Miracle-dz/UniFAR-FTUs huggingface dataset:
Bypassing UGC from Some Optimal Geometric Inapproximability Results

The Unique Games Conjecture (UGC) has emerged in recent years as the starting point for several optimal inapproximability results. While for <i>none</i> of these results a reverse reduction to Unique Games is known, the assumption of bijective projections in the Label Cover instance nevertheless seems critical in these proofs. In this work, we bypass the need for UGC assumption in inapproximability results for two geometric problems, obtaining a tight NP-hardness result in each case. The first problem, known as <i>L<sub>p</sub></i> Subspace Approximation, is a generalization of the classic least squares regression problem. Here, the input consists of a set of points <i>X</i> = {α<sub>1</sub>, … , α<sub>m</sub>} ⊆ R<i><sub>n</sub></i> and a parameter <i>k</i> (possibly depending on <i>n</i>). The goal is to find a subspace <i>H</i> of <i>R</i><i><sub>n</sub></i> of dimension <i>k</i> that minimizes the ℓ<sub><i>p</i></sub> norm of the Euclidean distances to the points in <i>X</i>. For <i>p</i> = 2, <i>k</i> = <i>n</i> − 1, this reduces to the least squares regression problem, while for <i>p</i> = ∞, <i>k</i> = 0 it reduces to the problem of finding a ball of minimum radius enclosing all the points. We show that for any fixed <i>p</i> ∈ (2, ∞), and for <i>k</i> = <i>n</i> − 1, it is NP-hard to approximate this problem to within a factor of γ<sub><i>p</i></sub> − ε for constant ε > 0, where γ<sub><i>p</i></sub> is the <i>p</i>th norm of a standard Gaussian random variable. This matches the γ<sub><i>p</i></sub> approximation algorithm obtained by Deshpande, Tulsiani, and Vishnoi who also showed the same hardness result under the UGC. The second problem we study is the related <i>L<sub>p</sub></i> Quadratic Grothendieck Maximization Problem, considered by Kindler, Naor, and Schechtman. Here, the input is a multilinear quadratic form ∑<sub><i>n</i></sub><sub><i>i</i>, <i>j</i> = 1</sub><i>a<sub>ij</sub>x<sub>i</sub>x<sub>j</sub></i> and the goal is to maximize the quadratic form over the ℓ<sub><i>p</i></sub> unit ball, namely, all <i>x</i> with ∑<sub><i>n</i></sub><sub><i>i</i> = 1</sub>|<i>x<sub>i</sub></i>|<sub><i>p</i></sub> ⩽ 1. The problem is polynomial time solvable for <i>p</i> = 2. We show that for any constant <i>p</i> ∈ (2, ∞), it is NP-hard to approximate the quadratic form to within a factor of γ<sub>2</sub><sub><i>p</i></sub> − ε for any ε > 0. The same hardness factor was shown under the UGC by Kindler et al. We also obtain a γ<sub>2</sub><sub><i>p</i></sub>-approximation algorithm for the problem using the convex relaxation of the problem defined by Kindler et al. A γ<sub>2</sub><sub><i>p</i></sub> approximation algorithm has also been independently obtained by Naor and Schechtman. These are the <i>first</i> approximation thresholds, proven under P ≠ NP, that involve the Gaussian random variable in a fundamental way. Note that the problem statements themselves do not explicitly involve the Gaussian distribution.
Evolutionary Optimization of Model Merging Recipes

We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.
MedCycle: Unpaired Medical Report Generation via Cycle-Consistency

Generating medical reports for X-ray images presents a significant challenge, particularly in unpaired scenarios where access to paired image-report data for training is unavailable. Previous works have typically learned a joint embedding space for images and reports, necessitating a specific labeling schema for both. We introduce an innovative approach that eliminates the need for consistent labeling schemas, thereby enhancing data accessibility and enabling the use of incompatible datasets. This approach is based on cycle-consistent mapping functions that transform image embeddings into report embeddings, coupled with report auto-encoding for medical report generation. Our model and objectives consider intricate local details and the overarching semantic context within images and reports. This approach facilitates the learning of effective mapping functions, resulting in the generation of coherent reports. It outperforms state-of-the-art results in unpaired chest X-ray report generation, demonstrating improvements in both language and clinical metrics.
Formulation and Evaluation of Fast-Disintegrating Sublingual Tablets of Atropine Sulfate: the Effect of Tablet Dimensions and Drug Load on Tablet Characteristics

In this study, we formulated and evaluated the effects of tablet dimensions and drug load on the characteristics of atropine sulfate (AS) fast-disintegrating sublingual tablets (FDSTs). We aim to develop AS FDSTs as an alternative non-invasive and portable dosage form for the emergency treatment of organophosphate (OP) toxicity. AS autoinjector, AtroPen®, is the only self-administered dosage form available as an antidote for-out-of-hospital emergency use, but it is associated with several limitations and drawbacks. Seven FDST formulations of two tablet sizes, 150 mg (A) and 50 mg (B), and of several AS loads, 0 mg (A1, B1), 2 mg (A2, B2), 4 mg (B3), and 8 mg (B4a, B4b), were formulated and manufactured by direct compression. AS FDST characteristics were evaluated using USP and non-USP tests. Results were statistically compared at p < 0.05. All FDSTs passed the USP content uniformity and friability tests, disintegrated and released AS in ≤30 and 60 s. B1 and B2 were significantly harder than A1 and A2. Water uptake of A1 was significantly the highest. However, B1 and B2 had shorter disintegration and wetting times and higher amounts of AS dissolved than did A1 and A2 (p < 0.05). Increasing AS negatively affected FDST tensile strength (p < 0.05 for B4a) and water uptake (p < 0.05 for B3, B4a and B4b), however, without affecting AS dissolution. Formulation of AS up to 16% into smaller FDSTs was successful. Smaller FDSTs were harder and disintegrated more quickly. These AS FDSTS have the potential for further in vivo testing to evaluate their OP antidote potential.
Effects of Parathyroid Hormone on Calcium Ions in Rat Bone Marrow Mesenchymal Stem Cells

The present study was conducted in order to explore the mechanisms whereby parathyroid hormone (PTH) maintains in vitro proliferation of bone marrow mesenchymal stem cells (BMSCs). Bone marrow was isolated from Sprague Dawley (SD) rat femurs, cultured in vitro, and passaged using a cell adherent culture method. The BMSC proliferation was evaluated by the methyl thiazolyl tetrazolium (MTT) assay and the fluorescence intensity of calcium ions in BMSCs was analyzed by laser scanning confocal microscopy (LSCM). Our results show that BMSC proliferation in the experimental group treated with PTH was more significant than controls. The calcium ion fluorescence intensity in BMSCs was significantly higher for the experimental group as compared to the control group. For each group, there was significant difference in the fluorescence intensity of calcium ions in BMSCs between 7 d and 14 d. In conclusion, parathyroid hormone increased the fluorescence intensity of calcium ions in BMSCs, which might represent a key mechanism whereby BMSC proliferation is maintained.
Importance of accounting for student identities and intersectionality for creating equitable and inclusive physics learning environments

This research focuses on the experiences of seven undergraduate women who were majoring in physics at a small liberal arts college. In the semi-structured, empathetic interviews we conducted, the women discuss how they decided to major in physics, their interactions with their peers and instructors, who has supported them during their physics trajectory, and suggestions that would improve their experiences in physics. We use Standpoint Theory and focus on the experiences of undergraduate women to get a holistic perspective of how they became interested in physics, how they have been supported in their physics journey as well as identify any challenges that they faced in their undergraduate physics program due to their identity. Using synergistic frameworks such as the domains of power and the Holistic Ecosystem for Learning Physics in an Inclusive and Equitable Environment (HELPIEE), we analyze how those in the position of power, e.g., instructors, can play important roles in establishing and maintaining safe, equitable, and inclusive environments for students, which is especially important for historically marginalized students such as women and ethnic and racial minority students in physics. Using these frameworks, we compare the experiences of these women with those in Johnson's research at a small college and our prior investigation at a large research university. We also discuss the suggestions provided by the undergraduate women to implement in the future to support current and future undergraduate women in physics and astronomy. Their suggestions are separated as personal advice for peers and suggestions for physics instructors.
Improved Survival in Asymptomatic Diabetic Patients With High-Risk Spect Imaging Treated With Coronary Artery Bypass Grafting

Background—The Bypass Angioplasty Revascularization Investigation trial demonstrated that symptomatic diabetics with multivessel coronary artery disease had a survival advantage with initial coronary artery bypass grafting (CABG) versus percutaneous coronary intervention (PCI). No published study has examined different treatments and outcome in asymptomatic diabetics. Methods and Results—This study group consisted of 826 asymptomatic diabetics (age 62±12 years; 76% men) without known coronary artery disease who had abnormal myocardial perfusion during stress single photon emission computed tomography (SPECT). SPECT images were classified as low-, intermediate-, and high-risk. Early revascularization (CABG or PCI ≤4 months after SPECT) was performed in 76 patients. Survival (follow-up, 5.3±3.3 years) was compared in patients treated with CABG, PCI, or medical therapy. Revascularization (CABG or PCI) was performed in 54 of 261 patients with high-risk scans and was independently associated with improved survival (&khgr;2=4.55; P=0.03 after multivariate adjustment). Subset analysis demonstrated that the survival advantage was confined to patients treated with CABG (n =39), with a 5-year survival CABG at 85%, PCI at 72%, and medical therapy at 67% (P=0.02 for 3 groups). Although CABG was associated with better survival, mortality remained high (3% per year). There was no survival advantage by treatment for patients with less-severe SPECT abnormalities. Conclusions—These nonrandomized data suggest that CABG improves survival in asymptomatic diabetic patients with high-risk SPECT, although revascularization was performed infrequently in these patients. These results parallel those of the Bypass Angioplasty Revascularization Investigation trial in symptomatic diabetic patients.
Prevalence of diabetes mellitus and impaired fasting blood glucose in patients with Lichen Planus

Background The relationship between Lichen Planus (LP) and diabetes was studied previously, but the results were in conflict. The aim of this study was to find the prevalence of diabetes mellitus (DM) in patients with LP among Iranian patients. Methods In this study, 80 patients with LP were enrolled. They referred to dermatology clinic of our hospital during one year. A self-designed checklist for the study included duration of the disease, the pattern of the distribution of lichenoid lesions and fasting blood sugar (FBS). Results From 80 patients with LP, 16 (20%) had diabetes. Also, 14 patients (17.5%) had impaired fasting glucose. The mean age of diabetic patients was significantly higher than non-diabetic group (p=0.039). In addition, the duration of LP in patients with DM was significantly higher than non-diabetic patients (p=0.024). Conclusion In our study, we saw a high prevalence of DM among patients with LP. Comparing our findings with the overall prevalence of DM in Iran, there was a significant difference between the prevalence of DM among patients with LP and the overall prevalence (p=0.001). Regarding our findings screening for FBS in patients with LP is required in Iran.
Diversity-aware Channel Pruning for StyleGAN Compression

StyleGAN has shown remarkable performance in unconditional image generation. However, its high computational cost poses a significant challenge for practical applications. Although recent efforts have been made to compress StyleGAN while preserving its performance, existing compressed models still lag behind the original model, particularly in terms of sample diversity. To overcome this, we propose a novel channel pruning method that leverages varying sensitivities of channels to latent vectors, which is a key factor in sample diversity. Specifically, by assessing channel importance based on their sensitivities to latent vector perturbations, our method enhances the diversity of samples in the compressed model. Since our method solely focuses on the channel pruning stage, it has complementary benefits with prior training schemes without additional training cost. Extensive experiments demonstrate that our method significantly enhances sample diversity across various datasets. Moreover, in terms of FID scores, our method not only surpasses state-of-the-art by a large margin but also achieves comparable scores with only half training iterations.
Recent Efforts Towards Understanding the Early Universe from a Fundamental Quantum Perspective

The observable universe is fundamentally inhomogeneous and anisotropic. Quantum description of the generation of these inhomogeneities and anisotropies is ill-understood and unsatisfactory. After providing a brief account of the standard approach of the generation of the classical density perturbations starting from the quantum fluctuations of inflaton field, I critically review various assumptions which are crucial for the success of this description, and point out various shortcomings around it. I also discuss the basic ideas and recent works by using an alternative path to overcome those shortcomings which is motivated by the so-called Collapse Model interpretation of quantum mechanics. Inspired by these works, I argue the necessity of constructing a class of manifestly inhomogeneous and anisotropic quantum states after inflation and discuss my recent works which provide one such prescription of building such a state, called the $T-$vacuum, defined in the radiation dominated stage of the early universe.
Cleo Collaboration

We have observed four fully reconstructed B 0 → D * + D * − candidates in 5.8 million Υ(4S) → BB decays recorded with the CLEO detector. The background is estimated to be 0.31 ± 0.10 events. The probability that the background could produce four or more signal candidates with the observed distribution among D * + and D * − decay modes is 1.1 × 10 −4. The measured decay rate, B(B 0 → D * + D * −) = [6.2 +4.0 −2.9 (stat) ± 1.0(syst)] × 10 −4 , is large enough for this decay mode to be of interest for the measurement of a time-dependent CP asymmetry.
Saturation and fluctuations in the proton wavefunction at large momentum transfers in exclusive diffraction at HERA

We present a model of proton geometry where the number and size of gluon density hotspots in the proton's thickness function evolves with the resolution scale of the event given by the Mandelstam $t$ variable in exclusive diffractive $ep$ collisions. We use the impact-parameter dependent saturation dipole model bSat/IPSat, as well as its linearised (non-saturated) version bNonSat. In the latter the proton thickness has a clear interpretation as a thickness and in the former it is directly related to the saturation scale. The resulting phenomenological model for the splitting of hotspots, making full use of earlier experimental and phenomenological studies, is able to describe the entire incoherent $t$-spectrum for $|t|>1.1~$GeV$^2$ with a single phenomenological parameter. We use the previously suggested hotspot model as an initial condition for our evolution. The resulting model is a resolution scale-evolution in the same vein as a parton shower.The incoherent cross section is directly proportional to geometrical fluctuations in the proton's inital state. The hotspot evolution give rise to several kinds of event-by-event fluctuations such as in the hotspot number, width and normalization, and saturation scale fluctuations is a direct effect of these. A natural consequence of our resolution based evolution is that the hotspots obtain an effective repulsion. We use our hotspot evolution model to investigate saturation scale effects in the $t$-spectrum, and found that HERA data is not sensitive to this physics.
Hierarchical Processing in Spoken Language Comprehension

Understanding spoken language requires a complex series of processing stages to translate speech sounds into meaning. In this study, we use functional magnetic resonance imaging to explore the brain regions that are involved in spoken language comprehension, fractionating this system into sound-based and more abstract higher-level processes. We distorted English sentences in three acoustically different ways, applying each distortion to varying degrees to produce a range of intelligibility (quantified as the number of words that could be reported) and collected whole-brain echo-planar imaging data from 12 listeners using sparse imaging. The blood oxygenation level-dependent signal correlated with intelligibility along the superior and middle temporal gyri in the left hemisphere and in a less-extensive homologous area on the right, the left inferior frontal gyrus (LIFG), and the left hippocampus. Regions surrounding auditory cortex, bilaterally, were sensitive to intelligibility but also showed a differential response to the three forms of distortion, consistent with sound-form-based processes. More distant intelligibility-sensitive regions within the superior and middle temporal gyri, hippocampus, and LIFG were insensitive to the acoustic form of sentences, suggesting more abstract nonacoustic processes. The hierarchical organization suggested by these results is consistent with cognitive models and auditory processing in nonhuman primates. Areas that were particularly active for distorted speech conditions and, thus, might be involved in compensating for distortion, were found exclusively in the left hemisphere and partially overlapped with areas sensitive to intelligibility, perhaps reflecting attentional modulation of auditory and linguistic processes.
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis

A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments. FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications. Theoretical results for the smooth non-convex objective function are provided by us, and it is shown that the $\epsilon-$stationary solution is achieved by our algorithm at the rate of $\mathcal{O}\left(\frac{1}{\sqrt{T}}\right)$, where $T$ is the total number of communication rounds. Additionally, via empirical validation, we demonstrated that the performance of FedNMUT is superior to the existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. This proves the capability of the proposed algorithm to counteract the negative effects of communication noise in a decentralized learning framework.
Interleaving Discovery and Composition for Simple Workflows

This paper describes a simple approach for service discovery and composition, where services are described using a subset of the DAML-S ontology. The central idea is to base composition on the mismatch between service discovery queries and their results, and iteratively letting this guide subsequent queries. This approach is a result of work aimed at understanding how much the DAML-S model can be simplified and still be viable for semantic annotation of Web services, specifically when the “use case” is that of automatic substitution of services that have become unavailable. A proof-ofconcept prototype to demonstrate the viability of the scheme is described.
Perceptual Load Affects Eyewitness Accuracy and Susceptibility to Leading Questions

Load Theory (Lavie, 1995, 2005) states that the level of perceptual load in a task (i.e., the amount of information involved in processing task-relevant stimuli) determines the efficiency of selective attention. There is evidence that perceptual load affects distractor processing, with increased inattentional blindness under high load. Given that high load can result in individuals failing to report seeing obvious objects, it is conceivable that load may also impair memory for the scene. The current study is the first to assess the effect of perceptual load on eyewitness memory. Across three experiments (two video-based and one in a driving simulator), the effect of perceptual load on eyewitness memory was assessed. The results showed that eyewitnesses were less accurate under high load, in particular for peripheral details. For example, memory for the central character in the video was not affected by load but memory for a witness who passed by the window at the edge of the scene was significantly worse under high load. High load memories were also more open to suggestion, showing increased susceptibility to leading questions. High visual perceptual load also affected recall for auditory information, illustrating a possible cross-modal perceptual load effect on memory accuracy. These results have implications for eyewitness memory researchers and forensic professionals.