0.4 m/s. A reduction in the stiffness of a Cartesian impedance control is investigated as a further strategy. For clamping, a feedforward neural network (FNN) is trained and tested in different joint angle configurations to classify whether a collision or clamping occurs with an accuracy of 80%. A second FNN classifies the clamping kinematic chain to enable a subsequent kinematic projection of the clamping joint angle onto the rotational platform coordinates. In this way, a structure opening is performed in addition to the softer retraction movement. The reaction strategies are compared in real-world experiments at different velocities and controller stiffnesses to demonstrate their effectiveness. The results show that in all collision and clamping experiments the PR terminates the contact in less than 130 ms.
Is Argument Structure of Learner Chinese Understandable: A Corpus-Based Analysis

This paper presents a corpus-based analysis of argument structure errors in learner Chinese. The data for analysis includes sentences produced by language learners as well as their corrections by native speakers. We couple the data with semantic role labeling annotations that are manually created by two senior students whose majors are both Applied Linguistics. The annotation procedure is guided by the Chinese PropBank specification, which is originally developed to cover first language phenomena. Nevertheless, we find that it is quite comprehensive for handling second language phenomena. The inter-annotator agreement is rather high, suggesting the understandability of learner texts to native speakers. Based on our annotations, we present a preliminary analysis of competence errors related to argument structure. In particular, speech errors related to word order, word selection, lack of proposition, and argument-adjunct confounding are discussed.
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling

Weighted model counting (WMC) is the task of computing the weighted sum of all satisfying assignments (i.e., models) of a propositional formula. Similarly, weighted model sampling (WMS) aims to randomly generate models with probability proportional to their respective weights. Both WMC and WMS are hard to solve exactly, falling under the $\#\mathsf{P}$-hard complexity class. However, it is known that the counting problem may sometimes be tractable, if the propositional formula can be compactly represented and expressed in first-order logic. In such cases, model counting problems can be solved in time polynomial in the domain size, and are known as domain-liftable. The following question then arises: Is it also the case for weighted model sampling? This paper addresses this question and answers it affirmatively. Specifically, we prove the domain-liftability under sampling for the two-variables fragment of first-order logic with counting quantifiers in this paper, by devising an efficient sampling algorithm for this fragment that runs in time polynomial in the domain size. We then further show that this result continues to hold even in the presence of cardinality constraints. To empirically verify our approach, we conduct experiments over various first-order formulas designed for the uniform generation of combinatorial structures and sampling in statistical-relational models. The results demonstrate that our algorithm outperforms a start-of-the-art WMS sampler by a substantial margin, confirming the theoretical results.
Origins of limited non-basal plasticity in the {\mu}-phase at room temperature

We unveil a new non-basal slip mechanism in the {\mu}-phase at room temperature using nanomechanical testing, transmission electron microscopy and atomistic simulation. The (1-105) planar faults with a displacement vector of 0.07[-5502] can be formed by dislocation glide. They do not disrupt the Frank-Kasper packing and therefore enable the accommodation of plastic strain at low temperatures without requiring atomic diffusion. The intersections between the (1-105) planar faults and basal slip result in stress concentration and crack nucleation during loading.
Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.
Angle-Delay Profile-Based and Timestamp-Aided Dissimilarity Metrics for Channel Charting

Channel charting is a self-supervised learning technique whose objective is to reconstruct a map of the radio environment, called channel chart, by taking advantage of similarity relationships in high-dimensional channel state information. We provide an overview of processing steps and evaluation methods for channel charting and propose a novel dissimilarity metric that takes into account angular-domain information as well as a novel deep learning-based metric. Furthermore, we suggest a method to fuse dissimilarity metrics such that both the time at which channels were measured as well as similarities in channel state information can be taken into consideration while learning a channel chart. By applying both classical and deep learning-based manifold learning to a dataset containing sub-6GHz distributed massive MIMO channel measurements, we show that our metrics outperform previously proposed dissimilarity measures. The results indicate that the new metrics improve channel charting performance, even under non-line-of-sight conditions.
Characterizing Direct Product Testing via Coboundary Expansion

A $d$-dimensional simplicial complex $X$ is said to support a direct product tester if any locally consistent function defined on its $k$-faces (where $k\ll d$) necessarily come from a function over its vertices. More precisely, a direct product tester has a distribution $\mu$ over pairs of $k$-faces $(A,A')$, and given query access to $F\colon X(k)\to\{0,1\}^k$ it samples $(A,A')\sim \mu$ and checks that $F[A]|_{A\cap A'} = F[A']|_{A\cap A'}$. The tester should have (1) the ``completeness property'', meaning that any assignment $F$ which is a direct product assignment passes the test with probability $1$, and (2) the ``soundness property'', meaning that if $F$ passes the test with probability $s$, then $F$ must be correlated with a direct product function. Dinur and Kaufman showed that a sufficiently good spectral expanding complex $X$ admits a direct product tester in the ``high soundness'' regime where $s$ is close to $1$. They asked whether there are high dimensional expanders that support direct product tests in the ``low soundness'', when $s$ is close to $0$. We give a characterization of high-dimensional expanders that support a direct product tester in the low soundness regime. We show that spectral expansion is insufficient, and the complex must additionally satisfy a variant of coboundary expansion, which we refer to as \emph{Unique-Games coboundary expanders}. Conversely, we show that this property is also sufficient to get direct product testers. This property can be seen as a high-dimensional generalization of the standard notion of coboundary expansion over non-Abelian groups for 2-dimensional complexes. It asserts that any locally consistent Unique-Games instance obtained using the low-level faces of the complex, must admit a good global solution.
Evaluating the potential impacts of grey seal predation and fishery bycatch/discards on cod productivity on the Western Scotian Shelf and in the Bay of Fundy

The recovery of many groundfish stocks throughout the Northwest Atlantic has been impeded by elevated natural (i.e., non-fishing) mortality (M) among older/larger individuals. The causes of elevated mortality are not well known, though predation by rapidly growing grey seal herds and unreported fishing are thought to be possible drivers of mortality for Atlantic Cod (Gadus morhua) on the Western Scotian Shelf and in the Bay of Fundy (known as"4X5Y cod") and in nearby ecosystems. We developed a statistical catch-at-age model for 4X5Y cod that accounted for both grey seal predation and estimated bycatch/discards to evaluate the degree to which either of these factors may influence cod mortality. The model was fit over a range of predation and discarding scenarios to account for uncertainties and a lack of data for these processes. We found that most cod M remained unaccounted for unless cod comprised a large proportion (>0.45) of the grey seal diet by weight. If the reported bycatch estimates are taken as accurate, then the magnitude of cod discards from non-directed fisheries was minor, though these estimates are highly uncertain.
Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge

This paper proposes a novel lip reading framework, especially for low-resource languages, which has not been well addressed in the previous literature. Since low-resource languages do not have enough video-text paired data to train the model to have sufficient power to model lip movements and language, it is regarded as challenging to develop lip reading models for low-resource languages. In order to mitigate the challenge, we try to learn general speech knowledge, the ability to model lip movements, from a high-resource language through the prediction of speech units. It is known that different languages partially share common phonemes, thus general speech knowledge learned from one language can be extended to other languages. Then, we try to learn language-specific knowledge, the ability to model language, by proposing Language-specific Memory-augmented Decoder (LMDecoder). LMDecoder saves language-specific audio features into memory banks and can be trained on audio-text paired data which is more easily accessible than video-text paired data. Therefore, with LMDecoder, we can transform the input speech units into language-specific audio features and translate them into texts by utilizing the learned rich language knowledge. Finally, by combining general speech knowledge and language-specific knowledge, we can efficiently develop lip reading models even for low-resource languages. Through extensive experiments using five languages, English, Spanish, French, Italian, and Portuguese, the effectiveness of the proposed method is evaluated.
Periodic Event-Triggered CACC and Communication Co-design for Vehicle Platooning

Cooperative Adaptive Cruise Control (CACC) based vehicle platooning can increase the safety and efficiency of traffic. This work looks into the communication and control problems of vehicle platooning, and proposes a control and communication co-design for CACC. First, an integrated radar system is presented. This system integrates sensing of relative position, speed, and communication between a predecessor and its follower. Second, a working scheme for the integrated radar system is presented. This scheme allows the radar systems to switch periodically between different working modes without interferences from other modes. Therefore, the relative position, speed, and communication can be asynchronously periodically updated to the controller. Third, a periodic event-triggered control approach is presented. This approach allows asynchronous periodic sampling of the output, and is deeply co-designed with the radar system and its working scheme. Delays are also considered in the control approach. The co-design CACC approach can guarantee the vehicle platoons to be string stable. A numerical example has shown the feasibility of the approach.
A System to Prevent Social Violence using Convolutional Neural Network

Most women face violence in public and at home, including rape, physical and emotional abuse, mocking, and harassment. A social violence support application might allow people to seek aid from their friends, or relatives, or even request administrative assistance. The goal here is to detect clearly and reliably the screams of the individual in danger, that is, if the scream arose out of dread and horror, based on a particular collection of audio. Screams elicited by dread and panic usually have a shorter length, a higher frequency, and shrill pitches, whereas screams elicited by other emotions or intentionally have a longer duration, a fixed frequency, and pitch. In this sense, if we can use scream recognition to recognize dangerous and consequential circumstances in our society and inform the appropriate individuals at the appropriate moment, we will be able to avert these issues to a degree that will benefit both society and its citizens. To assist the wider populace, we have implemented a system using Convolutional Neural Network to identify screams automatically. This model will assist us in recognizing screams and sending SOS signals or messages to suitable contacts. As a result, people who are in danger will be able to call the people from their selected contacts or general authorities who are within their reach at any time. This system will not only assist victims in danger, but it will also provide them with a sense of security. On the other hand, the general authority will be able to use this software to limit the quantity of social and domestic violence.