Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Self-Adjoint Quasi-periodic Operators with complex spectrum

We give a precise and complete description on the spectrum for a class of non-self-adjoint quasi-periodic operators acting on $\ell^2(\mathbb{Z}^d)$ which contains the Sarnak's model as a special case. As a consequence, one can see various interesting spectral phenomena including $\mathscr{P}\mathscr{T}$ symmetric breaking, the non-simply-connected two-dimensional spectrum in this class of operators. Particularly, we provide new examples of non-self-adjoint operator in $\mathscr{l}^{2}(\mathbb{Z})$ whose spectra (actually a two-dimensional subset of $\mathbb{C}$) can not be approximated by the spectra of its finite-interval truncations.
On $k$-Du Bois and $k$-rational singularities

We introduce new notions of $k$-Du Bois and $k$-rational singularities, extending the previous definitions in the case of local complete intersections (lci), to include natural examples outside of this setting. We study the stability of these notions under general hyperplane sections and show that varieties with $k$-rational singularities are $k$-Du Bois, extending previous results in [MP22b] and [FL22b] in the lci and the isolated singularities cases. In the process, we identify the aspects of the theory that depend only on the vanishing of higher cohomologies of Du Bois complexes (or related objects), and not on the behaviour of the K\"ahler differentials.
A Decomposition Approach to Last Mile Delivery Using Public Transportation Systems

This study explores the potential of using public transportation systems for freight delivery, where we intend to utilize the spare capacities of public vehicles like buses, trams, metros, and trains, particularly during off-peak hours, to transport packages within the city instead of using dedicated delivery vehicles. The study contributes {to the growing} literature on innovative strategies for performing sustainable last mile deliveries. We study an operational level problem called the Three-Tier Delivery Problem on Public Transportation, where packages are first transported from the Consolidation and Distribution Center (CDC) to nearby public vehicle stations by delivery trucks. From there, public vehicles transport them into the city area. The last leg of the delivery is performed to deliver the packages to their respective customers using green vehicles or eco-friendly systems. We propose mixed-integer linear programming formulations to study the transport of packages from the CDC to the customers, use decomposition approaches to solve them, and provide numerical experiments to demonstrate the efficiency and effectiveness of the system. Our results show that this system has the potential to drastically reduce the length of trips performed by dedicated delivery vehicles, thereby reducing the negative social and environmental impacts of existing last mile delivery systems.
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts

A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs. Toward building more robust and reliable DSTs, we introduce a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models. Two key characteristics of this approach are: (i) it only needs the output of the DST with no need for model parameters, and (ii) it can learn to generate natural language utterances that can target any DST. Through experiments over state-of-the-art DSTs, the proposed framework leads to the greatest reduction in accuracy and the best attack success rate while maintaining good fluency and a low perturbation ratio. We also show how much the generated adversarial examples can bolster a DST through adversarial training. These results indicate the strength of prompt-based attacks on DSTs and leave open avenues for continued refinement.
Coexistence of polar and antipolar phases in ferroelectric halide perovskite 
CsGeBr3

Recently ferroelectricity has been demonstrated in the family of halide perovskites: CsGeX$_3$ (X=Cl, Br, I). We develop a first-principles-based computational approach to probe finite-temperature properties of \cgb. Our computations reveal the existence of a dynamic antipolar phase above the Curie temperature. The phase can be stabilized below the Curie temperature through quenching. Furthermore, competition between the polar and antipolar phases results in the formation of rare polar-antipolar domains. We demonstrate that such polar-antipolar domains can result in the formation of unusual polarization fields with head-to-head and tail-to-tail dipole arrangements, which not only offers an explanation for the recent experimental findings but could also lead to the discovery of novel topological polar structures.
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; the diversity of objectives in real-world tasks and human opinions exacerbate the issue. This paper proposes embracing the heterogeneity of diverse rewards by following a multi-policy strategy. Rather than focusing on a single a priori reward, we aim for Pareto-optimal generalization across the entire space of preferences. To this end, we propose rewarded soup, first specializing multiple networks independently (one for each proxy reward) and then interpolating their weights linearly. This succeeds empirically because we show that the weights remain linearly connected when fine-tuned on diverse rewards from a shared pre-trained initialization. We demonstrate the effectiveness of our approach for text-to-text (summarization, Q&A, helpful assistant, review), text-image (image captioning, text-to-image generation, visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the alignment of deep models, and how they interact with the world in all its diversity.
ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems

Dialogue response selection aims to select an appropriate response from several candidates based on a given user and system utterance history. Most existing works primarily focus on post-training and fine-tuning tailored for cross-encoders. However, there are no post-training methods tailored for dense encoders in dialogue response selection. We argue that when the current language model, based on dense dialogue systems (such as BERT), is employed as a dense encoder, it separately encodes dialogue context and response, leading to a struggle to achieve the alignment of both representations. Thus, we propose Dial-MAE (Dialogue Contextual Masking Auto-Encoder), a straightforward yet effective post-training technique tailored for dense encoders in dialogue response selection. Dial-MAE uses an asymmetric encoder-decoder architecture to compress the dialogue semantics into dense vectors, which achieves better alignment between the features of the dialogue context and response. Our experiments have demonstrated that Dial-MAE is highly effective, achieving state-of-the-art performance on two commonly evaluated benchmarks.
Edge Addition and the Change in Kemeny's Constant

Given a connected graph $G$, Kemeny's constant $\mathcal{K}({G})$ measures the average travel time for a random walk to reach a randomly selected vertex. It is known that when an edge is added to $G$, the value of Kemeny's constant may either decrease, increase, or stay the same. In this paper, we present a quantitative analysis of this behaviour when the initial graph is a tree with $n$ vertices. We prove that when an edge is added into a tree on $n$ vertices, the maximum possible increase in Kemeny's constant is roughly $\frac{2}{3}n,$ while the maximum possible decrease is roughly $\frac{3}{16}n^2$. We also identify the trees, and the edges to be added, that correspond to the maximum increase and maximum decrease. Throughout, both matrix theoretic and graph theoretic techniques are employed.
Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?

Available corpora for Argument Mining differ along several axes, and one of the key differences is the presence (or absence) of discourse markers to signal argumentative content. Exploring effective ways to use discourse markers has received wide attention in various discourse parsing tasks, from which it is well-known that discourse markers are strong indicators of discourse relations. To improve the robustness of Argument Mining systems across different genres, we propose to automatically augment a given text with discourse markers such that all relations are explicitly signaled. Our analysis unveils that popular language models taken out-of-the-box fail on this task; however, when fine-tuned on a new heterogeneous dataset that we construct (including synthetic and real examples), they perform considerably better. We demonstrate the impact of our approach on an Argument Mining downstream task, evaluated on different corpora, showing that language models can be trained to automatically fill in discourse markers across different corpora, improving the performance of a downstream model in some, but not all, cases. Our proposed approach can further be employed as an assistant tool for better discourse understanding.
A Bijection between Unbalanced Dyck Path and NE Lattice Path

Lattice paths are important tools on solving some combinatorial identities. This note gives a new bijection between unbalanced Dyck path (a path that never reaches the diagonal of the lattice) and NE (North and East only) lattice path from (0,0) to (n,n) by several partial reflections.
Bootstrap Prediction Inference of Non-linear Autoregressive Models

The non-linear autoregressive (NLAR) model plays an important role in modeling and predicting time series. One-step ahead prediction is straightforward using the NLAR model, but the multi-step ahead prediction is cumbersome. For instance, iterating the one-step ahead predictor is a convenient strategy for linear autoregressive (LAR) models, but it is suboptimal under NLAR. In this paper, we first propose a simulation and/or bootstrap algorithm to construct optimal point predictors under an $L_1$ or $L_2$ loss criterion. In addition, we construct bootstrap prediction intervals in the multi-step ahead prediction problem; in particular, we develop an asymptotically valid quantile prediction interval as well as a pertinent prediction interval for future values. In order to correct the undercoverage of prediction intervals with finite samples, we further employ predictive -- as opposed to fitted -- residuals in the bootstrap process. Simulation studies are also given to substantiate the finite sample performance of our methods.
Fitting of the solvable model for scattering by Helmholtz resonator

The analytic perturbation procedure is divergent on the continuous spectrum near the threshold of creation of resonances. For scattering on Helmholtz resonator with a small opening we suggest a modified procedure which is convergent and permits to observe the creation of the resonance. The role of the first step (“jump-start”) in that perturbation procedure is played by a solvable model of the resonator, which is completely fitted based on the resonance parameters. PACS numbers: 73.63. Hs,73.23. Ad,85.35. -p,85.35. Be
Predicting Prosodic Phrasing

Prosodic phrasing is an inherent part of the language mechanism. The conditions, though, that determine it, even though they have been a research topic for linguists for over two decades, are far from precisely determined and widely accepted. This thesis will present the results of a project in which an attempt was made to explore the reasons underlying the speakers’ choice of prosodic phrasing and identify regularities or constraints, which have been proposed over the years to govern it. The work departs from the comparison between two different proposals on the relation between syntactic and prosodic structure, namely Truckenbrodt’s (1999) syntax-based approach and Steedman’s (2000b) Information Structure based theory, and ends up identifying a third proposal, that of Nespor & Vogel’s (1986), as the one which better predicts the partition of the test utterances into Phonological Phrases. All proposals were assessed with respect to ToBI boundary strength tiers in a set of 451 annotated sentences of the BURadio News corpus.
Plane Symmetric Dissipative Future Universe Without Big Rip

Plane symmetric dissipative future universe without Big Rip in the context of Ecart formalism has been investigated. If cosmic dark energy behaves like a fluid with equation of state   p (p and ρ being pressure and energy density respectively) as well as generalized Chaplygin gas simultaneously then Big Rip dose not arise even for equation of state parameter 1    and scale factor is found to be regular for all time. Keywords—Phantom fluid, Big Rip, Plane symmetric universe.
A retrospective, observational study of 12 cases of expanded access customized phage therapy: production, characteristics, and clinical outcomes.

BACKGROUND Antibiotic resistance (AMR) is undermining modern medicine, a problem compounded by bacterial adaptation to antibiotic pressures. Phages are viruses that infect bacteria. Their diversity and evolvability offer the prospect of their use as a therapeutic solution. Reported are outcomes of customized phage therapy for patients with difficult-to-treat AMR infections. METHODS We retrospectively assessed 12 cases of customized phage therapy from a phage production center. Phages were screened, purified, sequenced, characterized, and FDA-approved via the IND compassionate care route. Outcomes were assessed as favorable or unfavorable by microbiologic and clinical standards. Infections were device-related or systemic. Other experiences such as time to treatment, antibiotic synergy and immune responses were recorded. RESULTS Fifty requests for phage therapy were received. Customized phages were generated for twelve patients. After treatment, 42% (5/12) of cases showed bacterial eradication and 58% (7/12) showed clinical improvement, with two-thirds of all cases (66%) showing favorable responses. No major adverse reactions were observed. Antibiotic-phage synergy in vitro was observed in most cases. Immunological neutralization of phage was reported in five cases. Several cases were complicated by secondary infections. Complete characterization of the phages (morphology, genomics, and activity) and their production (methods, sterility, and endotoxin tests) are reported. CONCLUSIONS Customized phage production and therapy was safe and yielded favorable clinical or microbiological outcomes in two-thirds of cases. A center or pipeline dedicated to tailoring the phages against a patient's specific AMR bacterial infection may be a viable option where standard treatment has failed.
Management of children with danger signs in integrated community case management care in rural southwestern Uganda (2014–2018)

Abstract Background In integrated community case management (iCCM) care, community health workers (CHWs) provide home-based management of fever, diarrhea and fast breathing for children aged <5 y. The iCCM protocol recommends that children with danger signs for severe illness are referred by CHWs to health facilities within their catchment area. This study examines the management of danger signs by CHWs implementing iCCM in a rural context. Methods A retrospective observational study that examined clinical records for all patients with danger signs evaluated by CHWs from March 2014 to December 2018 was conducted. Results In total, 229 children aged <5 y had been recorded as having a danger sign during 2014–2018. Of these children, 56% were males with a mean age of 25 (SD 16.9) mo, among whom 78% were referred by the CHWs as per the iCCM protocol. The age category of 12 to 35 mo had the highest numbers of prereferred and referred cases (54% and 46%, respectively). Conclusions CHWs play a key role in early symptomatic detection, prereferral treatment and early referral of children aged <5 y. Danger signs among children aged <5 y, if left untreated, can result in death. A high proportion of the children with danger signs were referred as per the iCCM protocol. Continuous CHW training is emphasized to reduce the number of referral cases that are missed. More studies need to focus on children aged 12–35 mo and why they are the most referred category. Policymakers should occasionally revise iCCM guidelines to detail the types of danger signs and how CHWs can address these.
Genome of the Lord Howe Island Stick Insect Reveals a Highly Conserved Phasmid X Chromosome

Abstract We present a chromosome-scale genome assembly for Dryococelus australis, a critically endangered Australian phasmid. The assembly, constructed with Pacific Biosciences continuous long reads and chromatin conformation capture (Omni-C) data, is 3.42 Gb in length with a scaffold N50 of 262.27 Mb and L50 of 5. Over 99% of the assembly is contained in 17 major scaffolds, which corresponds to the species’ karyotype. The assembly contains 96.3% of insect Benchmarking Unique Single Copy Ortholog genes in single copy. A custom repeat library identified 63.29% of the genome covered by repetitive elements; most were not identifiable based on similarity to sequences in existing databases. A total of 33,793 putative protein-coding genes were annotated. Despite the high contiguity and single-copy Benchmarking Unique Single Copy Ortholog content of the assembly, over 1 Gb of the flow-cytometry-estimated genome size is not represented, likely due to the large and repetitive nature of the genome. We identified the X chromosome with a coverage-based analysis and searched for homologs of genes known to be X-linked across the genus Timema. We found 59% of these genes on the putative X chromosome, indicating strong conservation of X-chromosomal content across 120 million years of phasmid evolution.
Sampling volume assessment for wearable multimodal optical diagnostic device

The process and results of numerical Monte Carlo simulation of optical radiation propagation in laser Doppler flowmetry (LDF) and fluorescence spectroscopy (FS) channels of a wearable diagnostic multimodal device are described in this paper. To achieve the goal, a multilayer skin model with different parameters of blood and melanin content and different distances between sources and radiation receivers was designed. The changes in the sampling (diagnostic) volume depending on the anatomical features of the biological tissues, as well as on the technical parameters of the device were shown. Depending on the scattering media optical properties and the source‐detector configuration of the device, the diagnostic volume can range from 2 to 7 mm3. The obtained results allow the formation of specialized medical and technical requirements for wearable multimodal devices implementing LDF and FS channels.
Comparative Study Among CO2 Laser, Intralesional Vitamin D3, and Combined Intralesional Vitamin D3 and CO2 Laser in Treatment of Palmoplantar Warts

BACKGROUND Many therapeutic modalities are used for palmoplantar warts; whether destructive, such as chemical cautery, electrocautery, cryocautery, surgical removal, and laser ablation, or immunotherapeutic, stimulating the immune system against the virus such as intralesional vitamin D3 injection. OBJECTIVE To compare the efficacy of intralesional vitamin D injection combined with CO2 laser to the efficacy of either modality alone. PATIENT AND METHODS Eighty age- and sex-matched patients with palmoplantar warts were divided into 4 groups: Group A received intralesional vitamin D3 injections, group B received ablative CO2 laser, group C received CO2 laser and intralesional vitamin D3 injection, and group D (control group) were injected intralesionally with normal saline. Assessment was performed clinically, photographically, and dermoscopically before and after treatment to evaluate the response, and then, another assessment was performed after 3 months to detect any recurrence. RESULTS Complete clearance was seen in 90% of cases in group C, in 80% in group A, and in 75% in group B with no statistically significant difference. CONCLUSION Intralesional vitamin D, CO2 laser, and the combination show comparable efficacy and recurrence rates. Intralesional vitamin D maybe a better option for people with a relative contraindication to CO2 laser.
Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven.

Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms. Then, a general HSI restoration model is formulated for optimization. Later, we comprehensively review existing HSI denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy 2-D convolutional neural network (CNN), 3-D CNN, hybrid, and unsupervised networks, to model-data-driven strategy. The advantages and disadvantages of each strategy for HSI denoising are summarized and contrasted. Behind this, we present an evaluation of the HSI denoising methods for various noisy HSIs in simulated and real experiments. The classification results of denoised HSIs and execution efficiency are depicted through these HSI denoising methods. Finally, prospects of future HSI denoising methods are listed in this technical review to guide the ongoing road for HSI denoising. The HSI denoising dataset could be found at https://qzhang95.github. io.