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Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is difficult due to its topic dependency, complexity, and subjectivity. The lack of context and phrasality of query suggestions emphasizes this problem. In a multi-step approach, we combine the benefits of large language models, pairwise comparison, and Elo-based scoring to identify and quantify bias in English search query suggestions. We apply our approach to the U.S. political news domain and compare bias in Google and Bing.

Human exploration of the moon is expected to resume in the next decade, following the last such activities in the Apollo programme time. One of the major objectives of returning to the Moon is to continue retrieving geological samples, with a focus on collecting high-quality specimens to maximize scientific return. Tools that assist astronauts in making informed decisions about sample collection activities can maximize the scientific value of future lunar missions. A lunar rock classifier is a tool that can potentially provide the necessary information for astronauts to analyze lunar rock samples, allowing them to augment in-situ value identification of samples. Towards demonstrating the value of such a tool, in this paper, we introduce a framework for classifying rock types in thin sections of lunar rocks. We leverage the vast collection of petrographic thin-section images from the Apollo missions, captured under plane-polarized light (PPL), cross-polarised light (XPL), and reflected light at varying magnifications. Advanced machine learning methods, including contrastive learning, are applied to analyze these images and extract meaningful features. The contrastive learning approach fine-tunes a pre-trained Inception-Resnet-v2 network with the SimCLR loss function. The fine-tuned Inception-Resnet-v2 network can then extract essential features effectively from the thin-section images of Apollo rocks. A simple binary classifier is trained using transfer learning from the fine-tuned Inception-ResNet-v2 to 98.44\% ($\pm$1.47) accuracy in separating breccias from basalts.

We present a randomized algorithm for solving low-degree polynomial equation systems over finite fields faster than exhaustive search. In order to do so, we follow a line of work by Lokshtanov, Paturi, Tamaki, Williams, and Yu (SODA 2017), Bj\"orklund, Kaski, and Williams (ICALP 2019), and Dinur (SODA 2021). In particular, we generalize Dinur's algorithm for $\mathbb{F}_2$ to all finite fields, in particular the "symbolic interpolation" of Bj\"orklund, Kaski, and Williams, and we use an efficient trimmed multipoint evaluation and interpolation procedure for multivariate polynomials over finite fields by Van der Hoeven and Schost (AAECC 2013). The running time of our algorithm matches that of Dinur's algorithm for $\mathbb{F}_2$ and is significantly faster than the one of Lokshtanov et al. for $q>2$. We complement our results with tight conditional lower bounds that, surprisingly, we were not able to find in the literature. In particular, under the strong exponential time hypothesis, we prove that it is impossible to solve $n$-variate low-degree polynomial equation systems over $\mathbb{F}_q$ in time $O((q-\varepsilon)^{n})$. As a bonus, we show that under the counting version of the strong exponential time hypothesis, it is impossible to compute the number of roots of a single $n$-variate low-degree polynomial over $\mathbb{F}_q$ in time ${O((q-\varepsilon)^{n})}$; this generalizes a result of Williams (SOSA 2018) from $\mathbb{F}_2$ to all finite fields.

Let $f = f(z,t)$ be a function holomorphic in $z \in O \subseteq {\mathbb C}^d$ for fixed $t\in \Omega$ and measurable in $t$ for fixed $z$ and such that$z \mapsto f(z,\cdot)$ is bounded with values in$E := L_{p}(\Omega)$, $1\le p \le \infty$. It is proved (among other things) that \[ \langle t\mapsto \varphi( f(\cdot,t) ) , \mu \rangle= \varphi(z \mapsto \langle f(z, \cdot) , \mu\rangle )\] whenever $\mu \in E'$ and $\varphi$ is a linear functional on $H^\infty(O)$ that is sequentially continuous with respect to bounded pointwise convergence in $H^\infty(O)$.

The HaliVer tool integrates deductive verification into the popular scheduling language Halide, used for image processing pipelines and array computations. HaliVer uses Vercors, a separation logic-based verifier, to verify the correctness of (1) the Halide algorithms and (2) the optimised parallel code produced by \halide when an optimisation schedule is applied to the algorithm. This allows proving complex, optimised code correct while reducing the effort to provide the required verification annotations. For both approaches, the same specification is used. We evaluated the tool on several optimised programs generated from characteristic Halide algorithms, using all but one of the essential scheduling directives available in Halide. Without annotation effort, Haliver proves memory safety in almost all programs. With annotations Haliver, additionally, proves functional correctness properties. We show that the approach is viable and reduces the manual annotation effort by an order of magnitude.

The way the media presents events can significantly affect public perception, which in turn can alter people's beliefs and views. Media bias describes a one-sided or polarizing perspective on a topic. This article summarizes the research on computational methods to detect media bias by systematically reviewing 3140 research papers published between 2019 and 2022. To structure our review and support a mutual understanding of bias across research domains, we introduce the Media Bias Taxonomy, which provides a coherent overview of the current state of research on media bias from different perspectives. We show that media bias detection is a highly active research field, in which transformer-based classification approaches have led to significant improvements in recent years. These improvements include higher classification accuracy and the ability to detect more fine-granular types of bias. However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems. Concluding from our analysis, we see the integration of recent machine learning advancements with reliable and diverse bias assessment strategies from other research areas as the most promising area for future research contributions in the field.

Reading levels are highly individual and can depend on a text's language, a person's cognitive abilities, or knowledge on a topic. Text simplification is the task of rephrasing a text to better cater to the abilities of a specific target reader group. Simplification of scientific abstracts helps non-experts to access the core information by bypassing formulations that require domain or expert knowledge. This is especially relevant for, e.g., cancer patients reading about novel treatment options. The SimpleText lab hosts the simplification of scientific abstracts for non-experts (Task 3) to advance this field. We contribute three runs employing out-of-the-box summarization models (two based on T5, one based on PEGASUS) and one run using ChatGPT with complex phrase identification., Comment: Paper accepted at SimpleText@CLEF'23, 12 pages, 1 Figure, 4 Tables

An Eggert-operad is a variant of Mac Lane's notion of a PROP, for which not only bijective maps, but all maps between standard finite sets, are part of the structure. We construct the free Eggert-operad and prove the universal property it satisfies. Further we use the free Eggert-operad to define the Eggert-operad $\mathrm{GROUP}_0^{\mathrm{fp}}$ via generators and relations and show that a $\mathrm{GROUP}_0^{\mathrm{fp}}$-algebra is a group and vice versa. At the end we prove operadic versions of some elementary facts about groups., Comment: 26 pages

We present spatially resolved measurements of the temperature of a flat liquid water microjet for varying pressures, from vacuum to 100% relative humidity. The entire jet surface is probed in a single shot by a high-resolution infrared camera. Obtained 2D images are substantially influenced by the temperature of the apparatus on the opposite side of the IR camera; a protocol to correct for the thermal background radiation is presented. In vacuum, we observe cooling rates due to water evaporation on the order of 105 K/s. For our system, this corresponds to a temperature decrease of approximately 15 K between upstream and downstream positions of the flowing leaf. Making reasonable assumptions on the absorption of the thermal background radiation in the flatjet we can extend our analysis to infer a thickness map. For a reference system our value for the thickness is in good agreement with the one reported from white light interferometry., Comment: The following article has been submitted to Structural Dynamics. After it is published, it will be found at Link

This note deals with the boundedness of the $H^\infty$ functional calculus of Ritt operators $T$ and associated square function estimates. The purpose is to give a shorter, concise and slightly more general approach towards Le~Merdy's results [19] on this subject., Comment: 17 pages

In subleading powers of soft-collinear effective theory (SCET), the Lagrangian contains couplings between soft quarks and hard-collinear quarks. Matrix elements of the hard-collinear parts of these couplings are radiative jet functions. In the position-space formulation of SCET, the Lagrangians are constructed from operators that appear to be gauge invariant. Nevertheless, we find violations of gauge invariance arise in the hard-collinear sector because gauge transformations can shift the momentum of a hard-collinear quark field from the hard-collinear sector to the soft sector, where the hard-collinear fields, by definition, have no support. The violations of gauge invariance are manifested in perturbation theory in the hard-collinear sector through the absence of certain Feynman diagrams that would be present in full QCD. A consequence of the absence of these diagrams is that the radiative jet functions that follow directly from the position-space Lagrangians are not gauge invariant, and we demonstrate this through explicit calculations in lower-order perturbation theory. We obtain gauge-invariant Lagrangians by adding to existing position-space Lagrangians terms that are proportional to the soft-quark equation of motion. These gauge-invariant Lagrangians are valid for nonzero, as well as zero, quark masses. We also remark briefly on the gauge invariance of certain Lagrangians that have been constructed in the label-momentum formulation of SCET., Comment: 33 pages, 7 figures. [v2] Major revision. Soft-quark equation of motion used to reformulate the Lagrangians. [v3] conceptual clarifications, version published in Physical Review D