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Although far more women than men are sexually violated in conflict settings, the records indicate that sexual violence against men and boys has been routinely practised as a weapon of war and genocide. Sexual violence against men and boys during the Holocaust was likely a regular occurrence, but it has remained undocumented and under-researched. Sexual violence against men, because it does not conform to prevalent gender norms and expectations, has been subjected to cultural and epistemic erasure. As a result, it is construed on the model of female rape, making it difficult to recognize male-victim specific forms of assault. Moreover, normative and legal frameworks developed to address it do not take into account the role that the stigma of homosexuality plays in male sexual violence. This article is based on oral testimonies by male heterosexual-identified Jewish survivors of the Holocaust. I focus on the survivors’ self-presentation as adult men in light of their past abuse and on the dynamic of the interviews. I also reference one memoir (Nate Leipciger’s The Weight of Freedom) and reinterpret a chapter from Elie Wiesel’s Night in light of my findings. Revealing the extent of sexual violence against men helps delegitimize harmful gender stereotypes and conceptions of manhood and ‘homosexuality’ and expose their central role in the perpetuation of genocidal violence.

Interactive image retrieval involves users searching a collection of images to satisfy their subjective information needs. However, even large image collections are finite and therefore may not be able to satisfy users. An alternate approach would be to explore a generative adversarial network (GAN) and model users' search intents directly in terms of the latent space used by the GAN to generate images. In this article, we present a simulation study exploring the performance of Gaussian Process bandits in the context of interactive GAN exploration. We used recent advances in interpretable GAN controls to investigate the scalability of different approaches in terms of image space dimensionality. While we present several experiments with promising results, none of the approaches tested scale sufficiently well to explore the entire GAN image space.

Wikipedia is frequently criticised for having poor readability and style issues. In this article, we investigate using GPT-2, a neural language model, to identify poorly written text in Wikipedia by ranking documents by their perplexity. We evaluated the properties of this ranking using human assessments of text quality, including readability, narrativity and language use. We demonstrate that GPT-2 perplexity scores correlate moderately to strongly with narrativity, but only weakly with reading comprehension scores. Importantly, the model reflects even small improvements to text as would be seen in Wikipedia edits. We conclude by highlighting that Wikipedia's featured articles counter-intuitively contain text with the highest perplexity scores. However, these examples highlight many of the complexities that need to be resolved for such an approach to be used in practice.

Exploratory searches are open-ended search tasks where users are learning, investigating or otherwise trying to acquire new knowledge. Exploratory search encapsulates many common search activities, such as conducting a scientific literature review or planning a vacation. However, few information retrieval systems are capable of distinguishing exploratory search from traditional lookup search and, therefore, cannot provide adaptive support for both scenarios. In this article, we briefly outline the behavioural differences between users performing exploratory search and lookup search, and describe how search engines can use this information to transparently tailor search results to support both types of search task.

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.

This is the third edition of the Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). This series of workshops emerged as a response to the growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as documents, multimedia or specialised collections, such as biomedical datasets. There are various approaches to supporting users in this interactive environment ranging from the development of new algorithms through visualisation methods to analysing users' search patterns. The overarching goal of ESIDA is to bring together researchers working in areas that span across multiple facets of exploratory search and data analytics to discuss and outline research challenges for this novel area. © 2019 ACM.

Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers. In this article, we use topic modelling to represent content in abstracts and full-text articles. Using Computer Science as a case study, we demonstrate that how well the abstract summarises the full-text is subfield-dependent. Indeed, we show that abstract representativeness has a direct impact on retrieval performance, with poorer abstracts leading to degraded performance. Finally, we present evidence that how well an abstract represents the full-text of an article is not random, but is a consequence of style and writing conventions in different subdisciplines and can be used to infer an "evolutionary" tree of subfields within Computer Science.

Understanding what is expected of academic writing can be difficult for novice writers to assimilate, and recent years have seen several automated tools become available to support academic writing. Our work presents a framework for annotating features of the Related Work section of academic writing, that supports writer feedback.

Recent advances in deep neural networks have given rise to new approaches to content-based image retrieval (CBIR). Their ability to learn universal visual features for any target query makes them a good choice for systems dealing with large and diverse image datasets. However, employing deep neural networks in interactive CBIR systems still poses challenges: either the search target has to be predetermined, such as with hashing, or the computational cost becomes prohibitive for an online setting. In this paper, we present a framework for conducting interactive CBIR that learns a deep, dynamic metric between images. The proposed methodology is not limited to precalculated categories, hashes or clusters of the search space, but rather is formed instantly and interactively based on the user feedback. We use a deep learning framework that utilizes pre-extracted features from Convolutional Neural Networks and learns a new distance representation based on the user’s relevance feedback. The experimental results show the potential of applying our framework in an interactive CBIR setting as well as symbiotic interaction, where the system automatically detects what image features might best satisfy the user’s needs.

In recent years there has been a growing interest in developing new methods and systems that allow users to interactively explore large volumes of data, such as document collections, multimedia collections or biomedical datasets. There are various approaches to support users in this interactive environment ranging from the development of new algorithms through visualisation methods to specialised interfaces. The overarching goal of this workshop is to bring together a group of researchers spanning across multiple facets of exploratory search and data analytics to discuss, and outline research challenges for this novel area.

This paper presents the design and study of interactive user modeling to support exploratory search tasks. Contrary to traditional interactions, such as query based search, query suggestions, or relevance feedback, interactive user modeling allows a user to perceive the state of the user model at all times and provide feedback that directly rewards or penalizes it. The technique allows the user to continuously tune the system's belief about the user's evolving information needs. We demonstrate that such functionality is useful in exploratory search where users need to get accustomed to a body of literature in a domain. We conducted two experiments where scientists carried out exploratory search tasks with our implementation of an interactive user modeling and retrieval system (SciNet) and two baselines: SciNet from which interactive user modeling was excluded and a real-world baseline (Google Scholar). The results show that interactive user modeling can help users to more effectively find relevant, novel and diverse information without compromises in task execution time.

Motivation: Linkage analysis remains an important tool in elucidating the genetic component of disease and has become even more important with the advent of whole exome sequencing, enabling the user to focus on only those genomic regions co-segregating with Mendelian traits. Unfortunately, methods to perform multipoint linkage analysis scale poorly with either the number of markers or with the size of the pedigree. Large pedigrees with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to converge and, as no attempts have been made to exploit parallelism, massively underuse available processing power. Here, we describe SWIFTLINK, a novel application that performs MCMC linkage analysis by spreading the computational burden between multiple processor cores and a graphics processing unit (GPU) simultaneously. SWIFTLINK was designed around the concept of explicitly matching the characteristics of an algorithm with the underlying computer architecture to maximize performance. Results: We implement our approach using existing Gibbs samplers redesigned for parallel hardware. We applied SWIFTLINK to a real-world dataset, performing parametric multipoint linkage analysis on a highly consanguineous pedigree with EAST syndrome, containing 28 members, where a subset of individuals were genotyped with single nucleotide polymorphisms (SNPs). In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5× speed-up over the single-threaded version and a 109× speed-up over the popular linkage analysis program SIMWALK. Availability: SWIFTLINK is available at https://github.com/ajm/swiftlink. All source code is licensed under GPLv3. Contact: alan.j.medlar@helsinki.fi

openaire: EC/H2020/637991/EU//COMPUTED Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory—comparison, knowledge acquisition, planning—and 3 lookup tasks—fact-finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length,maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.

Here we report on the optical design and on the spectral-spatial characterisation of a small 16 pixel camera. The prototype uses TES detectors with NEPs ~10-16 W/Hz0.5 which have been fabricated with near identical optical coupling structures to mimic their much lower NEP counterparts (~10-19 W/Hz0.5). This modification, which is achieved through changing only the pixel thermal conductance, G, has allowed us to perform spectral/spatial cryogenic testing using a 100mK ADR to view room temperature thermal sources. The measurements show a flat spectral response across the waveband and minimal side lobe structure in the antenna patterns down to 30dB.

Despite the growing importance of exploratory search, information retrieval (IR) systems tend to focus on lookup search. Lookup searches are well served by optimising the precision and recall of search results, however, for exploratory search this may be counterproductive if users are unable to formulate an appropriate search query. We present a system called PULP that supports exploratory search for scientific literature, though the system can be easily adapted to other types of literature. PULP uses reinforcement learning (RL) to avert the user from context traps resulting from poorly chosen search queries, trading off between exploration (presenting the user with diverse topics) and exploitation (moving towards more specific topics). Where other RL-based systems suffer from the "cold start" problem, requiring sufficient time to adjust to a user's information needs, PULP initially presents the user with an overview of the dataset using temporal topic models. Topic models are displayed in an interactive alluvial diagram, where topics are shown as ribbons that change thickness with a given topics relative prevalence over time. Interactive, exploratory search sessions can be initiated by selecting topics as a starting point.

We present a novel adaptation technique for search engines to better support information-seeking activities that include both lookup and exploratory tasks. Building on previous findings, we describe (1) a classifier that recognizes task type (lookup vs. exploratory) as a user is searching and (2) a reinforcement learning based search engine that adapts accordingly the balance of exploration/exploitation in ranking the documents. This allows supporting both task types surreptitiously without changing the familiar list-based interface. Search results include more diverse results when users are exploring and more precise results for lookup tasks. Users found more useful results in exploratory tasks when compared to a base-line system, which is specifically tuned for lookup tasks.

In exploratory search tasks, users usually start with considerable uncertainty about their search goals, and so the search intent of the user may be volatile as the user is constantly learning and reformulating her search hypothesis during the search. This may lead to a noticeable concept drift in the relevance feedback given by the user. We formulate a Bayesian regression model for predicting the accuracy of each individual user feedback and thus find outliers in the feedback data set. To accompany this model, we introduce a timeline interface that visualizes the feedback history to the user and gives her suggestions on which past feedback is likely in need of adjustment. This interface also allows the user to adjust the feedback accuracy inferences made by the model. Simulation experiments demonstrate that the performance of the new user model outperforms a simpler baseline and that the performance approaches that of an oracle, given a small amount of additional user interaction. A user study shows that the proposed modeling technique, combined with the timeline interface, made it easier for the users to notice and correct mistakes in their feedback, resulted in better and more diverse recommendations, allowed users to easier find items they liked, and was more understandable.

In exploratory search, when the user formulates a query iteratively through relevance feedback, it is likely that the feedback given earlier requires adjustment later on. The main reason for this is that the user learns while searching, which causes changes in the relevance of items and features as estimated by the user -- a phenomenon known as {it concept drift}. It might be helpful for the user to see the recent history of her feedback and get suggestions from the system about the accuracy of that feedback. In this paper we present a timeline interface that visualizes the feedback history, and a Bayesian regression model that can estimate jointly the user's current interests and the accuracy of each user feedback. We demonstrate that the user model can improve retrieval performance over a baseline model that does not estimate accuracy of user feedback. Furthermore, we show that the new interface provides usability improvements, which leads to the users interacting more with it.

In exploratory search, the user starts with an uncertain information need and provides relevance feedback to the system's suggestions to direct the search. The search system learns the user intent based on this feedback and employs it to recommend novel results. However, the amount of user feedback is very limited compared to the size of the information space to be explored. To tackle this problem, we take into account user feedback on both the retrieved items (documents) and their features (keywords). In order to combine feedback from multiple domains, we introduce a coupled multi-armed bandits algorithm, which employs a probabilistic model of the relationship between the domains. Simulation results show that with multi-domain feedback, the search system can find the relevant items in fewer iterations than with only one domain. A preliminary user study indicates improvement in user satisfaction and quality of retrieved information.

Poland's Threatening Other: The Image of the Jew from 1880 to the Present, by Joanna Beata Michlic. Lincoln: University of Nebraska Press, 2006. 386 pp. $75.00. Arriving in the midst of acrimonious public debates in Poland (to which Joanna B. Michlic contributed in the past) surrounding the revelations about Poles' complicity in the murder and suffering of Polish Jews during WWII, Pofond's Threatening Other: The Image of the Jew from 1880 to the Present is a historical inquiry into the etiology of antisemitic prejudice in Poland and its long-term impact on the Polish society. As such, it belongs to a new progressive school of Polish scholarship, dedicated to the task of revising Polish historical and cultural dogmas. The book provides a much-needed background for the events described inj. T. Gross' Neighbors (about the massacres of Jews in Jedwabne by their Polish neighbors in 1942) and Fear (about the Kielce pogrom in 1946), making them more comprehensible as social and historical phenomena, though by no means more justifiable. Polish philosopher Leszek Kolakowski once remarked that, in the Polish language, "the Jew" always functioned as "an abstract negative symbol" and a general term of abuse. Typically, throughout the history of Poland, Jews have been blamed for the country's social ills and economic and political woes. In her study, Michlic excavates the historical trajectory of such pejorative valuations of Jewishness. She examines their exceptional durability and potency, arguing that negative perceptions of Jews have played a crucial role in the formation of Polish national identity. According to Michlic, ethno-nationalism (a belief that national membership lies in common genealogy, language, and cultural history) emerged in Poland in the 1880s, in the wake of failed national uprisings against occupying powers. At the time when Poland did not exist on the map of Europe, the language of the Jewish menace was disseminated to raise political consciousness and to consolidate the sense of Polish identity and unity. The cohesiveness of the Polish national myth depended on the exclusion of the Jewish other. The antisemitic topos gained dangerous momentum after Poland regained independence in 1918, when it became, in Michlic's words, "a powerful, emotive tool for nation building" (p. 1). The strife for national self-definition culminated in the coming to power, in 1935, of ultranationalist"Endecja," which implemented discriminatory measures against Jewish citizens and encouraged anti-Jewish violence, leading to the deterioration of Polish -Jewish relations on the eve of World War II. Here, Michlic's analyses are especially noteworthy since they throw light on the political provenance of Polish indifference to the Jewish tragedy during the war, the absence of solidarity with the Jews, low societal approval for the acts of rescue, and the lack of recognition for the acts of Jewish resistance, most notably the Warsaw Ghetto Uprising as part of the Polish struggle against the Germans. Immediately after the war, the idiom of the Jewish menace played a significant role in Polish people's unsuccessful resistance against the Soviet regime. As Jews were being held responsible for the imposition of the Soviet rule (as reflected in the widespread myth of zydokomuna - "Judeo-communism"), the eruption of violence against Holocaust survivors in Poland was perceived as fully justified. Paradoxically, negative images of Jews were evoked also by the communist government to fight political opposition, eventually leading to an exodus of the remaining Polish Jews (most notably after a virulent wave of "anti- Zionist" incidents in 1968). In this context, Michlic accounts for an apparent paradox of the co -existence of communist slogans of equality and internationalism, and the communist government's reliance on ethno-nationalist traditions to legitimize its rule. …

Astronomical instruments for measuring Cosmic Microwave Background polarisation, such as CLOVER, require large arrays of Superconducting Transition Edge Sensors (TESs). We report recent results from a processing route development aimed at high yield fabrication of microstrip-coupled TESs. The incoming signal is delivered onto a silicon nitride membrane by means of a superconducting microstrip transmission line. This transmission line is then terminated with a thin-film load resistor. The wafer-based fabrication route of the Mo/Cu TESs gives highly reproducible device characteristics in terms of superconducting transition temperature, electrical and thermal characteristics. An overall device yield of 65% has been achieved for a multi-wafer processing run.

f) n Rams, Jacques Derrida reflects on his "un-interrupted dialogue" [le dialogue / ininterrompu] with Hans Georg Gadamer.1 Across the impassable caesura of the German philosopher's death, Derrida extends a belated invitation to Gadamer to engage in a conversation on Paul Celan' s poetry and prose. Interrupted yet unceasing, the posthumous conversation "between two infinities, the poem," as stated in the subtitle takes place as an act of witness to the third and as a tribute to a common friend and his poetic legacy. This moment of recollection and (double) mourning also involves a translation and retranslation of Celan's poetic word the word of a poet-translator, whose own poetry has been often deemed untranslatable. Indeed, the question of the limits of interpretation between hermeneutics and deconstruction pivots on the question of the limits of translation. As has often been noted, the problematic of translation, of the border-crossing both between languages and within each language, is central to Derrida's work.2 In his reflection on translation, Derrida acknowledges his indebtedness to Walter Benjamin's articulation of "the task of the translator," on one hand, and to Heidegger's writings on language on the other. What is beginning to emerge here is a constellation of texts functioning as sites of textual encounters that Derrida orchestrates between Celan and Heidegger, to which Benjamin has also been invited (these are the encounters that will culminate in the coda of Derrida's posthumous exchange with Gadamer). For the purposes of this essay, I will focus on three of these texts, which Derrida wrote between the years 1984 and 1987: "Des Tours de Babel," Shibboleth: For Paul Celan, and Of Spirit.5 In a way, in his respective engagements with Heidegger's texts and with Celan's poems, Derrida takes up the theme of a hoped-for yet unrealized encounter between the thinker and the poet, as it was alluded to in Celan's "Todtnauberg."JThe poem transcribes a fated meeting between the Jewish German poet from Romania and the German philosopher, and their walk in the woods of the Black Forest down the path that will forever remain "half-trodden" (Celan 2001, 13).4 Yet, it is this irrevocably "interrupted conversation" that Derrida will take up and carry toward its unfulfilled promise.

Exploratory searches are where a user has insufficient knowledge to define exact search criteria or does not otherwise know what they are looking for. Reinforcement learning techniques have demonstrated great potential for supporting exploratory search in information retrieval systems as they allow the system to trade-off exploration (presenting the user with alternatives topics) and exploitation (moving toward more specific topics). Users of such systems, however, often feel that the system is not responsive to user needs. This problem is not an inherent feature of such systems, but is caused by the exploration rate parameter being inappropriately tuned for a given system, dataset or user. We present a user study to analyze how different exploration rates affect search performance, user satisfaction, and the number of documents selected. We show that the tradeoff between exploration and exploitation can be modelled as a direct relationship between the exploration rate parameter from the reinforcement learning algorithm and the number of relevant documents returned to the user over the course of a search session. We define the optimal exploration/exploitation trade-off as where this relationship is maximised and show this point to be broadly concordant with user satisfaction and performance.

Current search engines offer limited assistance for exploration and information discovery in complex search tasks. Instead, users are distracted by the need to focus their cognitive efforts on finding navigation cues, rather than selecting relevant information. Interactive intent modeling enhances the human information exploration capacity through computational modeling, visualized for interaction. Interactive intent modeling has been shown to increase task-level information seeking performance by up to 100%. In this demonstration, we showcase SciNet, a system implementing interactive intent modeling on top of a scientific article database of over 60 million documents.

We describe OfficeHours, a recommender system that assists students in finding potential supervisors for their dissertation projects. OfficeHours is an interactive recommender system that combines reinforcement learning techniques with a novel interface that assists the student in formulating their query and allows active engagement in directing their search. Students can directly manipulate document features (keywords) extracted from scientific articles written by faculty members to indicate their interests and reinforcement learning is used to model the student's interests by allowing the system to trade off between exploration and exploitation. The goal of system is to give the student the opportunity to more effectively search for possible project supervisors in a situation where the student may have difficulties formulating their query or when very little information may be available on faculty members' websites about their research interests.

The user's understanding of information needs and the information available in the data collection can evolve during an exploratory search session. Search systems tailored for well-defined narrow search tasks may be suboptimal for exploratory search where the user can sequentially refine the expressions of her information needs and explore alternative search directions. A major challenge for exploratory search systems design is how to support such behavior and expose the user to relevant yet novel information that can be difficult to discover by using conventional query formulation techniques. We introduce IntentStreams, a system for exploratory search that provides interactive query refinement mechanisms and parallel visualization of search streams. The system models each search stream via an intent model allowing rapid user feedback. The user interface allows swift initiation of alternative and parallel search streams by direct manipulation that does not require typing. A study with 13 participants shows that IntentStreams provides better support for branching behavior compared to a conventional search system.

In exploratory search, when a user directs a search engine using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. One problem is that the user is often modelled as a passive source of relevance information, instead of an active entity trying to steer the system based on evolving information needs. This may cause the user to feel that the response of the system is inconsistent with her steering. Another problem arises due to the sheer size and complexity of the information space, and hence of the system, as it may be difficult for the user to anticipate the consequences of her actions in this complex environment. These problems can be mitigated by interpreting the user's actions as setting a goal for an optimization problem regarding the system state, instead of passive relevance feedback, and by allowing the user to see the predicted effects of an action before committing to it. In this paper, we present an implementation of these improvements in a visual user-controllable search interface. A user study involving exploratory search for scientific literature gives some indication on improvements in task performance, usability, perceived usefulness and user acceptance.

We consider the problem of Content-Based Image Retrieval (CBIR) with interactive user feedback when the user is unable to query the system with natural language text. We employ content-based techniques with Relevance Feedback mechanism to capture the precise need of the user and interactively refine the query. We apply the Exploration/Exploitation trade-off with Hierarchical Gaussian Process Bandits and pseudo feedback in order to tackle the problem of optimization in face of uncertainty and to improve the quality of multiple images selection. We tackle the scalability issue with Self-Organizing Map as a preprocessing techniques. A prototype system called ImSe was developed and tested in experiments with real users in different types of search tasks. The experiments show favorable results and indicate the benefits of proposed aprroach.

Supporting exploratory search is a very challenging problem, not least because of the dynamic nature of the exercise: both the knowledge and interests of the user are subject to constant change. Moreover, whether the results for a query are informative is strongly subjective. What is informative to one user, is too specific for the other; specificity differs between users depending on their intent and accumulated knowledge about the domain. We propose a formal model - motivated by Information Foraging Theory - for predicting the subjective specificity of search results based on simple observables such as result-clicks. Through two studies including both controlled and free-form exploratory search we show our model allows us to differentiate between levels of subjective result specificity with regard to the current information need of the user.

We introduce IntentRadar, an interactive search user interface that anticipates user's search intents by estimating them from user interaction. The estimated intents are represented as keywords and visualized on a radial layout that organizes the keywords as directions in the information space. IntentRadar assists users to direct their search by allowing to target relevance feedback on keywords by manipulating the position of the keywords on the radar. The system then learns and visualizes improved estimates of intents and retrieves documents corresponding to the present search intent estimate. IntentRadar has been shown to significantly improve users' task performance and the quality of retrieved information without compromising task execution time.

We have fabricated Transition Edge Sensors (TESs) whose thermal characteristics are completely characterised by few-mode ballistic phonon exchange with the heat bath. These TESs have extremely small amorphous SiNx support legs: 0.2 um thick, 0.7 to 1.0 um wide and 1.0 to 4.0 um long. We show, using classical elastic wave theory, that it is only necessary to know the geometry and bulk elastic constants of the material to calculate the thermal conductance and fluctuation noise. Our devices operate in the few-mode regime, between 5 and 7 modes per leg, and have noise equivalent powers (NEPs) of 1.2 aW Hz-1/2. The NEP is dominated by the thermal fluctuation noise in the legs, which itself is dominated by phonon shot-noise. Thus TESs have been demonstrated whose thermal characteristics are fully accounted for by an elastic noise-wave model. Our current devices, and second-generation devices based on patterned phononic filters, can be used to produce optically compact, mechanically robust, highly sensitive TES imaging arrays, circumventing many of the problems inherent in conventional long-legged designs., 10 pages, 9 figures, 1 table